Back to the main Siena page

SIENA news

Overview of availability of recent versions of packages

The newest "official" version of RSiena (1.4.7) is available from CRAN (February 22, 2024).

The most recent version of RSiena at GitHub is 1.4.23 (May 3, 2025) and can be downloaded from the downloads page and also from GitHub/stocnet.

The most recent version of multiSiena can be downloaded from the downloads page. multiSiena contains function sienaBayes, and is meant to be used alongside RSiena.

May 4, 2025:

• Please take note of the internal effect parameters of effects! Their values are mentioned in the effect names in the output. The details of the meaning of the effect depend on the internal effect parameter. This is described in the manual (Chapter 12, "Formulas for Effects").
  (The majority of effects do not have internal effect parameters, that is why you may have missed their existence in an initial RSiena course.)
• New version 1.4.23 of RSiena and the associated version 1.2.31 of multiSiena available from GitHub/stocnet and the Siena downloads page, respectively.

  The main changes visible to users with respect to the earlier version 1.4.22 are:

  • Effects
    • New effects fromAny, sameInXCycle4.
    • Effect shortName cycle4ND replaced by cycle4.
    • Better treatment of missing covariate values in effect sameXCycle4.
    • New internal effect parameter values 3 and 4 for sameXInPop, diffXInPop, sameXInPopIntn, sameXInActIntn, homXOutAct2.
    • Internal effect parameter values made visible in output for effects from, sameXInPop, diffXInPop, sameXVInPop, and sameXVInPop2.
  • New functionality
    • In print.sienaEffects(..., includeShortNames=TRUE), the effectNumber is also printed.
  • Changes in documentation:
    • Definition of effects outRateLog, inRateLog, and recipRateLog corrected in the manual.
  • Bug corrections
    • Various memory leaks repaired.

April 16, 2025:

• After an intermezzo of a few months, this website was updated again.
• Announcement of Siena-related workshops at the Sunbelt conference (Paris, June 2025): see the Siena courses and activities page.
  There also an on-demand introductory online workshop is announced.
• Two new versions of RSiena were made available, the most recent being version 1.4.22 (February 8, 2025).
  The main changes visible to users with respect to the earlier version 1.4.19 are:
  • Effects:
    • New effects altHigherEgoX, varAlt, and avSimVarAlt.
    • Effect higher also implemented for symmetric networks.
    • Internal parameter 0 (for log(x)) added for outActIntn.
    • outActIntn specified as an ego effect.
  • New functions selectionTable and influenceTable, with print methods.
  • New auxiliary GOF function egoAlterCombi.
  • Parameter showAll added to plot.sienaGOF.
• Appearance (in January 2025) of the paper
  Tanzhe Tang, Tom Snijders and Andreas Flache (2025). An Empirical and Simulation Investigation of Bounded Confidence and Negative Influence in Opinion Dynamics Using Stochastic Actor-Oriented Modelling. Journal of Artificial Societies and Social Simulation 28 (1) 2.
  DOI: https://doi.org/10.18564/jasss.5566
  This paper uses RSiena as an agent-based model and addresses ABM researchers.

September 3, 2024:

• New version 1.4.19 of RSiena and the associated version 1.2.29 of multiSiena available from GitHub/stocnet and the Siena downloads page, respectively.

  The main changes visible to users with respect to the earlier version 1.4.18 are:

  • Effects:
    • New effects outThreshold and outThreshold2.
    • outActIntn specified as an ego effect.
  • New functionality:
    • New network option 11 (Contemporaneous evaluation statistics model) for use of contemporaneous statistics (i.e., for co-evolution models, using both dependent variables in the statistics as simulated at the end of the period) for estimating all evaluation effects of the network variable. See section 5.10 in the manual.
  • Bug correction:
    • Error corrected that occurred for estimation by the GMoM if some effects are fixed.
    • Correction of list of effect names given when thetaBound is exceeded in siena07.

August 1, 2024:

• New version 1.4.18 of RSiena and the associated version 1.2.28 of multiSiena available from GitHub/stocnet and the Siena downloads page, respectively.

  The main changes visible to users with respect to the earlier version 1.4.13 are:

  • Effects:
    • New Generalized Method of Moments ("gmm") statistics for co-evolution of multiple networks: crprod_gmm, to_gmm, from_gmm.
  • Bug correction:
    • Bug corrected in sienaGOF which occurred for models with tested effects if iterations is less than sienaFitObject$n3.
  The new Generalized Method of Moments statistics are contemporaneous statistics, i.e., they use both dependent variables in the statistics as observed or simulated at the end of the period.
  Further see Section 6.10.1 in the manual and Section 4.9 in Siena algorithms.

June 11, 2024:

• New version 1.4.13 of RSiena and the associated version 1.2.26 of multiSiena available from GitHub/stocnet and the Siena downloads page, respectively.

  The main changes visible to users with respect to the earlier version 1.4.12 are:

  • Improvements of functionality:
    • Effectnames as reproduced by sienaFit objects now contain the correct values of internal effect parameters; this was not always so before.
      This is also the case when printing an effects object, except for interaction effects created by includeInteraction for which later the internal effect parameter of some of its main effects was changed by a call of setEffect.
      However, also in such cases the effectnames as reproduced by the sienaFit object will be correct.
    • New parameter thetaBound for siena07, which has the effect of stopping the estimation process if some parameters become too large (which would signal divergence).
    • Display of deviations in the siena07gui (the graphical window that appears during the running of siena07 unless in batch mode) modified so that numbers larger than or equal to 100,000 in absolute value are displayed in exponential format (and use only one line in the gui).
  • Bug correction:
    • Function updateSpecification corrected (it did not work properly for including interactions).
  • Effects:
    • New effect homXOutAct2.

May 2, 2024:

• New version 1.2.25 of multiSiena available from the Siena downloads page.
  (In the meantime, there has been a new version 1.4.12 of RSiena at GitHub/stocnet, but this does not differ from version 1.4.11 as far as will be evident to the user.)

  The main changes visible to users with respect to the earlier version 1.2.24 are:

  • Improvements of functionality:
    • New function sienaBayes.table to make a LaTeX table of sienaBayes results.

April 24, 2024:

• New version 1.4.11 of RSiena and 1.2.24 of multiSiena available from GitHub/stocnet and the Siena downloads page, respectively.

  The main changes visible to users with respect to the earlier version 1.4.10 are:

  • Effects:
    • Effect name outOutDist2AvIntn changed to avAlt.2M.tot .
    • New effects avAlt.2M.tie , avAlt.2M.tot , avAltU.2M.tie , dist2OutInActIntn , nDist2ActIntn , sharedToU .
      These are especially useful for two-mode networks; hence the part .2M. in some of the names.
    • Changed sqrt treatment in outOutDist2ActIntn and outOutDist2AvIntn / avAlt.2M.tot (see the manual).
  • Improvements of functionality:
    • Method print.sienaEffects has an extra parameter includeShortNames to do what the name of this parameter suggests.
      If you like to be reminded of what are the shortNames of each effect in effects object myeff, use
      print(myeff, includeShortNames=TRUE)

March 26, 2024:

• New version 1.4.10 of RSiena and 1.2.23 of multiSiena available from GitHub and the Siena downloads page.

  The main changes visible to users with respect to the earlier version 1.4.8 are:

  • New effects:
    • New effects crossXOutAct, outOutDist2ActIntn, outOutDist2AvIntn, inPopOutW.
    • New effects sameXV and sameXVInPop for bipartite and symmetric networks.
    • sameXCycle4 added for one-mode and symmetric networks.
    • sharedTo gets default internal effect parameter p=3 .
  • Improvements of functionality:
    • In multiSiena: function simulateData, for forward simulations based on sienaBayes results, extended to allow multiple dependent variables.
    • Function updateSpecification now also updates interaction effects and initialValues.
    • New parameter silent in sienaAlgorithmCreate activated.
  • Correction of memory leak that affected ML estimation and sienaBayes.
  • New parameter silent in sienaAlgorithmCreate.

  The new effects outOutDist2ActIntn, outOutDist2AvIntn, inPopOutW, sameXV, and sameXVInPop are of special interest for two-mode (bipartite) networks, and researchers who are studying such networks are invited to take a look in the manual for their description.

  In particular:
  Differences between sameXInPop, sameXVInPop, sameXCycle4:
  All work with an actor variable V, and sameXVInPop works in addition with a nodal variable U.
  Both variables V and U should be nonnegative integer-valued (interpreted as categorical) with a maximum not larger than 20.
  All three effects express (for positive parameters) that actors with the same categories of V make related choices of other nodes.
  For sameXInPop, they tend to choose the same nodes. For sameXVInPop, they tend to choose nodes of the same U category. For sameXCycle4, if they tend to choose some particular node in common, they will also tend to choose some other nodes in common.

February 29, 2024:

• New version 1.4.8 of RSiena and multiSiena available from GitHub and the Siena downloads page.

  The main changes visible to users with respect to the earlier version 1.4.7 are:

  • Correction of memory leak that affected ML estimation and sienaBayes.
  • New parameter silent in sienaAlgorithmCreate.

February 22, 2024:

• New version 1.4.7 of RSiena available from CRAN (back again) as well as GitHub and the Siena downloads page.

  The main changes visible to users with respect to the earlier version 1.4.2 (various of them because of issues raised at groups.io/g/RSiena) are:

  • New parameter targets in siena07, used to supersede the targets calculated from the data (not for use in estimation for regular data sets, see the help file for siena07).
    The use of this parameter for simulations will be demonstrated in a script on the website.
  • Allow getEffects to construct effects of more than one dependent network on continuous behavior dependent variables.
  • Some of the recently added effect groups were missing from effectsDocumentation. This led to an incomplete listing of the effects. They are now included.
  • effectsDocumentation() reports to the console the name of the file that was written.
  • sienaDataCreate stops with an error message if there is a bipartite network before a one-mode network.
  • If the data contains a continuous dependent behavioral variable and the algorithm specifies conditional estimation, the estimation stops with a clear error message.
  • Allow sienaDataCreate to work with a single variable defined as a dependent network given as a list of sparse matrices.
  • Changed the check for constant dyadic covariates for sparse matrices (GitHub issue #88).
  Furthermore:
  • sienaRI temporarily disabled because of a memory leak.
    However, this only means it is not 'exported'; it is still available internally, and you still can use it as RSiena:::sienaRI.
  • The option prML=2 for ML estimation by siena07 temporarily disabled because of a memory leak in the move proposal distribution (sienaAlgorithmCreate).
  We are working on the memory leaks!

February 19, 2024:

• The "official" version of RSiena was archived from CRAN.
  

  This means it is not available any more directly through install.packages(). It is still available from GitHub. The reason for the archiving was a memory leak in sienaRI and in maximum likelihood estimation by siena07 using prML=2 in sienaAlgorithmCreate. This does not affect any other functionality.

  I'm working on this and hope that RSiena will soon be available again from CRAN.

• Repository at GitHub moved from snlab-nl to RSiena at stocnet.
  

  Repository stocnet also contains the R packages goldfish, manynet and MoNAn.

December 14, 2023:

• New version 1.4.2 of RSiena available at GitHub and the Siena downloads page.

  The main changes visible to users with respect to the earlier version 1.4.1 are:

  • Correction of an error in the model for continuous dependent behavior variables.
  • Effects:
    • New effect outMoreX.
    • Effect outMore corrected and triplicated as outMore2 and outMore3, to allow using it with several different internal effect parameters.
    • Interactiontype of altLThresholdX and altRThresholdX is dyadic; of degAbsDiffX, degPosDiffX, and degNegDiffX is "" (blank).
  • New parameter iterations in sienaGOF to allow shorter computations.

November 1, 2023:

• New version 1.4.1 of RSiena available at CRAN, GitHub, and the Siena downloads page.

  The change in version number from 1.4 to 1.3 is because of the cumulative changes since 1.3.0, not because of particularly important differences with version 1.3.28.
  The main change is the improved and extended citation available as citation("RSiena"), recognizing more authors.

October 12, 2023:

• New version 1.3.28 of RSiena available at GitHub and the Siena downloads page.

  The main changes visible to users with respect to the earlier GitHub version 1.3.26 are:

  • Changed effects:
    • threshold , threshold2 , threshold3 , threshold4 changed to work with internal effect parameters referring to the non-centered behavioral variable. Note that this is not backward-compatible; if you use these effects, you have to change the internal effect parameters!
  • Improved documentation:
    • Descriptions of effects altInDist2W and totInDist2W added to the manual (the effects had been there since a long time, but not documented).
• New version of script InfluenceTables.r on the RSiena scripts page.

  This represents also the contributions from the threshold effects mentioned above.

October 3, 2023:

• New version of script InfluenceTables.r on the RSiena scripts page.

  This represents also the contributions from the one-sided similarity influence effects avAttHigher and avAttLower. Furthermore, some other small improvements were made (e.g., the levels of the behavioral variable are taken better from the siena data set).
  A newer version was uploaded on October 4, allowing control over the texts written in the plots.

October 1, 2023:

• New version 1.3.27 of RSiena available at GitHub and the Siena downloads page.

  New version 1.2.19 of multiSiena available from the Siena downloads page.

  The main changes visible to users with respect to the earlier GitHub version 1.3.26 are:

  • Improved functionality:
    • The slowness of siena07 that occurred since version 1.3.18 was ended (thanks to Vieri Pistocchi for noting that this had happened).
  • New effects:
    • Distance-two "similarity" influence effects: avInSimDist2, totInSimDist2.
    • Distance-two categorical "similarity" homophily effects: sameEgoInDist2, sameEgoInDist2.
    • avTAltDist2 and totTAltDist2 also implemented for behavior co-evolving with symmetric networks.
    • Effects for transforming "total" effects for two-network co-evolution to "average" effects: divOutEgoIntn, divInEgoIntn, divOutAltIntn, divInAltIntn. See the manual, search for "divOutEgoIntn".
    • outMore2, doubling outMore so that it can be used with two different internal effect parameters.
  • Improved documentation:
    • Some explanation is given in the manual, Section 5.12.1, about internal effect parameters for interactions created by includeInteraction.

August 21, 2023:

• New version 1.3.26 of RSiena available at GitHub and the Siena downloads page.

  New version 1.2.18 of multiSiena available from the Siena downloads page.

  The main changes visible to users with respect to the earlier GitHub version 1.3.23 are:

  • Improved functionality:
    • Improved Phase 1 derivative matrix computation for model for continuous behavior.
    • Added continuous behavior to returned simulated data (if returnDeps in siena07).
  • Bug corrections:
    • In getEffects, the effects object was given an attribute version, which was not done correctly in version 1.3.23. (This led to always giving a warning if any interaction effects were specified.)
    • Corrections of implementation of operating sienaGOF on a list of sienaFit objects (was not correct in version 1.3.23).
    • Period/groupwise tests in sienaTimeTest corrected for the case of non-saturated sets of dummy variables.
    • plot.sienaTimeTest for pairwise=TRUE changed so that the warning is avoided.
    • sienaGOF corrected so that again it can handle auxiliary functions referring to more than one varName (such as in mixedTriadCensus).
• Various scripts on the Siena scripts page updated to recent RSiena versions
  (in particular to the fact that keyword parameter was dropped for function includeInteraction in version 1.3.20).

June 30, 2023:

• New version 1.3.23 of RSiena available at GitHub and the Siena downloads page.

  New version 1.2.17 of multiSiena available from the Siena downloads page.

  The main changes visible to users with respect to the earlier GitHub version 1.3.22 are:

  • New effects:
    • New effects diffWXClosure, sameWWClosure, diffWWClosure, diffXWClosure, sameXWClosure, unequalX.
    • JoutMix made available for bipartite dependent networks.
    • For continuous behavior variables depending on a bipartite dependent network, the effect group continuousBipartiteObjective was created, with effects outdeg, outdegSqrt, and isolateOut.
    • sameXOutAct and diffXOutAct now have a parameter 2 for the sqrt version.
  • Bug corrections:
    • The error was corrected that occurred if useStdInits = TRUE in sienaAlgorithmCreate and the effects object includes interaction effects.
    • In sienaDataCreate, the warning message that there is at least one "upOnly" period now is made for each dependent variable instead of only the last.
  • Improvements of functionality:
    • The model for continuous behavior variables seems to work now.
      To use it, you must use an effects object created by this (or a future) version!
    • sienaGOF now also accepts a list of sienaFit objects.
    • A warning is given if the used version of the effects object is not current and it contains interaction effects (then it is possible that the interacting effects are chosen incorrectly, even though the effectName of the interaction seems OK).
    • Various coding improvements and better texts of warnings and error messages.
    • In the violin plot for sienaGOF, the default bandwidth selection method is used (the use of "nrd" sometimes led to absent plots because of negative bandwidth).

May 13, 2023:

• New version 1.3.22 of RSiena available at GitHub and the Siena downloads page.

  New version 1.2.16 of multiSiena available from the Siena downloads page.

  The main changes visible to users with respect to the earlier GitHub version 1.3.20 are:

  • Improvements of functionality:
    • Introduction, for research purposes (not regular use), of new "double step" model options for directed one-mode networks. This is obtained through options 7, 8, 9, 10 for modelType set in sienaAlgorithmCreate; see manual Section 5.9.
  • Bug corrections:
    • When dealing with errors reported at the CRAN checks of RSiena version 1.3.14.1 for r-oldrel-macos-x86_64, it was found that there were errors in coding of some "generic effects", which may have led to unpredictable behavior. This was cleaned up and corrected. This led to changes in coding of several distance-2 covariate-related network effects such as altDist2, totDist2 and altInDist2; some of these may have been incorrect in earlier versions.
    • For Maximum Likelihood and (in sienaBayes) Bayesian estimation, the new move step does not work for data sets containing more than one dependent variable. Therefore the new default prML=2 in sienaAlgorithmCreate (which uses move steps with a positive probability) was changed back to prML=1; furthermore, if Maximum Likelihood estimation with prML=2 is attempted for a data set containing more than one dependent variable, the program stops with a decent error message.

April, 2023:

• New version 1.3.20 of RSiena available at GitHub and the Siena downloads page.

  New version 1.2.15 of multiSiena available from the Siena downloads page.

  The main changes visible to users with respect to the earlier GitHub version 1.3.19 are:

  • Improvements of functionality:
    • The keyword parameter in includeInteraction was dropped because it did not have any consequences. The help page for includeInteraction now explains how internal effect parameters for user-defined interactions are determined.
    • Standard deviations added to output of descriptives.sienaGOF.
    • Warning in sienaAlgorithmCreate if maxlike is chosen and MaxDegree is specified. This is now also mentioned in the help page for sienaAlgorithmCreate.
  • Bug corrections:
    • updateSpecification now also updates internal parameter values.
    • In TriadCensus, the empty network will not lead to an error but be reported with the correct triad census.
  • Improvements of documentation:
    • In the help page for sienaDependent, it is mentioned that if there are one-mode as well as two-mode dependent networks, the one-mode networks should come first.

Sometime early 2023:

• The paper giving the explanation of the statistical theory behind the sienaBayes function for multilevel longitudinal network analysis was published:

  Johan Koskinen and Tom A.B. Snijders (2023). Multilevel longitudinal analysis of social networks.
  Journal of the Royal Statistical Society, Series A, 186, 376–400.
  DOI: https://doi.org/10.1093/jrsssa/qnac009

February 7, 2023:

• New 'official' version 1.3.14.1 of RSiena available at CRAN.

  It has the same functionality as version 1.3.14, except that problems were corrected that led to errors on certain platforms (related to the clang 16 C++ compiler).

• New version 1.3.19 of RSiena available at GitHub and the Siena downloads page.

  This integrates version 1.3.18 with the recent changes of CRAN version 1.3.14 to 1.3.14.1 and has some further changes.

  The main changes visible to users with respect to the earlier GitHub version 1.3.18 are:

  • New effect inPop_dya; internal parameter 2 for sameXInPop and diffXInPop.
  • coCovar and varCovar now can handle variables with at least one non-missing value, but will lead to an error (with error message) if all data are missing.

January 30, 2023:

• New version 1.3.18 of RSiena available at GitHub and the Siena downloads page.

  New version 1.2.14 of multiSiena available from the Siena downloads page.

  The main changes visible to users with respect to the earlier GitHub version 1.3.13 are:

  • Effects inPopIntnX, inActIntnX, outPopIntnX, outActIntnX, sameXInPopIntn, sameXOutPopIntn, sameXInActIntn, sameXOutActIntn restored (these had got lost in some way...).
  • sienaDataCreate now also accepts, as "...", a list of such objects.
  • sienaGOF now accepts simulated auxiliary statistics containing missing values. If there are any, this will be reported with a warning if giveNAWarning is TRUE (the default).
  • There is an additional step type move for MH proposal distribution for likelihood estimation (thanks to the DPhil thesis of Charlotte Greenan). This may lead to a gain in computation speed.
  • Accordingly, some other parameters are used in sienaAlgorithmCreate for determining probabilities of MH step types, now summarized in prML; with a new default. The old default for ML estimation still is available as prML=1, but this is not the default any more.
  • List element ac3 (auto-correlations of statistics during Phase 3) added to sienaFit object produced by siena07 for ML estimation.

November 27, 2022:

• There was an error in function siena08 of RSiena:
  an incorrect value was given for the p-value of test statistic T² for the null hypothesis that all parameters are 0. This error is corrected in version 1.3.15.
• New version 1.3.15 of RSiena available at CRAN and GitHub.

  New version 1.2.13 of multiSiena available from the Siena downloads page.

November 11, 2022:

• New version 1.3.14 of RSiena available at CRAN and GitHub.

  New version 1.2.12 of multiSiena available from the Siena downloads page.

  This version of RSiena is available for more computer platforms than version 1.3.13, but does not differ in other respects.
  The new version of multiSiena is brought in line with RSiena 1.3.14.

October 11, 2022:

• New version 1.3.13 of RSiena available at CRAN and GitHub.

  The main changes visible to users with respect to the earlier GitHub version 1.3.11 are:

  • New effects:
    • New effects outAct_ego, inAct_ego, reciAct_ego, toAny.
    • gwdspFB effect added for two-mode networks.
    • For effects to, toBack, toRecip, mixedInXW, internal effect parameter 3 now specifies truncation of the number of twosteps (change to MixedTwoStepFunction).
    • Several new effects related to primary setting: nonPCompress, primCompress, primary, primDegAct, primDegActDiff, primDegActDiffSqrt, primDegActSqrt, primDegActLog, primDegActInv. These are not yet documented in the manual.
  • Improvements of functionality:
    • If returnThetas==TRUE in the call of siena07, also simulated estimation statistics during Phase 2 (deviations from targets) are returned.
    • sigmas and meansigmas added to sienaRI object. See the help page for this function.
    • Print of standard deviations in the sienaRI object for printSigma=TRUE changed to using averages at the variance level.
  • Bug corrections:
    • universalOffset initialized as 0; it was earlier initialized as the maximum real number.
  • Corrections of documentation:
    • Modified help page for sienaRI.
    • Small modifications of help page for sienaGOF.

June 1, 2022:

• New version 1.3.11 of RSiena available at GitHub and 1.2.11 of multiSiena available from the Siena downloads page.

  The main changes are:

  • Correction of error that led to warning in R 4.2.0 for multivariate networks (but had no consequences for results).
  • Correction of help page for sienaGOF (with respect to groupName).
  • Correction of igraphNetworkExtraction in the help page for sienaGOF-auxiliary.
  • Further explanation of mixedTriadCensus in the help page for sienaGOF-auxiliary.

May 11, 2022:

• There was an error in effect sharedTo, which is corrected in RSiena version 1.3.10.
• New version 1.3.10 of RSiena available at GitHub and 1.2.10 of multiSiena available from the Siena downloads page.

  The main changes are:

  • Corrected implementation of sharedTo.
  • New effects avInAltW, avWInAlt, totInAltW, totWInAlt (with help from Robert Krause).
  • Bug corrected that occurred when several two-mode networks were included in the dependent variables, with an order restriction between them.
  • multiSiena brought in line with RSiena version 1.3.10.

March 22, 2022:

• Replacement of RSienaTest by multiSiena.

  As a step toward integration of sienaBayes in RSiena, the package RSienaTest, of which the main remaining purpose was that it contains sienaBayes, is now replaced by multiSiena.

  multiSiena is a multilevel extension of the RSiena package for simulation-based estimation of stochastic actor-oriented models for longitudinal network data collected as panel data. The extension consists of the function sienaBayes and associated functions. The purpose is to fit hierarchical Bayesian models random effects to sienaGroup data objects. This package is meant to be used alongside RSiena, and is available from the downloads page.

March 18, 2022:

• New version 1.3.9 of RSiena available at GitHub.

  The main changes are:

  • Correction of effects simAllNear and simAllFar.
  • Minor corrections of functions sienaTimeTest and summary.sienaGOF.

March 14, 2022:

• The paper to avoid confusion about the SAOM-TERGM comparison now is published:

  Per Block, James Hollway, Christoph Stadtfeld, Johan Koskinen and Tom Snijders (2022). Circular specifications and "predicting" with information from the future: Errors in the empirical SAOM-TERGM comparison of Leifeld & Cranmer.
  Network Science, First View, 1-22.
  DOI: https://doi.org/10.1017/nws.2022.6

  Abstract

  We review the empirical comparison of Stochastic Actor-oriented Models (SAOMs) and Temporal Exponential Random Graph Models (TERGMs) by Leifeld & Cranmer in Network Science (2019). When specifying their TERGM, they use exogenous nodal attributes calculated from the outcome networks' observed degrees instead of endogenous ERGM equivalents of structural effects as used in the SAOM. This turns the modeled endogeneity into circularity and obtained results are tautological. In consequence, their out-of-sample predictions using TERGMs are based on out-of-sample information and thereby predict the future using observations from the future. Thus, their analysis rests on erroneous model specifications that invalidate the article's conclusions. Finally, beyond these specific points, we argue that their evaluation metric - tie-level predictive accuracy - is unsuited for the task of comparing model performance.

  
  

March 8, 2022:

• A belated 'news' item:

  The statistical theory behind the sienaBayes function is now available in a paper at the ArXiV:

  Johan Koskinen and Tom A.B. Snijders (2022). Multilevel longitudinal analysis of social networks. arXiv preprint arXiv:2201.12713.

• New version 1.3.8 of RSiena available at GitHub.

  The main changes are:

  • Function sienaRI was made available also for two-mode networks (provided the number of second-mode nodes is smaller than the number of actors = first-mode nodes), and corrected for behavioral dependent variables.
  • New effect avDeg (see the manual).
  • Changed default internal effect parameters for simAllNear and simAllFar.
  • Warnings avoided in sienaTimeTest with includeTimeDummy.
  • Small improvements of help pages for sienaGroupCreate and sienaGOF.
• New script MoranDecompositionExample.R at the Siena scripts page.
  This is to illustrate how to obtain the numerical description of the distinction between selection and influence by a decomposition of the Moran (or other) statistic according to Steglich, Snijders & Pearson (2010, Sociological Methodology).
• Updated script RscriptMultipleGroups.R at the Siena scripts page.
  Now also including a section on the use of sienaGOF for multi-group data sets created by sienaGroupCreate.
• Updated script RscriptMultipleGroups_meta.R at the Siena scripts page.

February 17, 2022:

• New version 1.3.6 of RSiena available at GitHub.

  The main changes are:

  • New effects recipRateInv, recipRateLog (thanks to Steffen Triebel).
  • New effects absOutDiffIntn, avDegIntn, simAllNear, simAllFar.
  • Default internal effect parameter for outOutActIntn, outOutAvIntn, and both changed from 2 to 1.
  • Improvements of functionality:
    • Function includeInteraction now also can modify the initialValue of an effect; and the order of parameters for this function was changed, bringing it in line with the order in setEffect.

December 15, 2021:

• New version 1.3.5 of RSiena available at GitHub.

  There was a bug in version 1.3.4, which would turn up when models with interaction effects were estimated. This was corrected in version 1.3.5.

December 8, 2021:

• New version 1.3.4 of RSiena available at GitHub.

  The main changes are:

  • New effects inRateInv, inRateLog (thanks to Steffen Triebel).
  • Improvements of functionality:
    • When an effects object with interaction effects is printed, the names of the interacting effects are mentioned, and prefixes int. and i3. were dropped.
    • The check of whether an interaction effect is allowed now is done immediately when creating the interaction effect instead of waiting for its use in siena07.
    • For function sienaGroupCreate some changes were made: if it is applied to a list of length 1, attributes of the single group are not recomputed; if it is applied to a list of length larger than 1, the attributes range and range2 of behavioral variables of individual groups are computed as the range of the unions of the ranges of all the groups. The same is done for covariates.
    • print.sienaGroup slightly extended.
    • Creation of covariates gives a warning (optional) if all values are missing, and also if all non-missing values are the same.
  • Bug corrections:
    • Some small bugs for later use in sienaBayes were corrected.

October 11, 2021:

• New version 1.3.3 of RSiena available at GitHub.

  The main changes are:

  • New effects:
    internal effect parameter added to diffusion rate effects (avExposure etc.), to allow testing the hypothesis (Damon Centola) that social influence occurs when at least p network members already adopted the innovation (e.g., p=2);
    outOutAvIntn, as a two-mode analogue of avAlt;
    outOutActIntn made available for more network types.
  • Improvements of functionality:
    • Trial values of theta used during Phase 2 of siena07 added to the object ans produced by sienaFit as ans$thetas. The use of this option is demonstrated in script script_traceRM.R.
    • toggleProbabilities added to output of sienaRI .
  • Bug corrections:
    • The bug noted on October 6 was corrected in the following way (by changing sienaDataCreate, not sienaGroupCreate). If a data set contains a constant covariate, the simX effect will not run into an error any more. This is relevant mainly for sienaGroup data sets, where covariates might be constant for some of the groups.

October 6, 2021:

• Bug found in sienaGroupCreate: if there are any covariates that are constant in some group, the simX effect will lead to an error with error message "total probability non-positive". This may be a nuisance especially when using sienaBayes.

  This will be corrected in the new version of RSiena and RSienaTest.

July 29, 2021:

• New version 1.3.2 of RSiena available at GitHub.

  The main changes are:

  • New effects: crprodInActIntn (thanks to Nynke Niezink), XXW.
  • Improvements of functionality:
    • updateTheta also accepts sienaBayesFit objects as prevAns.
    • Effects of type creation or endow represented in the output of siena.table by creation and maintenance, respectively.
  • Bug corrections:
    • If upOnly or downOnly, the (out)degree (density) effect is also excluded for symmetric networks (this was reported by print01Report, but not carried out).
    • Message corrected in sienaDataCreate if there is an attribute higher.

July 13, 2021:

• Version 1.2-30 of RSienaTest available at R-Forge.
  

  This is again a very minor update of sienaBayes. The only change is:

  • At the end of initializeBayes, there was a mistaken adaptation of parameters nwarm and nmain. This adaptation was dropped.

May 10, 2021:

• New version 1.3.0 of RSiena available from CRAN; and from GitHub, of course.

The cumulative importance of all changes in the past two years (including Generalized Method of Moments and continuous behavioral variables) justifies that the version number now proceeds from 1.2.x to 1.3.0.

For changes with respect to earlier versions, see below, or look at the NEWS.md file in the source code on GitHub, and appendix B in the Siena manual.

May 1, 2021:

• Version 1.2.34 of RSiena available at GitHub.

  Main changes :

  • New function testSame.RSiena to test equality of parameters.
  • New effects: avInSim (thanks to Steffen Triebel), totInSim, avInSimPopAlt, totInSimPopAlt, constant, avAttHigher, avAttLower, totAttHigher, totAttLower. See the manual!
  • Changed effects: endowment and creation types for avInSim (brought in line with these types for avSim).
  • Improvements of functionality:
    • funnelPlot adapted to lists of sienaFit objects containing some missing estimates or standard errors.
    • plot.sienaGOF: new parameter position for the position of the observation with regard to the red dot.
  • Bug corrections:
    • Restore backward compatibility with respect to checks of x$gmm.
    • Correct names reported for tested effects (avoid wrong names being given if there are interactions without main effects).

April 15, 2021:

• Version 1.2-29 of RSienaTest available at R-Forge.
  

  This is a very minor update of sienaBayes. The main changes are:

  • The pre-warming phase is interrupted (and followed by the warming phase) if no proposals were accepted during two batches of iterations.
  • ThinBayesAcceptances is mentioned in the help page as part of the object produced.

April 6, 2021:

• Structural zeros have different consequences for MoM and ML / Bayes.

  For the behavior of structural zeros there is a difference between the MoM and ML / sienaBayes.

  Structural zeros (indicated in the data by the code 10) can be used to indicate turnover, i.e., actors entering or leaving the network. However, for ML estimation and sienaBayes this works differently than for the Method of Moments.

  Summary: for estimation by ML or sienaBayes, avoid using structural zeros at the end of a period for tie variables that have a regular value (0 or 1) at the beginning of the period.

  Simulations for estimation by the Method of Moments (MoM) take account only of the observations at the start of the wave, in a forward simulation; likelihood simulations, which are used for ML estimation and sienaBayes, connect the observations at the start of the wave with the observation at the end of the wave ('bridge simulations'). Likelihood simulations simulate the chain of changes that connects the observation at the start of a period to the observation at the end of this period.
  This implies that, for estimation by ML or sienaBayes, if a tie variable starts with a regular value (0 or 1) at a given wave, and the observation at the next wave is a structural zero (code 10), this variable has to assume the value 0 at the end of the period. However, if the structural zero is meant to indicate that the actor has left by this wave, presumably the tie did not become, or stay, absent by endogenous reasons. Very probably as a researcher you do not know the tie value at the end of this period; you then should indicate the tie variable as missing (code NA).
  You can still use structural zeros as usual for MoM estimation; also, structural zeros at the start of a wave can still be used to indicate that the actor did not take part in the network during this period (or, if it is for one tie variable only, that the tie is absent during the entire period).

  What to do to represent change of composition for estimation by ML or sienaBayes, when working with three or more waves? The method of joiners and leavers (using sienaCompositionChange) is available only for MoM estimation, and the remarks above imply that structural zeros at the end of a period, when accompanied at the beginning of this period for the same (ego, alter) pair by a regular value (0 or 1), are probably better to avoid for ML and sienaBayes, and replaced by a missing value indicator (NA). If estimation is by ML using siena07, the fact that in the next period this tie variable is structurally zero can then be indicated by making the data structure into a multi-group data set (sienaGroupCreate). Thus, when for 3 waves / 2 periods and MoM estimation you would use a sequence r-10-10 (where r is 0 or 1) for a given pair (i, j), for ML this would be transformed to the sequence r-NA 10-10. For estimation by sienaBayes, there is no option. Probably it is best, if you have data for sienaBayes where this problem occurs - and perhaps also for other data sets - to analyze just one period (two waves).

March 22, 2021:

• Version 1.2.33 of RSiena available at GitHub.

  Main changes :

  • The only change is the new configure.ac file (thanks to Alvaro Uzaheta). This gives wider possibilities for installation at a variety of platforms, and will help for a future submission to CRAN.
• Version 1.2.28 of RSienaTest available at R-Forge.
  

  Main changes :

  • The error in glueBayes, mentioned below (March 5), was corrected.

March 17, 2021:

• Version 1.2.32 of RSiena available at GitHub.

  Main changes :

  Generalized Method of Moments

  The Generalized Method of Moments now is implemented (thanks to Viviana Amati and Felix Schönenberger).
  The method is described in

  • Amati, Viviana, Schönenberger, Felix, and Snijders, Tom A.B. (2015). Estimation of stochastic actor-oriented models for the evolution of networks by generalized method of moments.
    Journal de la Société Française de Statistique, 156, 140-165.
  • Amati, Viviana, Schönenberger, Felix, and Snijders, Tom A.B. (2019). Contemporaneous Statistics for Estimation in Stochastic Actor-Oriented Co-Evolution Models .
    Psychometrika, 84, 1068-1096.
    DOI: https://doi.org/10.1007/s11336-019-09676-3
A script demonstrating its use is GMoMscript.R.

The software implementation is documented in GitHub at Changes_RSiena_GMoM.pdf.

Effects:

1. New effects: homXTransRecTrip, toU.
2. sqrt versions for parameter 2 for the effects to, toBack, toRecip, from, fromMutual.
3. Effects to, toU, toBack, toRecip, MixedInXW are dyadic.
4. Reinstated effect MixedInXW, also with sqrt version for parameter 2.
5. Dropped effect to.2 (identical to "to") and MixedInWX (identical to "toBack").

Further improvements of functionality:

1. dyadicCov made to accept also changing dyadic covariates.
2. new arguments plotAboveThreshold and verbose for funnelPlot.
3. effectsDocumentation now also includes gmm effects (at the bottom).
4. Improved representation of some symbols in effect names in LaTeX option of meta.table and siena.table.
5. Display of deviations from targets changed to after subtraction of targets.
6. Stop if no parameters are estimated and simOnly is FALSE.

Documentation:

1. Extended description of GMoM in the manual.
2. Description of toBack and toRecip in manual.

Reduction of functionality:

1. Vignette basicRSiena.Rmd dropped from the package (available at scripts page of website: see below).

Corrections / safeguards:

1. Correction of a bug that sometimes led to an error message if simOnly.
• New script GMoMscript.R, demonstrating how to use the Generalized Method of Moments (GMoM) on the Siena scripts page.
• New vignette basic_RSiena.html available at Siena scripts page.

March 5, 2021:

Error notification.
• The labeling of the matrices produced by extract.posteriorMeans(z), for z constructed by glueBayes in RSienaTest, is incorrect.
  This was corrected in version 1.2.28 of RSienaTest.
  By the way, you can figure out the correct labels yourself: the posterior means are produced for the parameters that are specified as random effects, and the order is the same as it is in the print of z itself.
  Also, (presumably) they are correct for objects z directly created by sienaBayes without subsequent intervention of glueBayes.

February 24, 2021:

• Version 1.2.30 of RSiena available at GitHub.

  Main changes :
  

  1. Resolved issue with continuous dependent behavior variables (Nynke Niezink).
• New scripts SE_checks.R and RscriptStandardErrors.R on the Siena scripts page.

December 10, 2020:

• Version 1.2-29 of RSiena available at GitHub.

  This completes the transition of the RSiena package to GitHub. The repository at R-Forge now is relevant only for RSienaTest. Version 1.2-29 is a follow-up both of version 1.2-27 (R-Forge) and 1.2-28 (GitHub).

  Main changes from versions 1.2-27 and 1.2-28:
  

  1. New effects (due to Christoph Stadtfeld): transtrip.FR, transtrip.FE, transtrip.EE, WWX.EE, WWX.FR, WXX.FE, WXX.ER, XWX.ER, XWX.FE, to.2, toBack, toRecip.
  2. New functions meta.table (for results of siena08 ) and funnelPlot (for lists of 'parallel' sienaFit objects).
  3. New parameter tested in sienaGOF (to potentially exclude some effects with score-type tests from the approximations for improvement of fit).
  4. Manual and its LaTeX code added to GitHub, see \docs\manual.

November 10, 2020:

• Inauguration of the RSiena Users' group at groups.io, https://groups.io/g/RSiena.

  This replaces the earlier Yahoo group, which was in place since 2005.

October 1, 2020:

• Version 1.2-28 of RSiena available at GitHub.

  Main changes:
  

  1. Constraint that two networks are disjoint operates correctly also when one of the networks is symmetric and the other is not.
  2. Constraint that one network is at least as high as another network operates correctly also when the higher networks is symmetric and the other is not.
  3. Manual and its LaTeX code added to GitHub, see \docs\manual.

September 17, 2020:

• Inauguration of the RSiena project at GitHub.

  This was carried out in collaboration with James Hollway. The release at GitHub contains version 1.2-26, and transition to version 1.2-27 (see below) will take place soon.

  At GitHub, there is a wiki which is still in a phase of development.

September 16, 2020:

• New version 1.2-27 of RSiena and RSienaTest available from the downloads page and also from the RSiena project at R-Forge.

Main changes in RSiena and RSienaTest visible to users are the following.

• tcltk no longer a requirement; if not available, batch mode is followed for siena07 (with help by James Hollway).
• New effect transTripX.
• For effect from.w.ind, option parameter=-1 added (see the manual).
• The to effect is an ego effect (permitting interactions).
• For siena.table, some of the effectNames changed to nice strings, so that LaTeX can run without errors on the table produced if type='tex'.
• The sienaMeta object produced by siena08 now has IWLS estimates more easily accessible, as object\$muhat and object\$se.muhat.
• Some improved error messages.
• sienaAlgorithmCreate: requirements for mult corrected in help page.

• Vignette basicRSiena added (was earlier available as a script); thanks to James Hollway.

• Improved initialisation in sienaBayes.
• Allow parameter targetMHProb of sienaBayes to have length 2, applying separately to the random parameters and the fixed parameters.
• Another attempt to discard the unnecessary objects zn and zsmall remaining after operation of sienaBayes.

April 8, 2020:

• New version 1.2-25 of RSiena and RSienaTest available from the downloads page and the RSiena project at R-Forge.

Main changes in RSiena and RSienaTest visible to users are the following.

• Auxiliary function dyadicCov for checking fit of dyadic variables using sienaGOF changed a bit; see the help page. The help page also demonstrates how this can be used for checking fit for monadic variables (ego-alter combinations).
• descriptives.sienaGOF now also shows the exceedance probabilities of the observed statistics.
• Correction of sienaDataCreate for actor covariates (gave a warning).

February 16, 2020:

• New version 1.2-24 of RSiena and RSienaTest available from the downloads page and the RSiena project at R-Forge.

Main changes in RSiena and RSienaTest visible to users are the following.

• New auxiliary function dyadicCov for sienaGOF.
• Correction of error in modelType when one-mode as well as two-mode networks are used.
• Added startingDate in siena08 and sienaBayes to the object produced.

January 12, 2020:

• New version 1.2-23 of RSiena available from CRAN.

Some changes in RSiena:

• Changed extension names of output files from .out to .txt.
• New option projname=NULL in sienaAlgorithmCreate. This option is used for all examples in the help pages of RSiena where siena07 is called, to write files for examples only to the temporary directory.

January 6, 2020:

• New version 1.2-22 of RSiena and RSienaTest available from the downloads page and the RSiena project at R-Forge.

Main changes in RSiena and RSienaTest visible to users are the following.

• New effects avGroupEgoX, outIso, outMore (outIso does perhaps not yet work properly).
• Tested effects reported in print of Multipar.RSiena and score.Test.
• Reordered effects object so that reciAct comes after instead of before inAct and inAct.c.
• Component startingDate added to sienaFit object; this is the starting date of estimation, and reported in siena.table(..., type='tex', ...).
• Object names are given in sienaFit.print if simOnly.
• Calculation of IndegreeDistribution and OutdegreeDistribution for sienaGOF, if there are no missings or structurals, now is faster.
• regrCoef and regrCor added to the sienaFit object also when dolby is not used.
• Immediate stop if useCluster and returnChains both are used (in this case, no chains would be returned anyway).
• sienaDataCreate: more informative message in case of constraints.
• Small improvements in many help pages.

• In sienaBayes: more precise check that prevAns has same specification as effects; priorSigEta added to sienaBayesFit object; dimnames(ThinParameters)[[3]] defined as effect names; ridge for prior variance of rate parameters if priorRatesFromData=1 or 2 decreased from 0.5 to 0.01; zm and zsmall removed at the end.
• plotPostMeansMDS: a title is shown, and coordinates show the dimensions of the MDS solution; the value returned contains not only the plot coordinates, but also the similarities (correlations) between the groups; new parameters pmonly and excludeRates.
• New parameter excludeRates also in extract.posteriorMeans.
• glueBayes: added z\$varyingInEstimated, z\$varyingNonRateInEstimated, z\$ratesInVarying to the object produced (else extract.sienaBayes did not work properly); if one of the sampling parameters is different, a warning is given instead of a stop.
• Corrected error in names of array returned by extract.posteriorMeans.

November 11, 2019: Publication of a paper by Josh Lospinoso and Tom Snijders about the basis of the sienaGOF function, including a data-driven way for selecting effects that may improve fit when it is poor.

Josh A. Lospinoso and Tom A. B. Snijders (2019). Goodness of fit for Stochastic Actor-Oriented Models. Methodological Innovations, 12, 2059799119884282.
DOI: https://doi.org/10.1177/2059799119884282.

Abstract

We propose a Mahalanobis distance-based Monte Carlo goodness of fit testing procedure for the family of stochastic actor-oriented models for social network evolution. A modified model distance estimator is proposed to help the researcher identify model extensions that will remediate poor fit.

The content of this paper was present already in Josh's DPhil dissertation of 2012, but was not yet published in a scientific journal.

November 5, 2019. A paper to avoid confusion about the SAOM-TERGM comparison.

Per Block, James Hollway, Christoph Stadtfeld, Johan Koskinen and Tom Snijders (2019). "Predicting" after peeking into the future: Correcting a fundamental flaw in the SAOM - TERGM comparison of Leifeld and Cranmer (2019). ArXiV, http://arxiv.org/abs/1911.01385.

Abstract

We review the empirical comparison of SAOMs and TERGMs by Leifeld & Cranmer (2019) in Network Science. We note that their model specification uses nodal covariates calculated from observed degrees instead of using structural effects, thus turning endogeneity into circularity. In consequence, their out-of-sample predictions using TERGMs are based on out-of-sample information and thereby predict the future using observations from the future. We conclude that their analysis rest on erroneous model specifications that render the article's conclusions meaningless. Consequently, researchers should disregard recommendations from the criticized paper when making informed modelling choices.

May 21, 2019:

• New version 1.2-17 of RSiena and RSienaTest available from the downloads page and the RSiena project at R-Forge.

Main changes in RSiena and RSienaTest visible to users are the following.

• New effects outAct.c, inAct.c, outPop.c, inPop.c, degPlus.c. These are centered versions, which might have better convergence properties than their namesakes without the .c.
• Effects antiInIso and antiInIso2 implemented also for non-directed networks.
• Effects outTrunc, outTrunc2, outSqInv, isolateNet, and degPlus got endowment effects.
• For inPopIntn, outPopIntn, inActIntn, and outActIntn, added 'centered' to the statistic name.
• plot.sienaGOF has a new parameter fontsize; the dots ... will also accept parameters cex, cex.main, cex.lab, cex.axis, as used more generally for plotting in R.
• In case of simulation as indicated by x$simOnly=TRUE, the object produced by siena07 now also contains the standard errors of the mean, called estMeans.sem (see the help page for siena07).
• On various places diagnostic output messages were relabeled to 'message' or 'warning', which implies that they can be turned off by commands such as suppressMessages or suppressWarnings.

• For sienaBayes:
  • prevAns now is allowed to have a different specification of random effects than the parameter given as effects;
  • it is not allowed to use prevAns and prevBayes objects simultaneously;
  • some further small algorithm changes (see CHANGELOG).

May 10, 2019: Publication of a paper by Nynke Niezink and co-authors explaining the extension of the Stochastic Actor-oriented Model to continuous behavior variables.

Nynke M. D. Niezink, Tom A. B. Snijders, and Marijtje van Duijn (2019). No longer discrete: Modeling the dynamics of social networks and continuous behavior.
Sociological Methodology, in press.
DOI: http://dx.doi.org/10.1177/0081175019842263

Abstract

The dynamics of individual behavior are related to the dynamics of the social structures in which individuals are embedded. This implies that in order to study social mechanisms such as social selection or peer influence, we need to model the evolution of social networks and the attributes of network actors as interdependent processes. The stochastic actor-oriented model is a statistical approach to study network-attribute coevolution based on longitudinal data. In its standard specification, the coevolving actor attributes are assumed to be measured on an ordinal categorical scale. Continuous variables first need to be discretized to fit into such a modeling framework. This article presents an extension of the stochastic actor-oriented model that does away with this restriction by using a stochastic differential equation to model the evolution of a continuous attribute. We propose a measure for explained variance and give an interpretation of parameter sizes. The proposed method is illustrated by a study of the relationship between friendship, alcohol consumption, and self-esteem among adolescents.

The corresponding extension of RSiena will be made available later this year.

April 25 21, 2019: Publication of the paper about the four/five-parameter model for effects of actor covariates on networks, which earlier was at the ArXiV.

February 26, 2019:

• New version 1.2-16 of RSiena and RSienaTest available from the downloads page and the RSiena project at R-Forge.

Main changes in RSiena and RSienaTest visible to users are the following.

• New effects maxAAlt, minXAlt (thanks to collaboration with Per Block, Marion Hoffman, Isabel Raabe, and Kieran Mepham) and outIsolate.
• Effect name of isolate effect (not its shortName) changed to in-isolate; this is more clear.
• New parameter thetaValues in siena07, allowing simulations in Phase 3 with varying parameters according to matrix input.
• Wald.RSiena, Multipar.RSiena and score.Test also produce one-sided test statistic if df=1.
• objects produced by Wald.RSiena, Multipar.RSiena and score.Test now have class sienaTest. This class also has a print method.
• sienaRI: earlier check for bipartite dependent variables; if so, stop.
• Argument verbose added to includeEffects and setEffect. (It existed already for includeInteraction.)

January 28, 2019:

There now is a script at the Siena scripts page showing how to implement multiple imputation of missing data for network panel data sets, as proposed in the paper
Robert W. Krause, Mark Huisman, and Tom A.B. Snijders (2018), Multiple imputation for longitudinal network data. Italian Journal of Applied Statistics, 30, 33-57.
http://sa-ijas.stat.unipd.it/sites/sa-ijas.stat.unipd.it/files/10.26398-IJAS.0030-002.pdf

For analyses using RSiena with an amount of missing data that is more than very small, this is a definite improvement over the default method used in RSiena!

December 27, 2018:

• A paper about co-evolution of two networks: friendship and studying together, and their effects on achievement, combining a variety of statistical methods and with a beautiful visualization:

  Christoph Stadtfeld, András Vörös, Timon Elmer, Zsófia Boda, and Isabel J. Raabe (2018). Integration in emerging social networks explains academic failure and success. Proceedings of the National Academy of Sciences, in press.
  DOI: https://doi.org/10.1073/pnas.1811388115

  Abstract (partial).
  Academic success of students has been explained with a variety of individual and socioeconomic factors. Social networks that informally emerge within student communities can have an additional effect on their achievement. We investigate how social networks emerge between previously unacquainted students and how integration in these networks explains academic success. Our study measures multiple important dimensions of social ties between students: their positive interactions, friendships, and studying relations. By using statistical models for dynamic network data, we are able to investigate the processes of social network formation in the cohort. We find that friendship ties informally evolve into studying relationships over the academic year. Studying together with others, in turn, has a strong impact on students' success at the examination.

December 12, 2018:

• A paper for visualizing the ministeps in the Stochastic Actor-oriented Model:

  jimi Adams and David Schaefer (2018). Visualizing Stochastic Actor-based Model Microsteps. Socius, "Sociological Research for a Dynamic World", Sage Publications.
  DOI: https://doi.org/10.1177/2378023118816545
  This visualization provides a dynamic representation of the microsteps involved in modeling network and behavior change with a stochastic actor-based model.
  See the script for executing this at the Siena scripts page.

December 6, 2018:

• New version 1.2-14 of RSiena and RSienaTest available from the downloads page and the RSiena project at R-Forge.

Main changes in RSiena and RSienaTest visible to users are the following.

• In the help page of sienaAlgorithmCreate, and in the manual, the possibility is mentioned that the multiplication factor (parameter mult) can be specified as a vector. This allows the possibility for ML estimation to specify the multiplication factor separately for periods × waves.

October 30, 2018:

• Error in sienaGroupCreate, now corrected.
• New version 1.2-13 of RSiena and RSienaTest available from the downloads page and the RSiena project at R-Forge.

There was an error in sienaGroupCreate for non-centered actor covariates. This now is corrected in version 1.2-13.

Main changes in RSiena and RSienaTest visible to users are the following.

• Correct error in sienaGroupCreate for non-centered actor covariates.
• Correct error in print01Report that occurred for changing dyadic covariates given as lists of sparse matrices.
• Also get simulated dependent behavior variables for siena07(..., returnDeps=TRUE, ...) for ML estimation (see help page).
• siena.table corrected for data sets with several dependent variables.
• For siena08: new parameters which and useBound in plot.sienaMeta; new parameter reportEstimates, allowing to reduce the output produced.
• updateSpecification: also update randomEffects column.
• More explanation of sparse matrix input in the help page for sienaDependent.

September, 2018: review of studies using actor-oriented models for studying peer influence on anti-social behaviors.

Already on the web in 2016: practical recommendations for data collection for longitudinal network studies in school settings.

May 31, 2018: meta-analysis of results of actor-oriented models.

Owen Gallupe,John McLevey, and Sarah Brown (2018). Selection and Influence: A Meta-Analysis of the Association Between Peer and Personal Offending.
Journal of Quantitative Criminology, in press.
DOI: https://doi.org/10.1007/s10940-018-9384-y.

Abstract
Whether people are affected by the criminal behavior of peers (the "influence" perspective) or simply prefer to associate with others who are similar in their offending (the "selection" perspective) is a long-standing criminological debate. The relatively recent development of stochastic actor-oriented models (SAOMs-also called SIENA models) for longitudinal social network data has allowed for the examination of selection and influence effects in more comprehensive ways than was previously possible. This article reports the results of a systematic review and meta-analysis of studies that use SAOMs to test for peer selection and influence effects.

May 13, 2018: the new official version 1.2-12 of RSiena is available from CRAN.

Except for a detail on a help page, it is identical to version 1.2-11.
Thanks to efforts by Felix Schönenberger, it is now available on all R platforms, including Solaris.

May 6, 2018: new version 1.2-11 of RSiena and RSienaTest available from the downloads page and the RSiena project at R-Forge.

Main changes in RSiena and RSienaTest visible to users are the following. For the definition of the effects, consult the manual.

• New effects gwdspFF and gwdspFB.
• Effects simEgoInDist2 and simEgoInDist2W for two-mode networks were corrected. Perhaps simEgoDist2 and simEgoDist2W were broken in a previous version; if so, that was now corrected.
• Added sqrt version for reciAct, obtained for parameter = 2.
• Allowed the value parameter=NULL for setEffect and includeInteraction, meaning that no change is made for the internal effect parameter. For setEffect this is the new default, implying that when starting the default values from allEffects.csv are used, just like in includeEffects (where no parameter can be given).
• New auxiliary functions for sienaGOF: Triad Census and mixedTriadCensus (contribution by Christoph Stadtfeld).
• triad census from igraph added to help page of sienaGOF-auxiliary.
• improved sienatable output for type="html": added rules=none, frame = void to general options of the html output file; changed - to –; added column for asterisks to have better alignment for estimates.
• Stop cluster also for a user interrupt in siena07.
• Extension of help page for siena07 by mentioning functions for accessing simulated networks for ML.
• In RSienaTest:
  extract.sienaBayes: corrected error that occurred if called with extracted="all" but there are no varying, or no non-varying parameters.

May 5, 2018: paper 'Statistical Power in Longitudinal Network Studies' by Christoph Stadtfeld, Tom A. B. Snijders, Christian Steglich, and Marijtje van Duijn. Sociological Methods and Research, 2018.
DOI: https://doi.org/10.1177/0049124118769113.

Abstract Longitudinal social network studies can easily suffer from insufficient statistical power. Studies that simultaneously investigate change of network ties and change of nodal attributes (selection and influence studies) are particularly at risk because the number of nodal observations is typically much lower than the number of observed tie variables. This article presents a simulation-based procedure to evaluate statistical power of longitudinal social network studies in which stochastic actor-oriented models are to be applied. Two detailed case studies illustrate how statistical power is strongly affected by network size, number of data collection waves, effect sizes, missing data, and participant turnover. These issues should thus be explored in the design phase of longitudinal social network studies.

March 24, 2018: new version 1.2-10 of RSiena and RSienaTest available from the downloads page and the RSiena project at R-Forge.

Versions 1.2-9 and 1.2-10 are almost identical.
Main changes in RSiena and RSienaTest visible to users are the following. For the effects, consult the manual.

• Added effects avExposure, totExposure, infectDeg, susceptAvCovar, infectCovar for symmetric networks.
• New effects degAbsDiffX, degPosDiffX, degNegDiffX, degAbsContrX, degPosContrX, degNegContrX.
• Effects XWX, XWX1, and XWX2 enabled for bipartite networks.
• Added parameter=2 for FFDeg, BBDeg, FBDeg, FRDeg, BRDeg; the purpose here is to decrease collinearity with the ('single') degree.
• Corrected altInDist2, totInDist2.
• Export of last simulated state from ML simulations if returnDeps; meaningful for the imputed/simulated missing tie values.
• Added endowment and creation effects for maxAlt and minAlt.
• Added components requestedEffects, theta, and se to siena08 (theta and se are the ML estimates).
• Error in summary of sienaMeta object corrected.
• For non-directed networks, the initial model now contains only basic rates and degree effect.

• new function extract.posteriorMeans for sienaBayes results.
• In sienaBayes, restrict check of maximum estimated parameter value after initialization to non-fixed effects.
• Correct construction of groupwise effects object in sienaBayes so that this will work also when evaluation effects for density and/or reciprocity are not included; and when there are interaction effects for which the main effects are not included.
• Corrections for print and summary of sienaBayes objects.
  In print.sienaBayesFit, include fixed parameters and give credibility intervals for rate parameters; include variance parameters; allow shorter ThinParameters; print objects returned through partialBayesResult.RData.
• multipleBayesTest corrected (there was an error for testing 2 or more linear combinations simultaneously) and adapted for cases with fixed parameters; adapted help file text.

March 21, 2018: Paper at ArXiV about model specification for numerical actor covariates.

February 24, 2018: first publication using sienaBayes.

February 24, 2018: error in distance-2 effects.

The current versions of RSiena and RSienaTest at R-Forge have errors for the distance-2 selection effects such as altDist2 and altInDist2. The current version of RSiena at the Siena downloads page does not have this error.
If you wonder about whether the version you are using has this error, the answer will be clear, because estimation with these effects will run into errors and not finish seemingly adequately.

February 16, 2018: more help for selection and influence tables.

In the manual, the section about selection and influence tables was updated.
New scripts SelectionTables.r and InfluenceTables.r are available from the scripts page.

September 11, 2017: new version 1.2-4 of RSiena and RSienaTest available from the downloads page and the RSiena project at R-Forge.

Main changes in RSiena and RSienaTest visible to users:

• setEffect and includeEffects work again with the option include = FALSE.

September 9, 2017: error in version 1.2-3 of RSiena and RSienaTest

The option include=FALSE does not work in functions setEffect and includeEffects of RSiena and RSienaTest, version 1.2-3. This has been repaired in R-Forge version 1.2-4. The version on CRAN, however, still is 1.2-3 and has this error.
You can work around this by not using this option, i.e., just creating an effects object and including effects, not excluding them. This works unless you wish to exclude effects that are included by default, such as recip for directed and transTriads for non-directed networks. Another way to work around this is by finding out the row numbers of the effects you wish to exclude in the total effects object (for which you can employ something like [print(myeff,includeOnly=FALSE)]), and setting the include column in those rows to FALSE.
Still another way is to download the two files given here (in most browsers you can download by right-clicking and then saving them on your computer; do keep the names intact; Internet Explorer is lousy and tends to mess them up):

• printSienaEffects.r
• correction_excludeEffects.r

source("correction_excludeEffects.r")

and this should do the trick.

September 8, 2017: new versions 1.2-3 of RSiena at CRAN;
new version 1.2-3 of RSienaTest is available from the downloads page and the RSiena project at R-Forge.

For the moment, the versions of RSiena at CRAN and R-Forge are identical. The creation of the new CRAN version should have made the package more stable.
The only change in functionality between versions 1.2-3 and 1.2-1 is that the totAlt effect now has been made available also for non-directed networks.

September 4, 2017: new versions 1.2-1 of RSiena and 1.2-2 of RSienaTest are available from the downloads page and the RSiena project at R-Forge.

Short overview of main changes:

1. The version number now was moved up to 1.2.x because there will be a new CRAN version, and all the changes in the past years justify going from 1.1.x to 1.2.x.
   
2. You now need to specify MaxDegree, modelType, behModelType and Offset in a different way for sienaAlgorithmCreate, and algorithm objects have to be created anew!!
3. There is a new function score.Test which allows more easily getting results of score tests.
4. The use of modelType and the recent addition behModelType was corrected. For networkModelType = 3 (initiative and confirmation model for non-directed networks), an offset is added to the confirmation model. See the manual for further explanation.
5. There are improvements of sienaBayes, which should avoid errors occurring for some specifications.

A more complete listing of all changes is as follows.

Main changes in RSiena and RSienaTest visible to users:

• Score test and sienaGOF corrected for the case that some parameters are fixed and not tested.
• New function score.Test. This allows, for a sienaFit object in which some effects were tested with test=TRUE, to get the results of the score-type test for some or all of the parameters tested. When some effects are tested with test=TRUE, the results of the score test are also presented when printing the estimation result.
• Argument varName added to updateTheta. This allows the update to be restricted to one or more of the dependent variables.
• Operation of option 'absorb' (behModelType = 2) corrected.
• In sienaAlgorithmCreate, for parameters Offset, MaxDegree, modelType, and behModelType, it is now required that these are named vectors with the names of the dependent variables, or NULL. The use of non-named vectors for these, in the case of using multiple processes may have led to errors in earlier versions. Note that the default for MaxDegree now is not MaxDegree=0 but MaxDegree=NULL.
• New effects sameXInPopIntn, sameXOutPopIntn, sameXInActIntn, sameXOutActIntn.
• For networkModelType = 3 (initiative and confirmation model), an offset is added to the confirmation model; this is taken from Offset in the algorithm object.
• For effects inPopX, outPopX, inActX, outActX, sameXInPop, sameXOutAct, diffXInPop, diffXOutAct, homXInPop, homXOutAct, altXOutAct, diffXOutAct, changed interaction2 to '' (was '1', erroneously) and default parameter to 1.
• Additional parameter dropRates in print.sienaEffects (useful for sienaBayes with many groups).
• In print.sienaEffects, omit last remark about random effects if only one line is printed.
• Corrected use of modeltype.
• Added an example for multiple processes in the help page for sienaGOF, and took out verbose reporting from sienaGOF in case of multiple processes.
• Change in print.sienaAlgorithm for modelType.

• New parameter OffSet in sienaAlgorithmCreate.
• Registration of native routines (requirement for CRAN).

• Parameter UniversalOffset of sienaAlgorithmCreate renamed to Offset.
• Added posterior variances to print.sienaBayesFit.
• Correction of error in sienaBayes that could lead to errors in results of estimations with data sets for multiple dependent variables (networks and/or behavior) with model specifications that contain an interaction effect for a dependent variable that is not the last of the dependent variables.
• Various changes in sienaBayes for less memory use and avoiding crashes in some situations.

June, 2017: RSiena wins the INSNA William D Richards Award.

This is a "lifetime achievement award", granted at the 2017 International Sunbelt Social Networks Conference (Beijing) to Tom Snijders and Christian Steglich, for "publically available social network analysis software without which it would be impossible to study social networks".

May 12, 2017: new versions 1.1-307 of RSiena and RSienaTest are available from the downloads page and the RSiena project at R-Forge.

Main changes in RSiena and RSienaTest visible to users:

• New function updateSpecification to include in an effects object a set of effects that are already included in another effects object. This can be useful, e.g., for multivariate data sets, for copying the specification for one dependent variable to another dependent variable.
• New effects inPopX, outPopX, inActX, outActX, sameWXClosure, degPlus, absDiffX, avAltPop, totAltPop, egoPlusAltX, egoPlusAltSqX, egoRThresholdX, egoLThresholdX, altRThresholdX, altLThresholdX.
• outOutAss dropped for symmetric networks; only outInAss remains.
• egoX effect has interactionType='ego' also for symmetric networks.
• The exclusion of effects if the variance of a covariate is 0, or a covariate has only two values, is dropped (these effects have no meaning, but their exclusion was a potential nuisance for meta-analyses).
• Description of gwespFB and gwespBF corrected.
• includeInteraction has an additional parameter random.
• siena08 now also accepts a list for ...
• Argument behModelType added to sienaAlgorithmCreate. For behModelType=2, the 'absorbing option' is chosen in the model for behavioral dependent variables; see Section 5.8 of the manual.
• Indication of effect parameters dropped in names for altInDist2 and totInDist2 (they have no effect parameters).
• ModelType now is specific to the dependent variable: given as a named integer vector in sienaAlgorithmCreate.
• sienaAlgorithmCreate, siena07: new option lessMem, reducing storage in siena07 by leaving out z$ssc and z$sf2 from the object produced;
  note that these are used by sienaTimeTest and sienaGOF, so running those functions will be impossible for sienaFit object obtained with lessMem=TRUE.
• extended information in print.sienaAlgorithm.
• Modified check for singular covariance matrix after Phase 3.
• Warning if includeEffects is used with parameter random.
• Warning for impossible or zero changes if maximum likelihood (see Section 6.9, and issue below for March 21, 2017).
• Some parts dropped from the object produced by siena07 to reduce memory use.

Changes in RSienaTest:

• sienaBayes: various changes to save memory (thanks to Ruth Ripley); improved reporting of groups with no changes;
  warning for impossible changes, see Section 11.3.1 of the manual;
  if priorRatesFromData=2, change to different robust covariance matrix estimator when this is necessary (i.e., for small number of groups);
  in print.summary, also report nImproveMH;
  a few lines added to help file.
• print.sienaEffects gives dimensions of priorMu and priorSigma if includeRandoms.

March 21, 2017: Bug for maximum likelihood and sienaBayes estimation due to structural values
This is a bug for versions up to and including 1.1-305.

For some time it has been known that maximum likelihood estimation using siena07 may have problems with structural zeros; also, it has been found that sienaBayes sometimes hangs after the initialization phase.
This seems to be because likelihood-based estimation by RSiena does not allow the following data configurations:

• tie variables in two consecutive waves changing from structural zero (code 10) to 1;
• tie variables in two consecutive waves changing from structural one (code 11) to 0;
• tie variables in three consecutive waves changing from structural zero (code 10) to NA to 1;
• tie variables in three consecutive waves changing from structural one (code 11) to NA to 0;
• and, for more than three consecutive waves, similar patterns with more NAs in between.

To solve such issues, the following remedies may be applied.

• If you are using siena07, then convert the data to a multi-group data set (a procedure somewhat similar to what is proposed by de la Haye, Embree, Punkay, Espelage, Tucker and Green in Social Networks, 2017; but without changes to the actor set).
• If you are using sienaBayes, make minimal changes to the data set so as to avoid the configurations mentioned above. For example, replace sequences 10-1 by NA-1, and 10-NA-1 by NA-NA-1.
  An example script to make such replacements is changeForbiddenChanges.R

In version 1.1-306 of RSiena/Test, the functions siena07 and sienaBayes include checks for these configurations, so that they lead to decent error messages instead of R hanging indecently.

October 10, 2016: new versions 1.1-302 of RSiena and RSienaTest are available from the downloads page and the RSiena project at R-Forge.

Main changes in RSiena and RSienaTest visible to users:

• New effects: sameXCycle4, homCovNetNet, contrastCovNetNet, covNetNetIn, homCovNetNetIn, contrastCovNetNetIn, inPopIntnX, inActIntnX, outPopIntnX, outActIntnX.
• Changes permitting the 4-cycles effects for larger and denser networks.
• Dropped cl.XWX effect from two-mode - one-mode coevolution (did not belong).
• egoSqX is an ego effect.
• Added cycle4 for one-mode networks.
• Added outAct, outInAss for symmetric networks.
• sienaRI: Structural zeros and ones are excluded from the calculations; option getChangeStatistics was added; row names were given to matrices that have rows corresponding to effects; the function now runs for models with only 1 parameter, and does not run, but also does not crash for a bipartite dependent variable.
  In the manual, a section about sienaRI was added (Section 13.5).
• Warning if includeEffects is used with parameter parameter (which it doesn't have, and signals confusion with getEffect).
• Small additions to print.sienaAlgorithm.
• Clearer output for MaxDegree in print.sienaFit.
• Correction of how effect parameter for outInAss for 2-mode networks is reported.

August 18, 2016: new versions 1.1-296 of RSiena and RSienaTest are available from the downloads page and the RSiena project at R-Forge.

Note: for the Mac version of RSienaTest, use the version at the downloads page rather than at R-Forge.

Changes in RSiena and RSienaTest:

• Corrected cycle4 effect for parameter=2 (i.e., sqrt version). (The results were not wrong but the new estimator for this effect is more efficient.)
• Error corrected in effects avWAlt and totWAlt.
• Warning if includeInteraction is used for more interactions than available given parameters nintn and behNintn.
• Additional auxiliary function CliqueCensus presented in help page sienaGOF-auxiliary.
• Deleted session parameter from print01Report.

Incompatibilities

It is not guaranteed that objects created by earlier versions of RSiena/Test can still be manipulated by more recent versions. Sometimes they will, but there also may be clashes. Therefore when starting to work with a new version of RSiena/Test it is advisable to create data objects and effects objects anew. If this leads to serious problems that cannot be solved by updating your objects, you could bring this up in the Siena/Stocnet yahoo discussion group.
Incompatibilities of functions that may lead to the necessity to adapt scripts are listed below.

• Since version 1.1-282, parameter priorRatesFromData in sienaBayes has values 0-1-2, with 0 = former FALSE, 1 = former TRUE, 2 = (new and default) robust estimation of prior for rate parameters from estimates at the end of initialization phase.
• Since version 1.1-279, an effects object is no longer used as argument for print01Report() (this was superfluous).
• Since version 1.1-279, effect AltsAvAlt was renamed to avXAlt (in line with other naming conventions).

Bugs

Minor bugs

• There is an error in the description of the gwespFB and gwespBF effects in RSiena/Test. The error is made in the effectsDocumentation and also when printing results of a fit including these effects, and also in the manual p. 113. The direction of the arrows is reversed. The description of the mixed effects on p. 133 is correct.
  This will be corrected in the next version (1.1-306).
• Under R 3.4.0, running siena07 and sienaBayes will lead to the following warning messages:

  In x$truncation * fra/maxRatio :
  Recycling array of length 1 in vector-array arithmetic is deprecated. Use c() or as.vector() instead.

  This can safely be ignored (it's just a warning, due to stricter and safer rules in R 3.4.0). In the next version of RSiena/Test (1.1-306) this warning will have disappeared.

• There is an error in sienaGOF() in the case that co-evolution of one-mode and two-mode networks is analysed; the bug occurs only if the networks were ordered in sienaDataCreate in such a way, that a two-mode network comes before a one-mode network. Then sienaGOF will give incorrect results but most likely will run into an error.
  The solution is simple: order the networks in sienaDataCreate so that the one-mode networks come first, followed by the two-mode networks, followed by the behavioral variables.
• The implementation of endowment and creation effects does not agree with changing structural zeros or ones. Therefore, if there is changing composition in a data set and the model should contain endowment and/or creation effects, the changing composition should be reflected by a changing composition ("compositionChange" object) and not by structural zeros.
  On the other hand, for goodness of fit testing by sienaGOF, data with a "compositionChange" object are not treated properly; there the structural zeros are necessary for treating changing composition. But if you use NA codes for the tie variables of absent actors, and these are indicated as such by a "compositionChange" object, then sienaGOF will work properly.
• The function sienaRIDynamics seems still / again to be incorrect (runs into a crash).
• The effect outRateLog (dependence of log rate on log(outdegree+1)) works properly only for non-conditional estimation (set cond = FALSE in sienaAlgorithmCreate).

For past bugs, see below.

May 28, 2016: new versions 1.1-294 of RSiena and RSienaTest are available from the downloads page and the RSiena project at R-Forge.

Changes in RSiena and RSienaTest:

• The manual was taken out of the installation; it still is in the source code distribution as RSienatest\doc\RSiena_Manual.tex. It still is publicly available from this website as http://www.stats.ox.ac.uk/~snijders/siena/RSiena_Manual.pdf.
• New effects totAltEgoX, totAltAltX, egoSqX, diffX, diffSqX, egoDiffX, avAltW, totAltW, avSimW, totSimW, jumpFrom, jumpSharedIn, mixedInXW, mixedInWX, avWalt, totWAlt.
• inActIntn also implemented for two-mode dependent networks.
• Endowment and creation effects were added for inAct, inActSqrt, outAct, outActSqrt.

Changes in RSiena:

• siena01Gui() and sienaDataCreateFromSession() dropped.

• Change in endowment effect estimation for avAlt effect.
• New effects simmelian, simmelianAltX, avSimmelianAlt, totSimmelianAlt.

Additional advice for how to achieve good convergence (in cases where this is difficult, e.g., complicated models and data sets) is given in Section 6.2 of the manual.
This - and more - was discussed at the third Advanced Siena Users' Meeting (AdSUM-2016) held in Zürich February 19-20, of which the slides are available here.

February 4, 2016: new versions 1.1-290 of RSiena and 1.1-291 of RSienaTest are available from the RSiena project at R-Forge and from the downloads page.

Changes in RSiena and RSienaTest:

• New effects FBDeg, FRDeg, BRDeg (RFDeg was mentioned earlier but was not implemented; its place is now taken by FRDeg), gwespFFMix, gwespBBMix, gwespBFMix, gwespFBMix, gwespRRMix, gwespMix.
• Corrected the omission of the check for positive derivative matrix at the end of phase 1 for effects with fixed parameters.
• Added parameters fix, test, parameter to includeInteraction(). This implies that the roundabout way of setting internal effect parameters for interaction effects, still explained at the help page of setEffect(), now is not necessary any more.
• More helpful error message for incorrect nodesets in sienaDataCreate(); extended help pages for sienaDataCreate() and sienaDependent().
• Correction to allow estimation for a one-dimensional parameter.
• fromBayes bug corrected.

Changes in RSienaTest:

• sienacpp() added (programmed by Felix Schönenberger); this includes Generalized Method of Moments estimation (Amati - Snijders); in the manual (Section 6.7) and in Siena algorithms (Section 3.3), some explanations are given.
• sienaBayes(): option initML added; check for zero distances; corrected function improveMH() in initialization.
• print.multipleBayesTest(): option descriptives added.
• glueBayes(): added p1 and p2 to created object.

This book is announced here. It is mostly about cross-sectional network data. In Chapter 2, The Multiple Flavours of Multilevel Issues for Networks by Tom Snijders, there is a section about the Stochastic Actor-Oriented Models for Multilevel Networks.
Note that almost at the same time, a special issue appeared of the journal Social Networks about the same theme.

For modeling transitivity in actor-oriented models, the gwesp effect often is better than the 'traditional' transTrip and transTies effects. 'Better' in the sense that often it will give a better fit (or, as good a fit as using the other two together) and better convergence. The gwesp effects (there are several: gwespFF, gwespBB, etc.) are extensively explained in the manual. gwespFF is similar to transTrip, and gwespBB is similar to cycle3. Then there are gwespFB, gwespBF, and gwespRR, which are other ways to improve the fit of the triad census in cases where this is desirable.

It probably is usually not a big difference, but one might use gwespFF as a default instead of transTrip or transTies, unless one does not want to go through the hassle of the extra explanation.

Note that the gwesps are elementary effects. The difference between elementary and evaluation effects also is explained in the manual. A practical consequence is that they can be used in interactions (their interactionType is dyadic). Therefore, the gwesp-related effect that has the similar role as transRecTrip (cf. Per Block, 2015, "Reciprocity, transitivity, and the mysterious three-cycle", Social Networks, 40:163-173) is the interaction between gwespFF and reciprocity, and can be defined by includeInteraction(). Since version 1.1-290, the internal effect parameter of such an interaction can be set straightforwardly by includeInteraction(). See the help page for this function (and possible also the help page for setEffect()).

September 10, 2015: new version (1.1-289) of RSiena and RSienaTest is available from the RSiena project at R-Forge and from the downloads page.

Changes in RSiena and RSienaTest:

• New defaults for siena07() set in sienaAlgorithmCreate(): doubleAveraging=0, diagonalize=0.2 (for MoM).
  Note: this will influence convergence properties (it should improve convergence).
• Improved one-step approximations to expected Mahalanobis distances in sienaGOF() (using control variates for score function).
• Permit 3-way interactions with one ego and two dyadic effects (this was erroneously not allowed).
• New effects Jin, Jout, JinMix, JoutMix, altXOutAct, doubleInPop, doubleOutAct.
• print01Report() now reports in-degrees also for two-mode networks.
• Better error handling for sienaTimeTest and scoreTest.
• inOutAss is dyadic.
• Corrected effectName and functionName of inPopIntn, outPopIntn, inActIntn, and outActIntn ('in' and 'out' were missing).
• Check for positive derivative matrix at the end of phase 1 (non-positive estimated derivatives lead to repeating a prolonged phase 1) omitted for effects with fixed parameters.

• New effects homXOutAct, FFDeg, BBDeg, RFDeg, diffXTransTrip (ported from RSienaTest).
• sameXInPop and diffXInPop also added for two-mode networks; but they are not dyadic!
• In names of behavior effects and statistics dropped the (redundant) parts 'behavior' and 'beh.' (as done earlier in RSienaTest).

• New function extract.sienaBayes().
• sienaBayes(): options diagonalize=0.2, doubleAveraging=0 for estimation of initial models in initialization phase; save initial results in case of divergence during initialization phase; check for large initial estimates done only for non-rate parameters.

July 21, 2015: new version (1.1-288) of RSiena and RSienaTest is available from the RSiena project at R-Forge and from the downloads page.

Changes in RSiena and RSienaTest:

• plot.sienaRI(): new parameter actors; proportions with piechart improved (hopefully); effect of parameter radius changed.
• siena.table does no more produce the double minus sign in html output.

• Correction of error for two-mode networks in sienaGOF().

• New effects homXOutAct, FFDeg, BBDeg, RFDeg, diffXTransTrip.
• sameXInPop and diffXInPop also added for two-mode networks; but they are not dyadic!
• In names of behavior effects and statistics dropped the (redundant) parts behavior and beh..
• sienaBayes: new parameter nSampRates; correction in use of prevBayes; more efficient calculation of multivariate normal density.
• Small changes in HowToCommit.tex.

Some recent publications (many others are at the applications page)

A paper that presents a method for meta-analysis, which is (presumably) better than siena08(). It uses R package mvmeta; note that another package for meta analysis that may be used is metafor. These packages can include explanatory variables at the network level. Also see the RSiena manual, Section 11.2:

June/July, 2015: Advanced Siena users' workshop slides of Sunbelt 2015

At the literature tab of the Siena pages, section Presentations (teaching material), there are available the slides of the Advanced Siena users' workshops of Sunbelt 2014 and 2015.

May 22, 2015: Stronger convergence criterion necessary

It turns out that the usual convergence criterion, tmax <= 0.10 where tmax is the maximum absolute value of the t-ratios for convergence, is not a satisfactory measure of convergence of the Robbins-Monro estimation algorithm used for RSiena. Earlier, simulation results had been obtained that provided support for this criterion, but it now has been found that especially when using non-centered covariates this criterion may be inadequate; and if models with non-centered covariates lead to potential problems for this convergence criterion, then it cannot be excluded that other models also do.
A stronger convergence criterion has been found that does give an adequate signal of convergence (the past shows that you never can be sure, but to our current knowledge there are convincing signs that this is adequate). This is the overall maximum convergence ratio, described since some time in the RSiena manual, and given with sienaFit objects as the element tconv.max. This 'overall maximum convergence ratio' is defined as the maximum convergence ratio for linear combinations of the estimation statistics, while tmax is the maximum absolute convergence ratio for only the set of statistics (i.e., coordinates) directly used for the estimation. Section 3.2 of Siena_algorithms.pdf gives definitions and further explanations, and arguments for the use of the criterion tconv.max <= 0.25. Some examples were found, for models including non-centered covariates, with important differences between results for estimations where only tmax <= 0.10, and estimations where also tconv.max <= 0.25. This shows that the earlier convergence criterion is not reliable. (@*&#!!) The stronger criterion can be satisfied by repeating, if necessary, estimation with successive use of the prevAns parameter, as before, and as explained in the manual. To improve convergence, some additional options for siena07() were developed. These are given as parameters to sienaAlgorithmCreate(). The main parameters for this purpose are doubleAveraging, diagonalize (this option was corrected and improved), and n2start. Their use is explained in Section 6.1.3 of the new edition of the RSiena users' manual.

Thus, the new rule for convergence is that tmax <= 0.10 (as before) and tconv.max <= 0.25 (new); the latter condition is the more important one. This now is mentioned in the RSiena manual.

The conclusion is that RSiena users are advised to reconsider their estimation results and continue the estimation until this criterion is satisfied. There is a possibility of differences in the results; it is not expected that there will be many important differences, but the possibility does exist.

May 22, 2015: new version (1.1-286) of RSiena and RSienaTest is available from the RSiena project at R-Forge.

Changes in RSiena and RSienaTest:

• The new convergence criterion tconv.max <= 0.25 is advertised in the manual.
• Component tmax added to sienaFit objects and tconv.max mentioned in print.sienaFit().
• sienaAlgorithmCreate() has new arguments n2start, truncation, doubleAveraging, standardizeVar; these allow improved possibilities for convergence, as indicated in Section 6.1.3 of the manual.
• Diagonalization (option diagonalize) corrected.
• When there are missings in constant or changing monadic covariates, and centered=FALSE for their creation by coCovar() or varCovar(), the mean will be imputed (used to be 0, which was an error). For changing covariates, this is the global mean.
• In coCovar() and varCovar() there is a new argument imputationValues, which are used (if given) for imputation of missing values. Like all missings, they are not used for the calculation of the target statistics in the Method of Moments.
  If one has a reasonable way of imputing the missing values, this will give more adequate simulations than the imputation of everything by the global mean.
• New effects: outOutActIntn, toDist2, from.w.ind.
• In the target statistic for the higher effect, contributions for value(ego)=value(alter) are now set appropriately at 0.5 (was 0).
• Decent error message when there are (almost) all NA in the dependent behavioural variable.
• The centering within effects for similarity variables at distance 2 now is done by the same similarity means as for the simX effect.

Some past bugs

• The four-cycle effects (cycle4, cycle4ND, sharedPop) sometimes had convergence difficulties caused by numerical overflow of the int data type in C++; this was the case if the number of nodes and the degrees etc. were such that the number of three-paths constituting the four-cycles could become too large. This was repaired in version 1.1-302.
  By the way, numerical overflow also will be a limitation for applying RSiena to very large networks for some other effects.
• For two-mode networks there was an error in sienaGOF() (semi-diagonal of generated adjacency matrices set to 0); this was repaired in RSiena version 1.1-288 and in RSienaTest version 1.1-286 of June 02, 2015.
• Siena and RSienaTest until and including version 1.1-284 dealt incorrectly with missing data in covariates when in coCovar() and varCovar() the option "center=FALSE" is used (this is not the default option). When covariates are not centered and contain missing values, these missings were imputed by the value 0. In most cases, this is not reasonable. This means that results for non-centered covariates with missings will be wrong.
• sienaBayes until and including version 1.1-283 contained an error for sampling the constant parameters.
  This was corrected in version 1.1-284.
• If there is an exact multicollinearity, a warning was given 'Noninvertible estimated covariance matrix', but nevertheless standard errors were reported (which should not be used).
  This was corrected in version 1.1-284.
• print01Report gave an error message for a sienaGroup object where the component objects have constant dyadic covariates.
  This was corrected in version 1.1-284.
• Effects cl.XWX and cl.XWX2 were not implemented correctly in versions RSiena and RSienaTest up to and including version 1.1-281. In these versions they were identical to cl.XWX1. A correction was made in version 1.1-282. (This error was not present in effects XWX and XWX2.)
• The component se of sienaFit objects produced by siena07(), which is there since version 1.1-271, was incorrect until and including version 1.1-277: it contained the squared standard errors instead of the standard errors themselves. This was fixed in version 1.1-278.
  This only affected the se component, the standard errors given in the usual output and print of siena07() were not affected.

Slides of presentations in 2014 on 'Advanced Siena' issues

• The slides of the presentation by Tom Snijders about recent developments in Siena, presented at the meeting of the European Collaborative Research Project Social Influence in Dynamic Networks (Paris, December 15-18, 2014) are available here.

• The slides of the Advanced Siena user's workshop at Sunbelt 2014 are available here. Pages 10-13 are new (they were not included in the workshop).

Some recent publications (many others are at the applications page)

First a position paper about coevolution that contains no application of Siena, but mentions it as a tool.

• Stefan Tasselli, Martin Kilduff, and Jochen Menges. Microfoundations of Organizational Social Networks: A Review and an Agenda for Future Research (2015). Journal of Management, 41, 1361-1387.
  DOI: http://dx.doi.org/10.1177/0149206315573996.

  Abstract
  This paper focuses on an emergent debate about the microfoundations of organizational social networks. We consider three theoretical positions: an individual agency perspective suggesting that people, through their individual characteristics and cognitions, shape networks; a network patterning perspective suggesting that networks, through their structural configuration, form people; and a coevolution perspective suggesting that people, in their idiosyncrasies, and networks, in their differentiated structures, coevolve. We conclude that individual attitudes, behaviors, and outcomes cannot be fully understood without considering the structuring of organizational contexts in which people are embedded, and that social network structuring and change in organizations cannot be fully understood without considering the psychology of purposive individuals. To guide future research, we identify key questions from each of the three theoretical perspectives and, particularly, encourage more research on how individual actions and network structure coevolve in a dynamic process of reciprocal influence.

Then some applications that may be of general interest.

• Per Block (2015). Reciprocity, transitivity, and the mysterious three-cycle. Social Networks, 40, 163-173.
  DOI: http://dx.doi.org/doi:10.1016/j.socnet.2014.10.005.

  Abstract
  Reciprocity and transitivity are the two most important structural mechanisms underlying friendshipnetwork evolution. While on their own they are understood in great detail, the relation between them israrely studied systematically. Are friendships outside of social groups more or less likely to be reciprocated than friendships embedded in a group? Using a theoretical framework that focusses on the situations inwhich friends interact and the social structures that stabilise one-sided friendships, I propose that thetendency towards reciprocation of friendships within transitive groups is usually lower than outside of transitive groups. In a meta-analysis of two datasets including 29 friendship networks using stochastic actor-oriented models, the interaction between reciprocity and transitivity is analysed. Supporting the theoretical reasoning, the interaction is consistently negative. Second, the tendency against forming three-cycles in friendship networks, which was consistently found in previous studies, is shownto be spurious and a result of neglecting to control for the tendency against reciprocation in transitive groups. The tendency against three-cycles is commonly seen as an indicator that unreciprocated friendships indicate local hierarchy differences between individuals; this proposition has to be re-evaluated in light of the findings of this study. Future studies that analyse the evolution of friendship networks should consider modelling reciprocation in transitive triplets and potentially omit modelling three-cycles.
  

• Per Block and Thomas Grund (2014). Multidimensional homophily in friendship networks. Network Science, 2, 189-212.
  DOI: http://dx.doi.org/10.1017/nws.2014.17.

  Abstract
  Homophily -the tendency for individuals to associate with similar others- is one of the most persistent findings in social network analysis. Its importance is established along the lines of a multitude of sociologically relevant dimensions, e.g. sex, ethnicity and social class. Existing research, however, mostly focuses on one dimension at a time. But people are inherently multidimensional, have many attributes and are members of multiple groups. In this article, we explore such multidimensionality further in the context of network dynamics. Are friendship ties increasingly likely to emerge and persist when individuals have an increasing number of attributes in common? We analyze eleven friendship networks of adolescents, draw on stochastic actor-oriented network models and focus on the interaction of established homophily effects. Our results indicate that main effects for homophily on various dimensions are positive. At the same time, the interaction of these homophily effects is negative. There seems to be a diminishing effect for having more than one attribute in common. We conclude that studies of homophily and friendship formation need to address such multidimensionality further.

• Alessandro Lomi and Christoph Stadtfeld (2014). Social Networks and Social Settings: Developing a Coevolutionary View. KZfSS Kölner Zeitschrift für Soziologie und Sozialpsychologie, 66, 395-415.
  DOI: http://dx.doi.org/10.1007/s11577-014-0271-8.

  Abstract
  One way to think about social context is as a sample of alters. To understand individual action, therefore, it matters greatly where these alters may be coming from, and how they are connected. According to one vision, connections among alters induce local dependencies-emergent rules of social interaction that generate endogenously the observed network structure of social settings. Social selection is the decision of interest in this perspective. According to a second vision, social settings are collections of social foci-physical or symbolic locales where actors meet. Because alters are more likely to be drawn from focused sets, shared social foci are frequently considered as the main generators of network ties, and hence of setting structure. Affiliation to social foci is the decision of central interest in this second view. In this paper we show how stochastic actor-oriented models (SAOMs) originally derived for studying the dynamics of multiple networks may be adopted to represent and examine these interconnected systems of decisions (selection and affiliation) within a unified analytical framework. We illustrate the empirical value of the model in the context of a longitudinal sample of adolescent participating in the Glasgow Teenage Friends and Lifestyle Study. Social selection decisions are examined in the context of networks of friendship relations. The analysis treats musical genres as the main social foci of interest.

• Steven A. Haas and David R. Schaefer (2014). With a little help from my friends? Asymmetrical social influence on adolescent smoking initiation and cessation. Journal of Health and Social Behavior, 55, 126-143.
  DOI: http://dx.doi.org/10.1177/0022146514532817.

  Abstract
  This study investigates whether peer influence on smoking among adolescents is asymmetrical. We hypothesize that several features of smoking lead peers to have a stronger effect on smoking initiation than cessation. Using data from the National Longitudinal Study of Adolescent Health we estimate a dynamic network model that includes separate effects for increases versus decreases in smoking, while also controlling for endogenous network change. We find that the impact of peer influence is stronger for the initiation of smoking than smoking cessation. Adolescents rarely initiate smoking without peer influence but will cease smoking while their friends continue smoking. We discuss the implications of these results for theories of peer influence and health policy.

  
  

April 2, 2015: new version (1.1-284) of RSiena and RSienaTest is available from the RSiena project at R-Forge.

Changes in RSiena and RSienaTest:

• New effects: simEgoDist2, simEgoInDist2, simEgoDist2W, simEgoInDist2W, sameXOutAct, diffXInPop, diffXOutAct. The centering for the similarity measure in effects such as simEgoDist2 and simDist2 is not yet sorted out (but this affects only the outdegree parameter).
• For creating new effects, it is relevant that new effect groups covarABNetNetObjective, covarANetNetObjective, and covarBNetNetObjective were introduced. See SienaSpec.tex/pdf, section 4.9: covarNetNet.
• Bug corrected that occurred in print01Report() for a sienaGroup object where the component objects have constant dyadic covariates.
• When a statistic is not plotted in plot.sienaGOF() because its variance is 0, a note about this is printed to the screen.
• Minimum and maximum of plotted region in plot.sienaGOF() is calculated without taking into account non-plotted statistics.
• Bug in sienaTimeTest() corrected that occurred for includeTimeDummy when timeDummy was greater than or equal to 10.
• In case of collinear parameter estimates, standard errors are reported as NA.
• plot.sienaGOF(): arguments main and ylab now are not any more arguments of this function proper, but if used they are understood as part of the ... argument (so using main and ylab as arguments now should work properly). (Thanks to David Kavaler.)

Changes in RSienaTest:

• sienaBayes: error corrected for sampling constant parameters (this error led to incorrect results); correction in initialization of truncation rate parameters based on prior.

December 12, 2014: new version (1.1-282) of RSiena and RSienaTest is available from the RSiena project at R-Forge.

Changes in RSiena and RSienaTest:

• Effects cl.XWX and cl2.XWX corrected (thanks to Christoph Stadtfeld).
• interactionType of gwesp.. effects was made dyadic (this permits interactions).
• New effects reciPop, reciAct, in3Plus, maxAlt, minAlt, transTrip1, transTrip2.
• Effect antiInIsolate2 got alias in2Plus.
• inPop is a dyadic effect (except for non-directed networks) (this permits interactions).
• egoX added as effect for non-directed networks (can be important for representing effects of group-level covariates in multi-group analyses).
• Components IActors and expectedI (not relative, but 'raw' importance of effects) added to sienaRI() and print.sienaRI().
• The check for MaxDegree when running siena07() now works properly also for sienaGroup objects.
• Manual introduces the term elementary effects.

Changes in RSienaTest:

• sienaBayes: the stop caused by singularity of the precision matrix after the multi-group estimation now is circumvented; still a warning is printed to the screen. (This permits the use of multiple elementary effects that have the same target statistic).
• sienaBayes: option priorRatesFromData changed to values 0-1-2, with 0 = former FALSE, 1 = former TRUE, 2 = robust estimation of prior for rate parameters from estimates at the end of initialization phase.

November 19, 2014: new version (1.1-280) of RSienaTest is available from the RSiena project at R-Forge.

Version 1.1-280 is almost the same as 1.1-279.

Changes in RSienaTest:

• Small changes in help pages for sienaGOF and for sienaCompositionChange.
• New parameters nSampVarying and nSampConst in sienaBayes().

November 13, 2014: new version (1.1-279) of RSiena is available from the RSiena project at R-Forge, and of RSienaTest from the downloads page.

The main changes of version 1.1-279 compared to 1.1-278 are the following.

There are two changes in RSiena and RSienaTest that will lead to incompatibilities with existing scripts, so that you will need to modify these. All scripts at the website were modified accordingly. Note, however, that sensible error messages will be produced (hopefully) when scripts according to the old implementations are used:

• Effect AltsAvAlt renamed to avXAlt (this seems more in line with other naming conventions).
• Effects object no longer used as argument for print01Report() (this was quite superfluous really).

• A lot of new effects were added: sameXInPop, transRecTrip2, totAlt, avInAlt, totInAlt, totRecAlt, totXAlt, avXInAlt, totXInAlt, avAltDist2, totAltDist2, avTAltDist2, totAAltDist2, avXAltDist, totXAltDist2, avTXAltDist2, totAXAltDist2, avInAltDist2, totInAltDist2, avTInAltDist2, totAInAltDist2, avXInAltDist2, totXInAltDist2, avTXInAltDist, totAXInAltDist2, XWX1, XWX2, cl.XWX1, cl.XWX2. See the manual!
• Endowment and creation effects were added for gwesp... effects.
• Some meaningless effects for two-mode networks were dropped.
• For non-invertible covariance matrices occurring at the end of siena07(), a diagnostic now is given for the linear combination that causes trouble.
• The function igraphNetworkExtraction() in the help page for sienaGOF-auxiliary was corrected (the earlier version dropped isolated nodes from simulated networks).
• In the help page for sienaGOF-auxiliary.Rd, the example of constraint is replaced by the example of eigenvector centrality (because constraint is undefined in igraph for isolated nodes, leading to computational problems).
• The diagonal of observed networks is set to 0 in sparseMatrixExtraction(); this diagonal was liable to lead to problems in assessing goodness of fit for data sets where the original data has a non-zero diagonal.
• The function sienaRIDynamics() (Indlekofer and Brandes, Network Science, 2013) was restored, after corrections.
• The "file" parameter of sienaRI() was dropped (it implied platform dependence).
• The section in manual about user-defined interaction effects was updated.
• Parameter showAll was added to descriptives.sienaGOF().
• Some minor checks were implemented, bugs corrected, and error messages improved (see the manual or the CHANGELOG file in the package for details).
• The file Siena_algorithms4.tex, which contains an overview of much of the coding used in RSiena, was renamed Siena_algorithms.tex and the associated pdf file now is available also at the Siena website (literature tab).
• p-values for goodness of fit in sienaGOF() are rounded to 3 decimal places, because more is definitely unreliable.
• The file effects.pdf was dropped from the distribution (it can be created by effectsDocumentation()).

• sienaBayes(): new parameters nImproveMH and priorRatesFromData; these give the possibility to truncate initial rate parameters depending on the prior (see manual).
• glueBayes() was corrected so that it can be applied sequentially.
• multipleBayesTest() now allows a matrix parameter to test linear combinations.
• Improved plot.multipleBayesTest (showing truncation at 0).

July 25, 2014: new version (1.1-278) of RSiena and RSienaTest is available from the downloads page.

The main changes of version 1.1-278 compared to 1.1-276 are the following.

• s50s was added to the data set.
• The se component of sienaFit objects was corrected (it should be the standard error, but was its square).
• new effects totDist2, altInDist2, totInDist2, totDist2W, altInDist2W, and totInDist2W were added.
• Some errors in print01Report() were corrected, and this function was further slightly improved, for descriptives for changing dyadic covariates and for cases where changes are only upward or downward.
• In sienaBayes (available in RSienaTest only), the data-dependent choice of priorSigma for the rate parameters now is internally multiplied by priorKappa; z$nwarm is changed to 0 if prevBayes is used; and the plotit functionality was dropped.
• sienaBayes(), glueBayes(), and print.sienaBayes() (all available in RSienaTest only) were adapted to allow inclusion of interaction effects without the corresponding main effects.
• A parameter nwarm2 was added to glueBayes().

In general you can see the changes of the two packages with respect to earlier versions in appendix B of the Siena users' manual and, more in detail, in the changeLog file in the source code.

July 16, 2014: sienaBayes

The function sienaBayes() in package RSienaTest version 1.1-276, developed by Johan Koskinen and Tom Snijders, now has developed far enough to be used. This is a function for multilevel Siena analyses, i.e., for analyzing network dynamics in multiple independent groups in which the same data structure, number of waves, and model specification hold, but (contrary to the existing multiGroup option) with parameters that may differ between groups. The R help page provides, as usual, the primary information. Section 11.3 of the manual is further important documentation.
It is still a beta version, and we are in a phase of collective collection of information about how to use it. The wiki-type of interactive website that was mentioned here previously has been discontinued because it was too slow. Please use the regular Siena-Stocnet discussion list for interaction about sienaBayes.

June 3, 2014: new version (1.1-276) of RSiena and RSienaTest is available from the downloads page.

The main changes of version 1.1-276 compared to 1.1-274 are the following.

• The function sienaBayes with some auxiliary functions (print, summary, bayesGlue) is now implemented in RSienaTest and released for use.
• Function includeEffects() now includes parameters fix and test.

April 15, 2014: Paper by Charlotte Greenan about diffusion of innovations

The paper by Charlotte Greenan, Diffusion of innovations in dynamic networks, appeared on the website of Journal of the Royal Statistical Society: Series A (Statistics in Society).

Abstract
The evolution of a dynamic social network and the diffusion of an innovation are jointly modelled, dependent on one another, by using an extension of a stochastic actor-oriented model developed by Snijders, which is modified so that the adoption times follow a proportional hazards model. The asymptotic behaviour of the method-of-moments estimator is examined. The model is demonstrated on a data set involving the initiation of cannabis smoking among adolescents, and a simulation study is presented.

Further information
This paper models the co-evolution of networks and behavior for the case where the behavior is a binary variable that can only increase. The basic idea is that such a variable indicates a time to event. This is also called adoption of innovation, event history analysis, duration analysis, survival analysis, etc.
The special aspect of this model is that effects are contained mainly in the rate function instead of the evaluation function. This yields the proportional hazards model for the time-to-event distribution. The implemented effects are described in the RSiena manual in Section 12.2.4: search for the average exposure effect (`avExposure') which is the first of these effects mentioned.

For an application, see

April 26, 2014: new version (1.1-274) of RSiena and RSienaTest is available from R-Forge.

A new version (1.1-274) of RSiena and RSienaTest can be obtained from the RSiena project at R-Forge.
A version for Mac is available from the downloads page.

The main changes of version 1.1-274 compared to 1.1-254 are the following.

• There now is the new function sienaRI(), contributed by Natalie Indlekofer, to assess relative importance of effects according to
  Indlekofer, Natalie, and Brandes, Ulrik, (2013). Relative importance of effects in stochastic actor-oriented models. Network Science, Vol. 1, Issue 3, 278-304.
  DOI: http://dx.doi.org/10.1017/nws.2013.21.
• Effect homWXClosure repaired.
• Possibility (optional) not to center dyadic covariates.
• Further work on sienaBayes() in RSienaTest.
• Duplication of outInv and outSqInv effects for two-mode networks canceled.
• Added component se (standard errors) to sienaFit objects.

February 17, 2014: new version (1.1-254) of RSiena and RSienaTest is available from R-Forge.

A new version (1.1-254) of RSiena and RSienaTest can be obtained from the RSiena project at R-Forge.

The main changes of version 1.1-254 compared to 1.1-250 are the following.

It should be noted that two changes were made that potentially have an influence on some results obtained.

First, the effects gwespFF, gwespBB, gwespFB, gwespBF, gwespRR were modified (thanks to Nynke Niezink) to bring their parametrization in accordance with the literature. This means that the 'old' parameter alpha' was effectively replaced by alpha = -log(1-exp(alpha')); here alpha is the internal effect parameter divided by 100. For the default alpha = log(2) we have alpha' = alpha, so this means no difference.
Second, in the help page for sienaGOF-auxiliary, geodesic distances were changed to non-directed. This makes more sense usually and was done to avoid runtime errors that occurred very rarely.

• New effects cl.XWX, homXTransTrip, homWXClosure, and sharedPop were added.
• Effect cycle4 extended to non-directed one-mode networks (for directed one-mode networks this is sharedPop).
• Effects outRateLog and outTrunc2 were ported to RSiena from RSienaTest.
• Effect jumpXTransTrip extended to non-directed networks.
• gwesp.. effects modified (see above) and extended to non-directed networks.
• Manual: added paragraph about how to import results from xtable() and siena.table() into MS-Word.
• User-defined interactions for dependent behavior variables now also are handled more adequately in functions setEffect() and updateTheta(), and when using the prevAns parameter in siena07(). (This was done for user-defined interactions for networks already in version 1.1-250.)
• sienaGOF: added the name of the sienaFit object as attribute sienaFitName to each of the sienaGofTest objects.
• Correction in sparseNetworkExtraction() to avoid errors occurring when the extracted network has no edges.
• In the help page for sienaGOF-auxiliary, geodesic distances changed to non-directed; this avoids a further error when the extracted network has no edges.
• Correction: Effect to is not a dyadic effect.
• Correction of an error in print.siena for data sets including other types than oneMode.
• Changed bandwidth selector for violin plots in plot.sienaGOF to "nrd", to avoid long violins in cases where all simulations have the same outcome.
• Further work on sienaBayes() in RSienaTest.

December 4, 2013: new version (1.1-250) of RSiena and RSienaTest available from R-Forge.

A new version (1.1-250) of RSiena and RSienaTest can be obtained from the RSiena project at R-Forge.

The main changes of version 1.1-250 compared to 1.1-246 are:

• Actor covariates do not need to be centered any more. This can be important for use with the effects avSimAltX, totSimAltX and avAltAltX, new in version 1.1-246. Centering is determined by the parameter 'centered' in functions coCovar() and varCovar(). Centering still is the default.
• Functions Wald.RSiena() and Multipar.RSiena() were added, carrying out Wald-type test after estimation by siena07().
• User-defined interactions are now handled more adequately in functions setEffect() and updateTheta(), and also when using the prevAns parameter in siena07().
• A problem in siena07() sometimes (rarely) occurring, giving an error message mentioning 'cvalue', was corrected.
• Divergent parameters in siena07() get NA for their rows and columns in the covariance matrix, instead of values like 999.
• A number of changes in revision 244 were ported from RSienaTest to RSiena.

October 31, 2013: new version (1.1-246) of RSiena and RSienaTest available from R-Forge.

A new version (1.1-246) of RSiena and RSienaTest can be obtained from the RSiena project at R-Forge.

The main changes of version 1.1-246 compared to 1.1-245 are:

• New behavior objective function effects avSimAltX, totSimAltX and avAltAltX to differentiate sources of peer influence in directed networks.
• Fix of a bug that occurred in the case of on average decreasing behavior variables.

October 17, 2013: new version (1.1-245) of RSiena and RSienaTest available from R-Forge.

A new version (1.1-245) of RSiena and RSienaTest can be obtained from the RSiena project at R-Forge.

The main changes of version 1.1-245 compared to 1.1-243 are (for RSienaTest only, RSiena still is the same):

• Available again for Mac (RSiena was already available for Mac), thanks to Mark Ortmann and James Hollway.
• New structural rate effect outRateLog (dependence of rates on a power of (outdegree + 1)).
• New effect outTrunc2 (duplicate of outTrunc, allows using this effect simultaneously with two effect parameters).
• Start of the manual reorganized and partially rewritten (with help from Zsófia Boda and Andras Vörös); instructions for siena01Gui() (which is still supported but without a positive advice) now are in the separate document siena01gui.pdf.
• Larger example for sienaCompositionChange.
• In siena08(), also Bonferroni combination of the two Fisher combinations now is reported.
• In phase2 of siena07(), rolled back change in truncation from version 1.1-227 to the earlier procedure.
• Function descriptives.sienaGOF() was added. This gives the numerical values of the statistics reported graphically in plot.sienaGOF().
• For ML estimation: added autocorrelations during phase 3 to summary.sienaFit().
• Minor changes of output in various functions, and in error message for includeEffects. In case results of siena07() are artificial, they are now reported not as variances equal to 999 but as NA.

September 17, 2013: new version (1.1-243) of RSiena and RSienaTest available from R-Forge.

A new version (1.1-243) of RSiena and RSienaTest can be obtained from the RSiena project at R-Forge.

The main changes of version 1.1-243 compared to 1.1-241 are:

• Improved treatment of structural values in sienaGOF for the case that structural values differ between waves. If your data include changing structural values, please use this new version for sienaGOF.
• Improved plotting of sienaGOF objects so that observed values outside of the range of simulated values don't run off the chart.
• New effects: anti isolates, anti in-isolates, anti in-near-isolates. These can be helpful in better modeling isolates and the number of indegrees equal to 0 or 1. (Outdegrees 0 could already be modeled by the outTrunc effect with parameter 1.) This may especially be helpful for improving goodness of fit for modeling bipartite networks.
• Correction to the Dolby option in siena07 for the case of more than 2 waves. The error did not lead to incorrect results, but to inferior convergence in the case of many waves (such as may occur in the multi-group option).
• Improved printing of results of siena07 in the case that it is used for simulation without estimation (option simOnly).

RSiena and RSienaTest are almost the same, except for function sienaBayes(), which is still experimental and undocumented, and included only in RSienaTest.

August, 2013: Special issue of Journal of Research on Adolescence on Network and Behavior Dynamics in Adolescence.

A special issue of Journal of Research on Adolescence was published about Network and Behavior Dynamics in Adolescence, almost all papers using RSiena. The editors are René Veenstra, Jan Kornelis Dijkstra, Christian Steglich, and Maarten H. W. Van Zalk.

August 24, 2013: new version (1.1-241) of RSiena and RSienaTest available from R-Forge.

A new version (1.1-241) of RSiena and RSienaTest can be obtained from the RSiena project at R-Forge.

The main changes of version 1.1-241 compared to 1.1-232 are:

• A bug, occurring sometimes for bipartite networks with only upward changes, was corrected.
• The errors that occurred when the covariance matrix was singular (because of perfect collinearity) now do not lead any more to an abort of siena07() but are trapped so the function continues to run and ends normally, with warning messages in the output.
• A few degree-related effects that were available already for one-mode networks now are also available for bipartite networks.
• For those who look at the values reported for the target statistics: the target statistic for the inPop effect was erroneously multiplied by n, and this is now redressed.
• A new effect inIsolatePop was added, but this still is not quite OK, so please leave it aside until the next revision.

RSiena and RSienaTest are almost the same, except for function sienaBayes(), which is still experimental and undocumented, and included only in RSienaTest.

June 15, 2013: new version (1.1-231) of RSiena and RSienaTest available from R-Forge

Bugs in sienaGOF repaired.

• There were some bugs in the auxiliary functions for sienaGOF(), affecting the results for data sets with bipartite networks, structural zeros or ones, and/or missing data. (For missing data, the results were affected only slightly.) These bugs were corrected.
• For data sets with composition change, Method of Moments estimation now is forced to be non-conditional.

May 14, 2013: new version (1.1-230) of RSienaTest

Error in MaxDegree solved; sienaGOF() still contains a bug for structural zeros.

Version 1.1-230 repairs an error in the operation of MaxDegree (set in sienaAlgorithmCreate / sienaModelCreate, used in siena07). This error existed for some time, and it is unknown for how long this was the case. Nynke Niezink repaired it (thank you!). Everybody who has used the MaxDegree option is advised to rerun the models that used this option, because the outcomes are likely to be wrong. We apologize...

April 2013: new version (1.1-227) of RSienaTest

Version 1.1-227 of RSienaTest has been committed to R-Forge.
Note that R also was updated a few weeks ago to R 3.0.0, so it makes sense to first update R and then RSienaTest.

A lot of functions and options that were up to now available in RSiena have been ported to RSienaTest.

• For the added effects see the new version of the manual, p. 174-175. (Some effects are totally new; also effects that were ported from RSienaTest to RSiena are mentioned.)

• sienaGOF() was changed and now has a basic functionality that does not require the use of other functions. Any dependent variable can be tested. There are out-of-the-box tests for IndegreeDistribution, OutdegreeDistribution, and BehaviorDistribution; and a network extractor function for the network/sna package. To achieve independence from other packages such as sna and igraph that do not belong to the core R distribution, some other functions are outside of RSiena/RSienaTest but given on the help page for "sienaGOF-auxiliary". A network extractor function for package igraph is given there, and auxiliary functions for testing the distribution of geodesic distances and the distribution of Burt's constraint measure. The former is suggested for general use, the latter only as an example and for specific use.

  There is now (as from May 2) a new version of the script sienaGOF_new.R that demonstrates the basic functionality of sienaGOF(). More is to follow.

• sienaTimeTest() was changed and now also contains effect-wise tests, groupwise tests (for group objects), automatic exclusion of collinear effects, and has improved print and summary methods.
• The function effectsDocumentation() now also can be used to give documentation for the currently used effects object, with shortName and other characteristics used to define the effects.
• A bug in the starting values for two-mode networks was corrected.
• A new option "Dolby" was added to sienaAlgorithmCreate() for variance reduction in siena07(). This will always (perhaps almost always) improve convergence. The default is Dolby=TRUE, which means that you don't have to do anything about it. Also a new option "diagonalize" was added, that is expected to improve convergence when the starting value for the parameter is relatively good already.
• Many help pages were improved: sometimes to make them better understandable or complete, sometimes to give more appropriate examples.
• The scripts Rscript02VariableFormat.R, Rscript03SienaRunModel.R, and Rscript04SienaBehaviour.R were updated. The same update was done for these scripts on the Siena website and in the manual. Because of the following item, this means these scripts are not downward compatible with older versions of RSiena and RSienaTest (just because these do not recognize the changed function names).
• To get function names that are a better indication of the function's purpose, sienaModelCreate() is now called sienaAlgorithmCreate(), but the earlier name is still retained as an alias; the class name of the object created by this function is now called sienaAlgorithm. For the same reason, sienaNet() is now called sienaDependent(), but here also the earlier name is still retained as an alias; the class name of the object created by this function is now sienaDependent.

RSiena: Siena in R

Appendix B of the RSiena manual contains a list of changes compared to earlier versions.

The RSiena users' manual is frequently updated. Likewise, the Siena scripts page is frequently updated with new or improved scripts. The front page of the manual and the header of the scripts report the date of publication.

Recently added features in RSiena are the possibility to estimate dynamics of multiple interdependent networks, which may be one-mode or two-mode networks; and the possibility to estimate models for networks with valued ties. See the Siena scripts page for examples.

For multiple networks, the elaboration and explanation are presented in:
Tom A.B. Snijders, Alessandro Lomi, and Vanina Jasmine Torló (2013). A model for the multiplex dynamics of two-mode and one-mode networks, with an application to employment preference, friendship, and advice. Social Networks, 35, 265-276.
DOI: http://dx.doi.org/10.1016/j.socnet.2012.05.005.
For two-mode networks, the article mentioned above gives some explanation, but a more specific explanation and example are given in:

• Guido Conaldi, Alessandro Lomi and Marco Tonellato (2012). Dynamic models of affiliation and the network structure of problem solving in an open source software project. Organizational Research Methods, 15, 385-412.
  DOI: http://dx.doi.org/10.1177/1094428111430541.
• Alessandro Lomi, Guido Conaldi, Marco Tonellato (2012). Organized Anarchies and the Network Dynamics of Decision Opportunities in an Open Source Software Project,
  p. 363-397 in Alessandro Lomi, J. Richard Harrison (ed.), The Garbage Can Model of Organizational Choice: Looking Forward at Forty, Research in the Sociology of Organizations, Volume 36, Emerald Group Publishing.
  DOI: http://dx.doi.org/10.1108/S0733-558X(2012)0000036017.

Some recent publications (many others are at the applications page)

• See the publications mentioned above.
• Jacob E. Cheadle, Michael Stevens, Deadric T. Williams, and Bridget J. Goosby (2013). The differential contributions of teen drinking homophily to new and existing friendships: An empirical assessment of assortative and proximity selection mechanisms. Social Science Research, 42, 1297-1310.
  DOI: http://dx.doi.org/10.1016/j.ssresearch.2013.05.001.

  Abstract
  Alcohol use is pervasive in adolescence. Though most research is concerned with how friends influence drinking, alcohol is also important for connecting teens to one another. Prior studies have not distinguished between new friendship creation, and existing friendship durability, however. We argue that accounting for distinctions in creation-durability processes is critical for understanding the selection mechanisms drawing drinkers into homophilous friendships, and the social integration that results. In order to address these issues, we applied stochastic actor based models of network dynamics to National Longitudinal Study of Adolescent Health data. Adolescents only modestly prefer new friendships with others who drinker similarly, but greatly prefer friends who indirectly connect them to homophilous drinkers. These indirect homophilous drinker relationships are shorter lived, however, and suggest that drinking is a social focus that connects adolescents via proximity, rather than assortativity. These findings suggest that drinking leads to more situational and superficial social integration.

  Note
  This paper differentiates between creation and maintenance (endowment effect; called durability here) of ties and between homophily at distances 1 and 2. For both, important results are found.

• Special issue Journal of Research on Adolescence, 23, Issue 3, pages 399-603:
  Network and Behavior Dynamics in Adolescence.

  Introduction:
  René Veenstra, Jan Kornelis Dijkstra, Christian Steglich and Maarten H. W. Van Zalk (2013). Network-Behavior Dynamics. Journal of Research on Adolescence, 23, 399-412.
  DOI: http://dx.doi.org/10.1111/jora.12070.

• Mark Lubell, John Scholz, Ramiro Berardo, and Garry Robins (2012). Testing Policy Theory with Statistical Models of Networks. Policy Studies Journal 40, 351-374.
  DOI: http://dx.doi.org/10.1111/j.1541-0072.2012.00457.x.
• Garry Robins, Jenny M. Lewis, and Peng Wang (2012). Statistical Network Analysis for Analyzing Policy Networks. Policy Studies Journal 40, 375-401.
  DOI: http://dx.doi.org/10.1111/j.1541-0072.2012.00458.x.
• Tom A.B. Snijders. (2011). Statistical Models for Social Networks. Annual Review of Sociology, 37, 129-151.
  DOI: http://dx.doi.org/10.1146/annurev.soc.012809.102709.
• Veenstra, R., and Steglich, C. (2012). Actor-based model for network and behavior dynamics: A tool to examine selection and influence processes. Chapter 34 (pp. 598-618) in B. Laursen, T. D. Little, and N. A. Card (Eds.), Handbook of developmental research methods. New York: Guilford Press.
• Social Networks has a special issue in two parts, issue 32.1 (2010) and 34.3 (2012), devoted to Network Dynamics. This carries a tutorial and several applications of Siena.
• Kevin Lewis, Marco Gonzalez, and Jason Kaufman (2012). Social selection and peer influence in an online social network. PNAS 109, 68-72.
  DOI: http://dx.doi.org/10.1073/pnas.1109739109.

  Abstract
  Disentangling the effects of selection and influence is one of social science's greatest unsolved puzzles: Do people befriend others who are similar to them, or do they become more similar to their friends over time? Recent advances in stochastic actor-based modeling, combined with self-reported data on a popular online social network site, allow us to address this question with a greater degree of precision than has heretofore been possible. Using data on the Facebook activity of a cohort of college students over 4 years, we find that students who share certain tastes in music and in movies, but not in books, are significantly likely to befriend one another. Meanwhile, we find little evidence for the diffusion of tastes among Facebook friends - except for tastes in classical/jazz music. These findings shed light on the mechanisms responsible for observed network homogeneity; provide a statistically rigorous assessment of the coevolution of cultural tastes and social relationships; and suggest important qualifications to our understanding of both homophily and contagion as generic social processes.
  DOI: http://dx.doi.org/10.1073/pnas.1109739109.

Back to the main Siena page