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Overview of availability of recent versions of packages
The newest "official" version of RSiena (1.3.0) is available from
CRAN
(May 10, 2021).
The most recent version of RSiena is 1.3.9 and can be downloaded from the
downloads page
and also from
GitHub.
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 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 twomode 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
simulationbased estimation of stochastic actororiented 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 SAOMTERGM 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 SAOMTERGM
comparison of Leifeld & Cranmer.
Network Science, First View, 122.
DOI:
https://doi.org/10.1017/nws.2022.6
Abstract
We review the empirical comparison of Stochastic Actororiented 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 outofsample predictions using TERGMs are based on outofsample
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  tielevel 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 twomode networks
(provided the number of secondmode nodes is smaller than the
number of actors = firstmode 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 multigroup 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:




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 nonmissing 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 twomode 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 nonpositive".
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:




May 10, 2021:
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:




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 multigroup data set
(sienaGroupCreate).
Thus, when for 3 waves / 2 periods and MoM estimation you would use a sequence
r1010 (where r is 0 or 1) for a given pair (i, j),
for ML this would be transformed to the sequence rNA 1010.
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 RForge.
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 actororiented models for
the evolution of networks by generalized method of moments.
Journal de la Société Française de Statistique,
156, 140165.
 Amati, Viviana, Schönenberger, Felix, and Snijders, Tom A.B. (2019).
Contemporaneous Statistics for Estimation in Stochastic
ActorOriented CoEvolution Models .
Psychometrika, 84, 10681096.
DOI:
https://doi.org/10.1007/s11336019096763
A script demonstrating its use is
GMoMscript.R.
The software implementation is documented in GitHub at
Changes_RSiena_GMoM.pdf.
Effects:
 New effects: homXTransRecTrip, toU.
 sqrt versions for parameter 2 for the effects to, toBack, toRecip,
from, fromMutual.
 Effects to, toU, toBack, toRecip, MixedInXW are dyadic.
 Reinstated effect MixedInXW, also with sqrt version for parameter 2.
 Dropped effect to.2 (identical to "to")
and MixedInWX (identical to "toBack").
Further improvements of functionality:

dyadicCov made to accept also changing dyadic covariates.
 new arguments plotAboveThreshold and verbose for funnelPlot.
 effectsDocumentation now also includes gmm effects (at the bottom).
 Improved representation of some symbols in effect names
in LaTeX option of meta.table and siena.table.
 Display of deviations from targets changed to after subtraction of targets.
 Stop if no parameters are estimated and simOnly is FALSE.
Documentation:
 Extended description of GMoM in the manual.
 Description of toBack and toRecip in manual.
Reduction of functionality:
 Vignette basicRSiena.Rmd dropped from the package (available at
scripts page of website: see below).
Corrections / safeguards:
 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:




December 10, 2020:
 Version 1.229 of
RSiena available at GitHub.
This completes the transition of the RSiena package to GitHub.
The repository at RForge now is relevant only for RSienaTest.
Version 1.229 is a followup both of version 1.227 (RForge)
and 1.228 (GitHub).
Main changes from versions 1.227 and 1.228:
 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.
 New functions meta.table (for results of siena08 ) and funnelPlot
(for lists of 'parallel' sienaFit objects).
 New parameter tested in sienaGOF (to potentially exclude some
effects with scoretype tests from the approximations
for improvement of fit).

Manual and its LaTeX code added to GitHub, see \docs\manual.




November 10, 2020:




October 1, 2020:
 Version 1.228 of
RSiena available at GitHub.
Main changes:

Constraint that two networks are disjoint
operates correctly also when one of the networks is symmetric
and the other is not.

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.

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 1.226, and transition to version 1.227
(see below) will take place soon.
At GitHub, there is a
wiki
which is still in a phase of development.




September 16, 2020:
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.
Main changes in RSiena :
 Vignette basicRSiena added (was earlier available as a script);
thanks to James Hollway.
Main changes in RSienaTest :
 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:
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 (egoalter 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:
Main changes in RSiena and RSienaTest visible to users are the
following.
 New auxiliary function dyadicCov for sienaGOF.
 Correction of error in modelType when onemode as well as twomode networks are used.
 Added startingDate in siena08 and sienaBayes to the object produced.




January 12, 2020:
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:
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.
Main changes in RSienaTest:
 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 datadriven 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 ActorOriented Models.
Methodological Innovations, 12, 2059799119884282.
DOI:
https://doi.org/10.1177/2059799119884282.
Abstract
We propose a Mahalanobis distancebased Monte Carlo goodness of
fit testing procedure for the family of stochastic actororiented
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 SAOMTERGM 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 outofsample predictions using
TERGMs are based on outofsample 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:
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 nondirected 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.
Main changes in RSienaTest:
 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 coauthors
explaining the extension of the Stochastic Actororiented 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 actororiented model is a statistical approach to study
networkattribute 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 actororiented 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 selfesteem 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/fiveparameter model for effects of actor covariates on networks,
which earlier was at the ArXiV.
Tom A.B. Snijders and Alessandro Lomi (2019).
Beyond Homophily:
Incorporating Actor Variables in Statistical Network Models.
Network Science, 7, 119.
Abstract
We consider the specification of effects of numerical actor attributes
in statistical models for directed social networks.
A fundamental mechanism is homophily or assortativity, where
actors have a higher likelihood to be tied with others having similar
values of the variable under study. But there are other mechanisms that
may also play a role in how the attribute values of two actors influence
the likelihood of a tie. We discuss three additional mechanisms:
aspiration to send ties to others having high values; conformity
in the sense of sending more ties to others whose values are close to what
may be considered the `social norm';
and sociability, where those having higher values will tend to send
more ties generally. These mechanisms may operate jointly,
and then their effects will be confounded.
We present a specification representing these effects simultaneously
by a fourparameter quadratic function of the values of sender and receiver.
Greater flexibility can be obtained by a fiveparameter extension.
We argue that empirical researchers often overlook the possibility
that homophily may be confounded with these other mechanisms,
and that for actor attributes that have important effects on directed networks,
these specifications may provide an improvement.
An illustration is given of the dependence of advice ties on
academic grades in a network of MBA students,
analyzed by the Stochastic Actororiented Model.
Associated with this paper is the
new script SelectionTables.r
which is available from the
scripts page.




February 26, 2019:
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 inisolate;
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 onesided 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.)
Main changes in RSienaTest:
 For sienaBayes:
 Allow arbitrary number of interactions.
 check that prevAns is a sienaFit object;
totally skip initial multigroup
estimation if prevAns is given with ((prevAns$n3 >= 500) & (!usePrevOnly)).
 simpleBayesTest and multipleBayesTest corrected; they were wrong for models
with userdefined interactions.
 glueBayes extended (to allow good use for models with fixed parameters)
and its check for prior rates relaxed (to allow priorRatesFromData=2 leading to slightly different prior rates.)
 New default prevBayes$nwarm >= 1 for newProposalFromPrev in sienaBayes.
 siena.table for sienaBayes results extended with betweengroups s.d.
 Argument verbose added to extract.posteriorMeans.




January 28, 2019:




December 27, 2018:
 A paper about coevolution 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 Actororiented Model:
jimi Adams and David Schaefer (2018).
Visualizing Stochastic Actorbased 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
actorbased model.
See the
script for executing this at the Siena scripts page.




December 6, 2018:
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.
Main changes in RSienaTest:
 For sienaBayes:
 the possibility now exists to specify prior variances for
nonvarying parameters, which can be meaningful especially for
effects of grouplevel variables;
 a new, hopefully better, initial value is used, defined as a
precision weighted mean of the multigroup MoM estimate and the prior mean;
 number of subphases for initial global parameter estimation by MoM
decreased to 2;
 prewarming phase introduced before the first improveMH.
 extract.sienaBayes corrected.




October 30, 2018:
There was an error in sienaGroupCreate for noncentered actor covariates.
This now is corrected in version 1.213.
Main changes in RSiena and RSienaTest visible to users are the
following.
 Correct error in sienaGroupCreate for noncentered 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.
Main changes in RSienaTest:
 For sienaBayes:

new parameters proposalFromPrev, which allows
taking proposal distributions from prevBayes object;

incidentalBasicRates resuscitated;

allow fixed rate parameters, with priorRatesFromData=1;

changed initial values of scale factors for proposal distributions;

new parameters target and usePrevOnly;

in case prevBayes is used, parameters nrunMHBatches, nSampVarying, nSampConst,
and nSampRates in the function call of sienaBayes supersede those
in the prevBayes object;

correct bug for threeeffect interactions;

copy parameters modelType, behModelType, MaxDegree, Offset, initML,
from parameter algo to the algorithms created within sienaBayes;

more extensive checking of smallest eigenvalue of covariance matrix;

for priorRatesFromData = 1 or 2, the resulting prior Covariance
matrix for priorKappa != 1
was incorrect; this was corrected;

reported timing changed to elapsed system time.




September, 2018: review of studies using actororiented models
for studying peer influence on antisocial behaviors.
Jelle J. Sijtsema and Siegwart M. Lindenberg (2018).
Peer influence in the development of adolescent antisocial behavior:
Advances from dynamic social network studies.
Developmental Review, in press.
DOI:
https://doi.org/10.1016/j.dr.2018.08.002
Abstract (second half only)
The current
article presents a review of recent empirical studies that have used dynamic social network
analyses to study peer influence effects for different forms of antisocial
behavior (i.e., aggression, delinquency, externalizing behavior, weapon carrying)
as these forms may be differently affected
by peer influence. Studies that lump different kinds of antisocial behavior together as 'externalizing
behavior' show mixed results with regard to peer influence. With regard to the development
of delinquency and weapon carrying, peer influences were observed in studies that
had six month to oneyear measurement intervals, but not in those with shorter intervals. With
regard to direct forms of aggression, peer influence was only observed in certain contexts and
depended on individual antisocial traits.
What is recommended for further advance in the field of
peer influence is to avoid container variables of antisocial behavior (such as 'externalizing behavior'),
to pay close attention to the role of status and belonging needs, and to focus more
strongly on a detailed examination of the sequential order of peer selection and influence processes
and their moderation by individual and contextual conditions.




Already on the web in 2016: practical recommendations for data collection
for longitudinal network studies in school settings.
Lars Leszczensky, Harald Beier, Hanno Kruse and Sebastian Pink (2016).
Collecting network panel data in schools: Practical guidance based on
the experiences of three german research projects.
http://www.mzes.unimannheim.de/publications/wp/wp166.pdf
Abstract
There is an increasing amount of literature on how to analyze
longitudinal data of complete social networks.
Guidance on how to collect such data in schools, however,
is both scant and desperately needed,
because longitudinal social network analysis has high data
requirements. Aiming to provide guidance for
future data collection of schoolbased networks, we share our experiences
gained in three different projects collecting longitudinal social
network data in different samples of German schools. This includes both
one largescale study that relied on a nationally representative sample
of schools and two smaller studies
that targeted more specific geographical areas.
We discuss key decisions researchers have to make before data
collection, the definition of school samples, and the selection of schools.
We further offer advice on how to improve school participation and students'
response, describe the fieldwork, and
make recommendations for projects aiming to collect longitudinal social network data.




May 31, 2018: metaanalysis of results of actororiented models.
Owen Gallupe,John McLevey, and Sarah Brown (2018).
Selection and Influence: A MetaAnalysis of the Association
Between Peer and Personal Offending.
Journal of Quantitative Criminology, in press.
DOI:
https://doi.org/10.1007/s109400189384y.
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
longstanding criminological debate.
The relatively recent development of stochastic actororiented models
(SAOMsalso 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 metaanalysis of
studies that use SAOMs to test for peer selection and influence effects.




May 13, 2018: the new official version 1.212 of RSiena
is available from
CRAN.
Except for a detail on a help page, it is identical to version 1.211.
Thanks to efforts by
Felix Schönenberger, it is now available on all R platforms, including Solaris.




May 6, 2018: new version 1.211 of RSiena and RSienaTest
available from the
downloads page
and
the RSiena project at RForge.
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 twomode 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
sienaGOFauxiliary.
 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 nonvarying 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
simulationbased procedure to evaluate statistical power of longitudinal
social network studies in which stochastic actororiented 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 1.210 of RSiena and RSienaTest
available from the
downloads page
and
the RSiena project at RForge.
Versions 1.29 and 1.210 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 nondirected networks, the initial model now contains
only basic rates and degree effect.
Changes in RSienaTest:
 new function extract.posteriorMeans for sienaBayes results.
 In sienaBayes, restrict check of maximum
estimated parameter value after initialization to nonfixed 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.
Tom A.B. Snijders and Alessandro Lomi (2018).
Beyond Homophily: Incorporating Actor Variables in Actororiented Network Models.
arXiv:1803.07172v1.
Web:
https://arxiv.org/abs/1803.07172.
The paper later was published, see entry for April 25, 2019.




February 24, 2018: first publication using sienaBayes.
Zsófia Boda (2018). Social Influence on Observed Race.
Sociological Science, 5, 2957.
DOI:
http://dx.doi.org/10.15195/v5.a3.
Abstract
This article introduces a novel theoretical approach for understanding racial fluidity,
emphasizing the social embeddedness of racial classifications.
We propose that social ties affect
racial perceptions through withingroup micromechanisms, resulting in
discrepancies between racial
selfidentifications and race as classified by others. We demonstrate this
empirically on data from 12
Hungarian high school classes with one minority group (the Roma) using
stochastic actororiented models for the analysis of social network panel data.
We find strong evidence for social influence:
individuals tend to accept their peers' judgement about another student's
racial category; opinions
of friends have a larger effect than those of nonfriends. Perceived social
position also matters: those
wellaccepted among majorityrace peers are likely to be classified as majority
students themselves.
We argue that similar analyses in other social contexts shall lead to a
better understanding of race
and interracial processes.




February 24, 2018:
error in distance2 effects.
The current versions of RSiena and RSienaTest at RForge have errors
for the distance2 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 1.24 of RSiena and RSienaTest
available from the
downloads page
and
the RSiena project at RForge.
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 1.23 of RSiena and
RSienaTest
The option include=FALSE does not work in functions setEffect
and includeEffects of RSiena and RSienaTest,
version 1.23. This has been repaired in RForge version 1.24. The version on
CRAN, however, still is 1.23 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 nondirected 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 rightclicking and then saving them on your computer;
do keep the names intact; Internet Explorer is lousy and tends to mess them up):
Suppose these two files are in your working directory, with the names they have here.
Then after having attached
RSiena or RSienaTest, in R give the command
source("correction_excludeEffects.r")
and this should do the trick.




September 8, 2017: new versions 1.23 of RSiena at CRAN;
new version 1.23 of RSienaTest is available from the
downloads page
and
the RSiena project at RForge.
For the moment, the versions of RSiena at CRAN and RForge are identical.
The creation of the new CRAN version should have made the package more stable.
The only change in functionality between versions 1.23 and 1.21 is that the
totAlt effect now has been made available also for nondirected networks.




September 4, 2017: new versions 1.21 of RSiena
and 1.22 of RSienaTest are available from the
downloads page
and
the RSiena project at RForge.
Short overview of main changes:
 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.
 You now need to specify
MaxDegree, modelType, behModelType and Offset in a different way
for sienaAlgorithmCreate, and algorithm objects have to be created anew!!
 There is a new function score.Test which allows more easily
getting results of score tests.
 The use of modelType and the recent addition behModelType
was corrected. For networkModelType = 3 (initiative and confirmation model
for nondirected networks), an offset is added to the confirmation model.
See the manual for further explanation.
 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 scoretype 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 nonnamed 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.
Changes in RSiena :
 New parameter OffSet in sienaAlgorithmCreate.
 Registration of native routines (requirement for CRAN).
Changes in RSienaTest:
 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 1.1307 of RSiena
and RSienaTest are available from the
downloads page
and
the RSiena project at RForge.
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 metaanalyses).
 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 1.1305.
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 likelihoodbased 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.
If data sets with such configurations are given to RSiena,
it will hang at the moment that it tries to create the chain for likelihood simulations.
To solve such issues, the following remedies may be applied.
 If you are using siena07, then convert the data to a multigroup 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 101 by NA1, and 10NA1 by NANA1.
An example script to make such replacements is
changeForbiddenChanges.R
This solution for siena07 is perfect because it does not change the data.
For sienaBayes it is less than perfect, but acceptable
if the fraction of such changes is small.
The solution for sienaBayes makes sense because, if the observations for dyad (i,j)
are a
structural zero for Wave 1 and a tie for Wave 2, then the tie must have
started some time before Wave 2; so the structural zero cannot have
persisted all the way from Wave 1 right up to Wave 2.
In version 1.1306 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 1.1302 of RSiena
and RSienaTest are available from the
downloads page
and
the RSiena project at RForge.
Main changes in RSiena and RSienaTest visible to users:
 New effects: sameXCycle4, homCovNetNet, contrastCovNetNet,
covNetNetIn,
homCovNetNetIn, contrastCovNetNetIn, inPopIntnX, inActIntnX, outPopIntnX,
outActIntnX.
 Changes permitting the 4cycles effects for larger and denser networks.
 Dropped cl.XWX effect from twomode  onemode coevolution
(did not belong).
 egoSqX is an ego effect.
 Added cycle4 for onemode 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 2mode networks is reported.




August 18, 2016: new versions 1.1296 of RSiena
and RSienaTest are available from the
downloads page
and
the RSiena project at RForge.
Note: for the Mac version of RSienaTest, use the version
at the downloads page
rather than at RForge.
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
sienaGOFauxiliary.
 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 1.1282, parameter priorRatesFromData
in sienaBayes has values 012,
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 1.1279, an effects object is no longer used
as argument for print01Report() (this was superfluous).
 Since version 1.1279, 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 (1.1306).
 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 vectorarray 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 (1.1306)
this warning will have disappeared.
 There is an error in sienaGOF() in the case that coevolution of
onemode and twomode networks
is analysed; the bug occurs only if the networks were ordered in sienaDataCreate
in such a way, that a twomode network comes before a onemode 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 onemode networks come first, followed by the twomode 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 nonconditional estimation
(set cond = FALSE in sienaAlgorithmCreate).
For past bugs, see below.




May 28, 2016: new versions 1.1294 of RSiena
and RSienaTest are available from the
downloads page
and
the RSiena project at RForge.
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 twomode dependent networks.
 Endowment and creation effects were added for
inAct, inActSqrt, outAct, outActSqrt.
Changes in RSiena:
 siena01Gui() and sienaDataCreateFromSession() dropped.
Changes in RSienaTest:
 Change in endowment effect estimation for avAlt effect.
 New effects
simmelian, simmelianAltX, avSimmelianAlt,
totSimmelianAlt.




February 22, 2016. Convergence issues; and AdSUM2016
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 (AdSUM2016)
held in Zürich February 1920, of which the slides are
available here.




February 4, 2016: new versions 1.1290 of RSiena
and 1.1291 of RSienaTest are available from
the RSiena project at RForge 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 onedimensional 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.




January 19, 2016. New book: Multilevel Network Analysis
for the Social Sciences
This book is announced here.
It is mostly about crosssectional network data.
In Chapter 2, The Multiple Flavours of Multilevel Issues for Networks
by Tom Snijders, there is a section about the
Stochastic ActorOriented Models for Multilevel Networks.
Note that almost at the same time, a special issue appeared of the journal
Social Networks about the same theme.




September 12, 2015. Specification of transitivity for actororiented models
For modeling transitivity in actororiented 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 gwesprelated effect that has
the similar role as transRecTrip
(cf. Per Block, 2015, "Reciprocity, transitivity, and the mysterious threecycle",
Social Networks, 40:163173) is the interaction between gwespFF and reciprocity,
and can be defined by includeInteraction().
Since version 1.1290, 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 (1.1289) of RSiena
and RSienaTest is available from
the RSiena project at RForge 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 onestep approximations to expected Mahalanobis
distances in sienaGOF() (using control variates for score function).
 Permit 3way 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 indegrees also for twomode 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 (nonpositive
estimated derivatives lead to repeating a prolonged phase 1)
omitted for effects with fixed parameters.
Changes in RSiena:
 New effects homXOutAct, FFDeg, BBDeg, RFDeg, diffXTransTrip
(ported from RSienaTest).
 sameXInPop and diffXInPop also added for twomode 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).
Changes 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 nonrate parameters.




July 21, 2015: new version (1.1288) of RSiena
and RSienaTest is available from
the RSiena project at RForge 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.
Changes in RSiena:
 Correction of error for twomode networks in sienaGOF().
Changes in RSienaTest:
 New effects homXOutAct, FFDeg, BBDeg,
RFDeg, diffXTransTrip.
 sameXInPop and diffXInPop also added for twomode 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 metaanalysis, 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:
Weihua (Edward) An (2015).
Multilevel meta network analysis with application to studying network
dynamics of network interventions. Social Networks, 43, 4856.
DOI:
http://dx.doi.org/10.1016/j.socnet.2015.03.006.
Abstract
This paper introduce new methods for multilevel meta network analysis.
The new methods can combine results from multiple network models,
assess the effects of predictors at network or higher levels
and account for both within and crossnetwork correlations
of the parameters in the network models.
They are demonstrated by applying them to
network dynamics of a smoking prevention intervention
implemented in 76 classes of six middle schools in China.
Remark: on p. 48 of this paper, it is stated that
'Previous meta network analysis, maybe except the Fisher's
method for combining independent pvalues (Snijders and Bosker, 2012;
Ripley et al., 2014), mostly assumes that the estimated parameters
for a particular variable in the network models are generated by a
common effect'.
This is incorrect.
In the method of Snijders and Baerveldt (2003) implemented in RSiena
function siena08(),
the model is a random effects model (following Cochran, 1954),
but without the assumption of a normal distribution.
The statement is correct that Fisher's method does not
make the assumption of a common effect.
The fixed effects metaanalysis model is available in RSiena
as the multigroup option.
In siena08() also a ML estimate is implemented under
normality; see the manual.
However, the REML method used in An's paper, and available in
mvmeta and metafor, is usually slightly better
than the ML method; again, see the manual.
Two papers about coevolution of two networks,
one positive and one negative:
J. Ashwin Rambaran, Jan Kornelis Dijkstra, Anke Munniksma,
and Antonius H.N. Cillessen (2015).
The development of adolescents' friendships and antipathies:
A longitudinal multivariate network test of balance theory.
Social Networks, 43, 162176.
DOI:
http://dx.doi.org/10.1016/j.socnet.2015.05.003.
Abstract
This paper examines the interplay between friendship (best friend)
and antipathy (dislike) relationships among adolescents.
Based on (structural) balance theory, it was expected that
friendships would be formedor maintained when two adolescents
disliked the same person (shared enemy hypothesis), that friends
would tend to agree on whom they disliked (friends' agreement hypothesis),
that adolescents would tend to dislike the friends of those
they disliked (reinforced animosity hypothesis), and
that they would become or stay friends with dislikes of
dislikes (enemy's enemy hypothesis). Support was found
for the first three hypotheses, and partially for the fourth.
Gijs Huitsing, Tom A.B. Snijders, Marijtje A.J. van Duijn,
and René Veenstra (2014).
Victims, bullies, and their defenders:
a longitudinal study of the coevolution of positive and negative networks.
Development and Psychopathology, 26, 645659.
DOI:
http://dx.doi.org/10.1017/S0954579414000297
Abstract
The (co)evolution of bullying/victimization
and defending was examined
within three elementary schools across three
time points within a year. Most bullies and defenders were in
the same grade as the victims, although a substantial number
of bullies and defenders were in other grades
(most often one grade higher).
In line with goalframing theory, multiplex network analyses
provided evidence for the social support hypothesis
(victims with the same bullies defended each other over time)
as well as the retaliation hypothesis (defenders run the risk
of becoming victimized by the bullies of the victims they defend).
In addition, the analysis revealed that bullies with the same
victims defended each other over time and that defenders of
bullies initiated harassment of those bullies' victims.
Coevolution of a onemode and a twomode network,
with differentiation between creation and maintenance of ties:
Christoph Stadtfeld, Daniele Mascia, Francesca Pallotti,
and Alessandro Lomi (2015).
Assimilation and differentiation: A multilevel perspective on
organizational and network change. Social Networks, 44, 363374.
DOI:
http://dx.doi.org/10.1016/j.socnet.2015.04.010.
Abstract
This paper analyses how concurrent multilevel
processes of (internal) organizational and (external) network
change affect one another over time.
New effects are presented that afford specification and identification
of two apparently conflicting microrelational mechanisms
that jointly affect decisions to modify the portfolio of
internal organizational activities. The first mechanism,
assimilation, makes network partners more similar by facilitating
the replication and diffusion of experience. The second mechanism,
functional differentiation, operates to maintain and amplify differences
between network partners by preventing or limiting internal
organizational change. Data are analyzed that were collected on a
regional community of hospital organizations connected by
collaborative patient transfer relations observed over a
period of seven years. It is found that processes of social
influence conveyed by network ties may lead both to
similarity and differences among connected organizations.
Network dynamics with actors grouped in pairs:
Christoph Stadtfeld and Alex (Sandy) Pentland (2015).
Partnership Ties Shape Friendship Networks: A Dynamic Social Network Study.
Social Forces 94, 453477.
DOI:
http://dx.doi.org/10.1093/sf/sov079.
Abstract
Partnership ties shape friendship networks through
different social forces. First, partnership ties
drive clustering in friendship networks:
individuals who are in a partnership tend to have
common friends and befriend other couples.
Second, partnership ties influence the level of homophily
in these emerging friendship clusters.
Partners tend to be similar in a number of attributes (homogamy).
If one partner selects friends based on preferences for homophily,
then the other partner may befriend the same person regardless
of whether they also have homophilic preferences.
Thus, two homophilic ties emerge based on a single partner's
preferences. This amplification of homophily can be observed
in many attributes (e.g., ethnicity, religion, age).
Gender homophily, however, may be deamplified,
as the gender of partners differs in heterosexual partnerships.
In our study, we follow dynamic friendship formation among
126 individuals and their cohabiting partners in a
universityrelated graduate housing community over
a period of nine months.




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 tratios for convergence,
is not a satisfactory measure of convergence of the RobbinsMonro 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 noncentered covariates this criterion may be inadequate; and
if models with noncentered 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 noncentered 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 (1.1286) of RSiena
and RSienaTest is available from
the RSiena project at RForge.
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 fourcycle 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 threepaths constituting the fourcycles could become too large.
This was repaired in version 1.1302.
By the way, numerical overflow also will be a limitation for applying RSiena to very large
networks for some other effects.
 For twomode networks there was an error in sienaGOF()
(semidiagonal of generated adjacency matrices set to 0); this was repaired
in RSiena version 1.1288 and in RSienaTest version 1.1286
of June 02, 2015.
 Siena and RSienaTest until and including version 1.1284
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 noncentered covariates with missings will be wrong.
 sienaBayes until and including version 1.1283
contained an error for sampling the constant parameters.
This was corrected in version 1.1284.
 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 1.1284.
 print01Report gave an error message for a sienaGroup object
where the component objects have constant dyadic covariates.
This was corrected in version 1.1284.
 Effects cl.XWX and cl.XWX2 were not implemented correctly in versions
RSiena and RSienaTest
up to and including version 1.1281.
In these versions they were identical to cl.XWX1.
A correction was made in version 1.1282.
(This error was not present in effects XWX and XWX2.)

The component se of sienaFit objects produced by
siena07(), which is there since version 1.1271,
was incorrect until and including version 1.1277: it contained the squared standard errors
instead of the standard errors themselves. This was fixed in
version 1.1278.
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 1518, 2014) are
available here.

The slides of the Advanced Siena user's workshop at Sunbelt 2014
are
available here.
Pages 1013 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, 13611387.
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 threecycle.
Social Networks, 40, 163173.
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 onesided friendships, I propose that thetendency towards
reciprocation of friendships within transitive groups is usually lower
than outside of transitive groups.
In a metaanalysis of two datasets including 29 friendship networks using
stochastic actororiented models, the interaction between reciprocity
and transitivity is analysed. Supporting the theoretical reasoning,
the interaction is consistently negative. Second, the tendency against
forming threecycles 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 threecycles is commonly seen as an indicator that
unreciprocated friendships indicate local hierarchy differences between
individuals; this proposition has to be reevaluated 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 threecycles.

Per Block and Thomas Grund (2014). Multidimensional homophily in friendship networks.
Network Science, 2, 189212.
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 actororiented 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, 395415.
DOI:
http://dx.doi.org/10.1007/s1157701402718.
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 dependenciesemergent 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 fociphysical 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 actororiented 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, 126143.
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 (1.1284) of RSiena
and RSienaTest is available from
the RSiena project at RForge.
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 nonplotted 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 (1.1282) of RSiena
and RSienaTest is available from
the RSiena project at RForge.
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 nondirected networks)
(this permits interactions).
 egoX added as effect for nondirected networks
(can be important for representing effects of grouplevel covariates
in multigroup 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 multigroup 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 012,
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 (1.1280) of RSienaTest
is available from
the RSiena project at RForge.
Version 1.1280 is almost the same as 1.1279.
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 (1.1279) of RSiena
is available from
the RSiena project at RForge,
and of RSienaTest from the
downloads page.
The main changes of version 1.1279 compared to 1.1278 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).
Changes in RSiena and RSienaTest:
 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 twomode networks were dropped.
 For noninvertible 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 sienaGOFauxiliary was corrected
(the earlier version dropped isolated nodes from simulated networks).
 In the help page for sienaGOFauxiliary.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 nonzero 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 userdefined 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).
 pvalues 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()).
Changes in RSienaTest:
 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 (1.1278) of RSiena and RSienaTest
is available from the
downloads page.
The main changes of version 1.1278 compared to 1.1276 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 datadependent 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 1.1276,
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 wikitype of interactive website that was mentioned here previously
has been discontinued because it was too slow.
Please use the regular
SienaStocnet discussion list for interaction about sienaBayes.




June 3, 2014: new version (1.1276) of RSiena and RSienaTest
is available from
the
downloads page.
The main changes of version 1.1276 compared to 1.1274 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 actororiented model developed
by Snijders, which is modified so that the adoption times follow a
proportional hazards model. The asymptotic behaviour of the
methodofmoments 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 coevolution 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 timetoevent 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
John M. Light, Charlotte C. Greenan, Julie C. Rushby, Kimberly M. Nies, and
Tom A.B. Snijders (2013).
Onset to First Alcohol Use in Early Adolescence: A Network Diffusion Model.
Journal of Research on Adolescence, 23, 487499.
DOI:
http://dx.doi.org/10.1111/jora.12064.




April 26, 2014: new version (1.1274) of RSiena and RSienaTest
is available from RForge.
A new version (1.1274) of RSiena and RSienaTest can
be obtained from
the RSiena project at RForge.
A version for Mac is available from the
downloads page.
The main changes of version 1.1274 compared to 1.1254 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 actororiented models.
Network Science, Vol. 1, Issue 3, 278304.
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 twomode networks
canceled.
 Added component se (standard errors) to sienaFit objects.




February 17, 2014: new version (1.1254) of RSiena and RSienaTest
is available from RForge.
A new version (1.1254) of RSiena and RSienaTest can
be obtained from
the RSiena project at RForge.
The main changes of version 1.1254 compared to 1.1250 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(1exp(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 sienaGOFauxiliary, geodesic distances
were changed to nondirected. 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 nondirected onemode networks
(for directed onemode networks this is sharedPop).
 Effects outRateLog and outTrunc2 were ported
to RSiena from RSienaTest.
 Effect jumpXTransTrip extended to nondirected networks.
 gwesp.. effects modified (see above) and extended to
nondirected networks.
 Manual: added paragraph about how to import results from
xtable() and siena.table() into MSWord.
 Userdefined 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 userdefined interactions for networks
already in version 1.1250.)
 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 sienaGOFauxiliary, geodesic distances
changed to nondirected; 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 (1.1250) of RSiena and RSienaTest
available from RForge.
A new version (1.1250) of RSiena and RSienaTest can be obtained from
the RSiena project at RForge.
The main changes of version 1.1250 compared to 1.1246 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 1.1246.
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 Waldtype test after estimation by siena07().
 Userdefined 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 (1.1246) of RSiena and RSienaTest
available from RForge.
A new version (1.1246) of RSiena and RSienaTest can be obtained from
the RSiena project at RForge.
The main changes of version 1.1246 compared to 1.1245 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 (1.1245) of RSiena and RSienaTest
available from RForge.
A new version (1.1245) of RSiena and RSienaTest can be obtained from
the RSiena project at RForge.
The main changes of version 1.1245 compared to 1.1243 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 1.1227
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 (1.1243) of RSiena and RSienaTest
available from RForge.
A new version (1.1243) of RSiena and RSienaTest can be obtained from
the RSiena project at RForge.
The main changes of version 1.1243 compared to 1.1241 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 inisolates, anti innearisolates.
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 multigroup 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 (1.1241) of RSiena and RSienaTest
available from RForge.
A new version (1.1241) of RSiena and RSienaTest can be obtained from
the RSiena project at RForge.
The main changes of version 1.1241 compared to 1.1232 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 degreerelated effects that were available already for onemode
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 (1.1231) of RSiena and RSienaTest available from
RForge
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 nonconditional.




May 14, 2013: new version (1.1230) of RSienaTest
Error in MaxDegree solved; sienaGOF() still contains a bug for structural zeros.
Version 1.1230 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 (1.1227) of RSienaTest
Version 1.1227 of RSienaTest has been committed to
RForge.
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. 174175.
(Some effects are totally new; also effects that were
ported from RSienaTest to RSiena are mentioned.)
The main further changes are:




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 onemode or twomode 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 twomode and onemode networks,
with an application to employment preference, friendship, and advice.
Social Networks, 35, 265276.
DOI: http://dx.doi.org/10.1016/j.socnet.2012.05.005.
For twomode 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, 385412.
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. 363397 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/S0733558X(2012)0000036017.
Important new options in RSiena since April 2010
are the sienaGOF and sienaTimeTest functions contributed by Josh Lospinoso,
respectively for goodness of fit checking and
for testing time homogeneity for data with 3 or more waves.
See the manual for further explanation of these functions.




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, 12971310.
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 creationdurability 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 399603:
Network and Behavior Dynamics in Adolescence.
Introduction:
René Veenstra, Jan Kornelis Dijkstra, Christian Steglich
and Maarten H. W. Van Zalk (2013).
NetworkBehavior Dynamics.
Journal of Research on Adolescence, 23, 399412.
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, 351374.
DOI:
http://dx.doi.org/10.1111/j.15410072.2012.00457.x.
 Garry Robins, Jenny M. Lewis, and Peng Wang (2012).
Statistical Network Analysis for Analyzing Policy Networks.
Policy Studies Journal 40, 375401.
DOI:
http://dx.doi.org/10.1111/j.15410072.2012.00458.x.
 Tom A.B. Snijders. (2011).
Statistical Models for Social Networks.
Annual Review of Sociology, 37, 129151.
DOI:
http://dx.doi.org/10.1146/annurev.soc.012809.102709.
 Veenstra, R., and Steglich, C. (2012).
Actorbased model for network and behavior dynamics:
A tool to examine selection and influence processes.
Chapter 34 (pp. 598618) 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, 6872.
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 actorbased modeling,
combined with selfreported 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.
Further publications are listed at the
webpage with literature
and the
webpage with further applications.









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