
SIENA version 4, also called
RSiena,
is a contributed package for the R statistical system,
see
https://cran.rproject.org/package=RSiena.
It can be installed in the regular way from CRAN,
but often a more recent version is obtainable as indicated
at the downloads page.
The incorporation of RSiena in R makes available all other
possibilities offered by R; in particular, to execute R
in a Mac, Unix/Linux, or Solaris environment.
Further information on RSiena is on the
RSiena page.
RSiena was the recipient of the 2017
INSNA William D Richards Award.
This is a
"lifetime achievement award" for
"publically available social network analysis software without which
it would be impossible to study social networks".
Known bugs and new papers are given at the
news webpage.
The SIENA program is part of an ongoing research effort.
The research team is composed of Tom Snijders,
Christian Steglich,
Johan Koskinen,
Nynke Niezink,
Viviana Amati,
Christoph Stadtfeld,
and Stepan Zaretckii,
with earlier contributions from
Ruth Ripley,
Krists Boitmanis, Felix Schönenberger, Charlotte Greenan,
Josh Lospinoso,
Paulina Preciado,
Robert Krause,
Michael Schweinberger,
Mark Huisman,
and Marijtje van Duijn.
Courses and a user group are mentioned at the
activities webpage.
The earlier SIENA 3 program also contains
methods for analyzing Exponential Random Graph Models (ERGMs).
For the latter methods, users now are referred to
the standalone program
pnet
or the R package
statnet,
although the old SIENA version 3
still is available for those who wish to use it.
It runs under Windows.
The
StOCNET interface still can be
downloaded, but is no longer maintained.
SIENA 3 can be executed from the
StOCNET environment,
or as a stand alone program.
The StOCNET project was an activity of
Christian Steglich, Tom Snijders, and
SciencePlus (Minne Oostra),
with important earlier contributions from Evelien Zeggelink, Peter Boer, Bert Straatman,
Mark Huisman, and others.


Scientific Summary
Social networks are dynamic by nature.
Network dynamics is important for domains ranging from friendship networks
(e.g., van Duijn et al., 2003; Burk et al., 2007; special issue of
Journal of Research
on Adolescence on
Network and Behaviour Dynamics in Adolescence) to, for example,
interorganisational networks (Borgatti and Foster, 2003; Berardo and Scholz, 2010).
Ties can be established and can be terminated; also there may be changes in the node set.
Changes in ties may be considered the result of the structural positions of the actors
within the network  e.g., when friends of friends become friends ,
characteristics of the actors ('actor covariates'), characteristics of pairs of actors
('dyadic covariates'), and residual random influences representing unexplained influences.
The study of network dynamics should shed light on the underlying theoretical
micromechanisms that induce the evolution of social network structures on the macrolevel.
Stochastic actorbased models for network dynamics (Snijders, 2017;
Snijders, van de Bunt, and Steglich, 2010) are a type of models that have the
purpose to represent network dynamics on the basis of observed longitudinal data,
and evaluate these according to the paradigm of statistical inference.
This means that the models represent network dynamics as being driven by many
different tendencies, such as the micromechanisms alluded to above, which may
have been theoretically derived and/or empirically established in earlier research,
and which may well operate simultaneously.
Some examples of such tendencies are reciprocity, transitivity
('friends of my friends are my friends'), homophily (choice of network ties based on
similarity of salient attributes), and assortative matching
(choice of network ties based on similarity of network position).
In this way, the models aim to give a good representation of the stochastic dependence
between the creation, and possibly termination, of different network ties.
These stochastic actorbased models allow to test hypotheses about these tendencies,
and to estimate parameters expressing their strengths,
while controlling for other tendencies (which in statistical terminology might be called 'confounders').
The actororiented nature means that changes in the network are modelled as representing
from choices by the actors who are represented by the nodes in the network.
This leads to a model combining agency and structure, and is well suited for
expressing theories based on purposeful behaviour by social actors.
With respect to data requirements, the focus is on the analysis of network panel data,
i.e., data where the dependent variables are constituted by a network, and possibly one
or more individual variables, that have been observed repeatedly for a given group of actors.
The number of repeated measurements varies in practice usually from 2 to 10, but may be larger.
In the basic model (Snijders, 2001) the changing network is the dependent variable.
Many important scientific questions, however, can be framed as questions about the
mutually dependent dynamics of networks and attributes (behaviour, attitudes, performance, etc.)
of the individual actors in the network.
This here is called coevolution of networks and behaviour, where the term 'behaviour'
also stands for performance, attitudes, etc.
The choice of network ties may depend on the attributes and network embeddedness
of the actors and of their possible candidates towards whom to extend a tie;
this is called social selection. Also, the behaviour of actors may depend on not only
their own attributes but also on their network position and on the behaviour and
other attributes of those to whom they are directly or indirectly tied in the network;
this is called social influence. Models for the coevolution of networks and
behaviour allow the joint representation of social selection and social influence,
as elaborated in Steglich, Snijders, and Pearson (2010).
In addition to models for onemode networks, also models for twomode networks
(sometimes called affiliation networks), and for multivariate networks are implemented
(Snijders, Lomi, and Torló, 2013).
The latter also are coevolution models. Thus it is possible to study,
for example, the coevolution of friendship and club membership.
Statistical methods for parameter estimation and hypothesis testing based on
these models have been implemented in the R package RSiena (Ripley, Snijders,
Boda, Vörös, and Preciado, 2017).
R is a general statistical software system (R Development Core Team, 2017),
and the incorporation of these methods in an R package allows combining them
with many other statistical methods.



Berardo, R., and Scholz, J.T. (2010), SelfOrganizing Policy Networks:
Risk, Partner Selection and Cooperation in Estuaries.
American Journal of Political Science, 54: 632649.

Borgatti, S.P., Foster, P.C., 2003. The network paradigm in organizational research:
a review and typology. Journal of Management, 29: 9911013.

Burk, W.J., Steglich, C.E.G., and Snijders, T.A.B. (2007)
Beyond dyadic interdependence: Actororiented models for coevolving
social networks and individual behaviors,
International Journal of Behavioral Development, 31: 397404.

Journal of Research on Adolescence, 23, Issue 3, pages 399603.
Special Issue: Network and Behavior Dynamics in Adolescence.

R Development Core Team (2022) R: A Language and Environment for Statistical Computing.
R Foundation for Statistical Computing, Vienna, Austria.
http://www.Rproject.org.

Ruth Ripley, Tom A.B. Snijders, Zsófia Boda, Andras Vörös,
and Paulina Preciado (2017).
Manual for SIENA version 4.0.
Oxford: University of Oxford, Department of Statistics, http://www.stats.ox.ac.uk/~snijders/siena/.

Tom A.B. Snijders (2001) The statistical evaluation of social network dynamics,
Sociological Methodology  2001, 40: 361395.
 Tom A.B. Snijders (2017).
Stochastic ActorOriented Models for Network Dynamics.
Annual Review of Statistics and Its Application, 4, 343363.
DOI:
http://dx.doi.org/10.1146/annurevstatistics060116054035
Here is the eprint access to this article.
 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.

Snijders, T.A.B., Steglich, C.E.G., and Schweinberger, M. (2007)
'Modeling the coevolution of networks and behavior', in
Kees van Montfort, Han Oud and Albert Satorra (eds),
Longitudinal Models in the Behavioral and Related Sciences.
Mahwah, NJ: Lawrence Erlbaum. pp. 4171.

Snijders, T.A.B., van de Bunt, G.G. and Steglich, C.E.G. (2010)
Introduction to stochastic actorbased models for network dynamics,
Social Networks, 32: 4460.

van Duijn, M.A.J., Zeggelink, E.P.H., Huisman, M., Stokman, F.N., and Wasseur, F.W. (2003)
Evolution of sociology freshmen into a friendship network,
Journal of Mathematical Sociology, 27: 153191.
