Description Details Author(s) References See Also Examples

Fits statistical models to longitudinal sets of networks, and to longitudinal sets of networks and behavioral variables. Not only one-mode networks but also two-mode networks and multivariate networks are allowed. The models are stochastic actor-oriented models.

Package `"RSienaTest"`

is the development version, and
is distributed through R-Forge, see
http://r-forge.r-project.org/R/?group_id=461.
Package `"RSiena"`

is the official release.

The main flow of operations of this package is as follows.

Data objects can be created from matrices and
vectors using `sienaDependent`

, `coCovar`

,
`varCovar`

, `coDyadCovar`

, etc.,
and finally `sienaDataCreate`

.

Effects are selected using an `sienaEffects`

object,
which can be created using `getEffects`

and may be further specified by `includeEffects`

,
`setEffect`

, and `includeInteraction`

.

Control of the estimation algorithm requires a
`sienaAlgorithm`

object that
defines the settings (parameters) of the algorithm,
and which can be created by `sienaAlgorithmCreate`

.

Function `siena07`

is used to fit a model.

A general introduction to the method is available in the tutorial paper Snijders, van de Bunt, and Steglich (2010). Next to the help pages, more detailed help is available in the manual (see below) and a lot of information is at the website (also see below).

Package: | RSiena |

Type: | Package |

Version: | 1.2-14 |

Date: | 2018-12-05 |

Depends: | R (>= 3.0.0) |

Imports: | Matrix |

Suggests: | tcltk, network, codetools, lattice, MASS, parallel, xtable, tools, utils |

SystemRequirements: | GNU make, tcl/tk 8.5, Tktable |

License: | GPL-2 |

LazyData: | yes |

NeedsCompilation: | yes |

BuildResaveData: | no |

Ruth Ripley, Krists Boitmanis, Tom Snijders, Felix Schoenenberger. Contributions by Josh Lospinoso, Charlotte Greenan, Christian Steglich, Johan Koskinen, Mark Ortmann, Nynke Niezink, Natalie Indlekofer, Christoph Stadtfeld, and Robert Hellpap.

Maintainer: Tom A.B. Snijders <[email protected]>

Schweinberger, Michael, and Snijders, Tom A.B. (2007). Markov models for digraph panel data: Monte Carlo-based derivative estimation.

*Computational Statistics and Data Analysis*51, 4465-4483.Snijders, Tom A.B. (2001). The statistical evaluation of social network dynamics.

*Sociological Methodology*, 31, 361-395.Snijders, Tom A.B. (2017). Stochastic Actor-Oriented Models for Network Dynamics.

*Annual Review of Statistics and Its Application*, 4, 343-363.Snijders, Tom A.B., van de Bunt, Gerhard G., and Steglich, Christian E.G. (2010). Introduction to actor-based models for network dynamics.

*Social Networks*, 32, 44-60.Snijders, Tom A.B., Steglich, Christian E.G., and Schweinberger, Michael (2007). Modeling the co-evolution of networks and behavior. Pp. 41-71 in

*Longitudinal models in the behavioral and related sciences*, edited by Kees van Montfort, Han Oud and Albert Satorra; Lawrence Erlbaum.Steglich, Christian E.G., Snijders, Tom A.B., and Pearson, Michael A. (2010). Dynamic networks and behavior: Separating selection from influence.

*Sociological Methodology*, 40, 329-393.The manual: http://www.stats.ox.ac.uk/~snijders/siena/RSiena_Manual.pdf

The website: http://www.stats.ox.ac.uk/~snijders/siena/.

1 2 3 4 5 6 7 8 | ```
mynet1 <- sienaDependent(array(c(tmp3, tmp4), dim=c(32, 32, 2)))
mydata <- sienaDataCreate(mynet1)
myeff <- getEffects(mydata)
myeff <- includeEffects(myeff, transTrip)
myeff
myalgorithm <- sienaAlgorithmCreate(nsub=3, n3=200)
ans <- siena07(myalgorithm, data=mydata, effects=myeff, batch=TRUE)
summary(ans)
``` |

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