Bayesian Analysis of Item-Level Twin Data
R (>= 3.4.0), foreign, coda, matrixStats, rjags
Bayesian analysis of item-level hierarchical twin data using an integrated item response theory model. Analyses are based on Schwabe & van den Berg (2014) <doi:10.1007/s10519-014-9649-7>, Molenaar & Dolan (2014) <doi:10.1007/s10519-014-9647-9>, Schwabe, Jonker & van den Berg (2016) <doi:10.1007/s10519-015-9768-9> and Schwabe, Boomsma & van den Berg (2016) <doi:10.1016/j.lindif.2017.01.018>.

BayesValidate implements the software validation method
described in the paper "Validation of Software for Bayesian
Models using Posterior Quantiles" (Cook, Gelman, and Rubin,
2005).  It inputs a function to perform Bayesian inference as
well as functions to generate data from the Bayesian model
being fit, and repeatedly generates and analyzes data to check
that the Bayesian inference program works properly.

Bayesian Variable selection in Linear Models
Within the context of the lineal model, this package
provides tools for the analysis of the variable selection
problem from a Bayesian perspective. Special emphasis is placed
on providing simple and intuitive methods to explore and
synthesize the results. BayesVarSel allows the calculations to
be performed either exactly (sequential or paralel computation)
or heuristically, using a Gibbs sampling algorithm studied in
Garcia-Donato and Martinez-Beneito (2012). The default
implementation takes advantage of a closed-form expression for
the posterior probabilities that the prior proposed in Bayarri,
Berger, Forte and Garcia-Donato (2012) produces. Nevertheless,
other alternative priors, like Zellner (1986) g-prior,
Zellner-Siow (1980,1984) or Liang, Paulo, Molina, Clyde and
Berger (2008) can be used.

Within the context of the lineal model, this package
provides tools for the analysis of the variable selection
problem from a Bayesian perspective. Special emphasis is placed
on providing simple and intuitive methods to explore and
synthesize the results. BayesVarSel allows the calculations to
be performed either exactly (sequential or parallel
computation) or heuristically, using a Gibbs sampling algorithm
studied in Garcia-Donato and Martinez-Beneito (2012). The
default implementation takes advantage of a closed-form
expression for the posterior probabilities that the prior
proposed in Bayarri, Berger, Forte and Garcia-Donato (2012)
produces. Nevertheless, other alternative priors, like Zellner
(1986) g-prior, Zellner-Siow (1980,1984) or Liang, Paulo,
Molina, Clyde and Berger (2008) can be used.