MCMCordfactanal
(Package: MCMCpack) :
Markov Chain Monte Carlo for Ordinal Data Factor Analysis Model
This function generates a sample from the posterior distribution of an ordinal data factor analysis model. Normal priors are assumed on the factor loadings and factor scores while improper uniform priors are assumed on the cutpoints. The user supplies data and parameters for the prior distributions, and a sample from the posterior distribution is returned as an mcmc object, which can be subsequently analyzed with functions provided in the coda package.
● Data Source:
CranContrib
● Keywords: models
● Alias: MCMCordfactanal
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plotState
(Package: MCMCpack) :
Changepoint State Plot
Plot the posterior probability that each time point is in each state.
● Data Source:
CranContrib
● Keywords: hplot
● Alias: plotState
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choicevar
(Package: MCMCpack) :
Handle Choice-Specific Covariates in Multinomial Choice Models
This function handles choice-specific covariates in multinomial choice models. See the example for an example of useage.
● Data Source:
CranContrib
● Keywords: manip
● Alias: choicevar
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NoncenHypergeom
(Package: MCMCpack) :
The Noncentral Hypergeometric Distribution
Evaluates the density at a single point or all points, and generate random draws from the Noncentral Hypergeometric distribution.
● Data Source:
CranContrib
● Keywords: distribution
● Alias: NoncenHypergeom, dnoncenhypergeom, rnoncenhypergeom
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MCpoissongamma
(Package: MCMCpack) :
Monte Carlo Simulation from a Poisson Likelihood with a Gamma Prior
This function generates a sample from the posterior distribution of a Poisson likelihood with a Gamma prior.
● Data Source:
CranContrib
● Keywords: models
● Alias: MCpoissongamma
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read.Scythe
(Package: MCMCpack) :
Read a Matrix from a File written by Scythe
This function reads a matrix from an ASCII file in the form produced by the Scythe Statistical Library. Scythe output files contain the number of rows and columns in the first row, followed by the data.
● Data Source:
CranContrib
● Keywords: file
● Alias: read.Scythe
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MCMChpoisson
(Package: MCMCpack) :
Markov Chain Monte Carlo for the Hierarchical Poisson Linear
MCMChpoisson generates a sample from the posterior distribution of a Hierarchical Poisson Linear Regression Model using the log link function and Algorithm 2 of Chib and Carlin (1999). This model uses a multivariate Normal prior for the fixed effects parameters, an Inverse-Wishart prior on the random effects variance matrix, and an Inverse-Gamma prior on the variance modelling over-dispersion. The user supplies data and priors, and a sample from the posterior distribution is returned as an mcmc object, which can be subsequently analyzed with functions provided in the coda package.
● Data Source:
CranContrib
● Keywords: MCMC, Poisson, bayesian, glmm, hierarchical models, mixed models, models
● Alias: MCMChpoisson
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InvWishart
(Package: MCMCpack) :
The Inverse Wishart Distribution
Density function and random generation from the Inverse Wishart distribution.
● Data Source:
CranContrib
● Keywords: distribution
● Alias: InvWishart, diwish, riwish
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MCMCSVDreg
(Package: MCMCpack) :
Markov Chain Monte Carlo for SVD Regression
This function generates a sample from the posterior distribution of a linear regression model with Gaussian errors in which the design matrix has been decomposed with singular value decomposition.The sampling is done via the Gibbs sampling algorithm. The user supplies data and priors, and a sample from the posterior distribution is returned as an mcmc object, which can be subsequently analyzed with functions provided in the coda package.
● Data Source:
CranContrib
● Keywords: models
● Alias: MCMCSVDreg
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MCMCoprobitChange
(Package: MCMCpack) :
Markov Chain Monte Carlo for Ordered Probit Changepoint Regression Model
This function generates a sample from the posterior distribution of an ordered probit regression model with multiple parameter breaks. The function uses the Markov chain Monte Carlo method of Chib (1998). The user supplies data and priors, and a sample from the posterior distribution is returned as an mcmc object, which can be subsequently analyzed with functions provided in the coda package.
● Data Source:
CranContrib
● Keywords: models
● Alias: MCMCoprobitChange
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