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BNSP (version 2.1.0)

Bayesian Non- And Semi-Parametric Model Fitting

Description

MCMC algorithms & processing functions for non- and semi-parametric models: 1. Dirichlet process mixtures & 2. spike-slab for multivariate (and univariate) response analysis, with nonparametric models for the means, the variances and the correlation matrix.

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Version

Install

install.packages('BNSP')

Monthly Downloads

429

Version

2.1.0

License

GPL (>= 2)

Maintainer

Georgios Papageorgiou

Last Published

May 24th, 2019

Functions in BNSP (2.1.0)

plot.mvrm

Creates plots of terms in the mean and/or variance models
simD

Simulated dataset
s

mgcv constructor s
sm

Smooth terms in mvrm formulae
ti

mgcv constructor ti
te

mgcv constructor te
plotCorr

Creates plots of the correlation matrices
summary.mvrm

Summary of an mvrm fit
predict.mvrm

Model predictions
dpmj

Dirichlet process mixtures of joint models
mvrm2mcmc

Convert posterior samples from function mvrm into an object of class `mcmc'
BNSP-package

Bayesian non- and semi-parametric model fitting
clustering

Computes the similarity matrix
print.mvrm

Prints an mvrm fit
continue

Continues the sampler from where it stopped
histCorr

Creates plots of correlation matrices
mvrm

Bayesian semiparametric analysis of multivariate continuous responses, with variable selection