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BNSP (version 2.2.2)

Bayesian Non- And Semi-Parametric Model Fitting

Description

MCMC algorithms & processing functions for: 1. single response multiple regression, see Papageorgiou, G. (2018) , 2. multivariate response multiple regression, with nonparametric models for the means, the variances and the correlation matrix, with variable selection, see Papageorgiou, G. and Marshall, B. C. (2020) , 3. joint mean-covariance models for multivariate responses, see Papageorgiou, G. (2022) , and 4.Dirichlet process mixtures, see Papageorgiou, G. (2019) .

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Version

Install

install.packages('BNSP')

Monthly Downloads

429

Version

2.2.2

License

GPL (>= 2)

Maintainer

Georgios Papageorgiou

Last Published

March 26th, 2023

Functions in BNSP (2.2.2)

histCorr

Creates plots of correlation matrices
ami

Amitriptyline dataset from Johnson and Wichern
BNSP-package

Bayesian non- and semi-parametric model fitting
clustering

Computes the similarity matrix
dpmj

Dirichlet process mixtures of joint models
print.mvrm

Prints an mvrm fit
mvrm2mcmc

Convert posterior samples from function mvrm into an object of class `mcmc'
plotCorr

Creates plots of the correlation matrices
predict.mvrm

Model predictions
plot.mvrm

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

mgcv constructor s
simD

Simulated dataset
mvrm

Bayesian semiparametric analysis of multivariate continuous responses, with variable selection
lmrm

Bayesian semiparametric modelling of covariance matrices for multivariate longitudinal data
simD2

Simulated dataset
te

mgcv constructor te
ti

mgcv constructor ti
summary.mvrm

Summary of an mvrm fit
sinusoid

Sinusoid terms in mvrm formulae
sm

Smooth terms in mvrm formulae
continue

Continues the sampler from where it stopped
chol

The Cholesky and modified Cholesky decompositions