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Boom (version 0.4)

Bayesian Object Oriented Modeling

Description

A C++ library for Bayesian modeling, with an emphasis on Markov chain Monte Carlo. Although boom contains a few R utilities (mainly plotting functions), its primary purpose is to install the BOOM C++ library on your system so that other packages can link against it.

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Version

Install

install.packages('Boom')

Monthly Downloads

2,253

Version

0.4

License

LGPL-2.1 | file LICENSE

Maintainer

Steven Scott

Last Published

May 21st, 2016

Functions in Boom (0.4)

beta.prior

Beta prior for a binomial proportion
markov.prior

Prior for a Markov chain
normal.prior

Normal (scalar Gaussian) prior distribution
check.data

Checking data formats
compare.many.densities

Compare several density estimates.
mvn.independent.sigma.prior

Independence prior for the MVN
mvn.diagonal.prior

diagonal MVN prior
uniform.prior

Uniform prior distribution
ar1.coefficient.prior

Normal prior for an AR1 coefficient
compare.den

Compare several density estimates.
lognormal.prior

Lognormal Prior Distribution
double.model

Prior distributions for a real valued scalar
add.segments

Function to add horizontal line segments to an existing plot
is.even

Check whether a number is even or odd.
compare.vector.distribution

Boxplots to compare distributions of vectors
thin

Thin the rows of a matrix
compare.many.ts

Compares several density estimates.
mvn.prior

Multivariate normal prior
sd.prior

Prior for a standard deviation or variance
gamma.prior

Gamma prior distribution
boxplot.mcmc.matrix

Plot the distribution of a matrix
dirichlet.prior

Dirichlet prior for a multinomial distribution
plot.many.ts

Multiple time series plots
discrete-uniform-prior

Discrete prior distributions
GenerateFactorData

Generate a data frame of all factor data
match_data_frame

MatchDataFrame
plot.density.contours

Contour plot of a bivariate density.
pairs.density

Pairs plot for posterior distributions.
boxplot.true

side-by-side boxplots from a matrix, with optional reference values
histabunch

A Bunch of Histograms
suggest.burn.log.likelihood

Suggest MCMC Burn-in from Log Likelihood
plot.macf

Plots individual autocorrelation functions for many-valued time series
plot.dynamic.distribution

Plots the pointwise evolution of a distribution over an index set.
normal.inverse.gamma.prior

Normal inverse gamma prior
external.legend

Add an external legend to an array of plots.
normal.inverse.wishart.prior

Normal inverse Wishart prior