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Boom (version 0.9.16)

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

4,100

Version

0.9.16

License

LGPL-2.1 | file LICENSE

Maintainer

Steven Scott

Last Published

September 2nd, 2025

Functions in Boom (0.9.16)

dmvn

Multivariate Normal Density
circles

Draw Circles
inverse-wishart

Inverse Wishart Distribution
lmgamma

Log Multivariate Gamma Function
regression.coefficient.conjugate.prior

Regression Coefficient Conjugate Prior
plot.density.contours

Contour plot of a bivariate density.
mvn.prior

Multivariate normal prior
match_data_frame

MatchDataFrame
is.even

Check whether a number is even or odd.
markov.prior

Prior for a Markov chain
replist

Repeated Lists of Objects
mvn.independent.sigma.prior

Independence prior for the MVN
gamma.prior

Gamma prior distribution
compare.den

Compare several density estimates.
plot.dynamic.distribution

Plots the pointwise evolution of a distribution over an index set.
scaled.matrix.normal.prior

Scaled Matrix-Normal Prior
normal.inverse.gamma.prior

Normal inverse gamma prior
log.integrated.gaussian.likelihood

Log Integrated Gaussian Likelihood
external.legend

Add an external legend to an array of plots.
uniform.prior

Uniform prior distribution
GenerateFactorData

Generate a data frame of all factor data
normal.inverse.wishart.prior

Normal inverse Wishart prior
invgamma

Inverse Gamma Distribution
histabunch

A Bunch of Histograms
wishart

Wishart Distribution
sd.prior

Prior for a standard deviation or variance
double.model

Prior distributions for a real valued scalar
rmvn

Multivariate Normal Simulation
mscan

Scan a Matrix
plot.macf

Plots individual autocorrelation functions for many-valued time series
mvn.diagonal.prior

diagonal MVN prior
plot.many.ts

Multiple time series plots
thin.matrix

Thin a Matrix
thin

Thin the rows of a matrix
rvectorfunction

RVectorFunction
TimeSeriesBoxplot

Time Series Boxplots
traceproduct

Trace of the Product of Two Matrices
lognormal.prior

Lognormal Prior Distribution
sufstat.Rd

Sufficient Statistics
normal.prior

Normal (scalar Gaussian) prior distribution
suggest.burn.log.likelihood

Suggest MCMC Burn-in from Log Likelihood
pairs.density

Pairs plot for posterior distributions.
boxplot.mcmc.matrix

Plot the distribution of a matrix
check

Check MCMC Output
ToString

Convert to Character String
MvnGivenSigmaMatrixPrior

Conditional Multivaraite Normal Prior Given Variance
beta.prior

Beta prior for a binomial proportion
check.data

Checking data formats
ar1.coefficient.prior

Normal prior for an AR1 coefficient
dirichlet-distribution

The Dirichlet Distribution
discrete-uniform-prior

Discrete prior distributions
diff.double.model

DiffDoubleModel
add.segments

Function to add horizontal line segments to an existing plot
boxplot.true

Compare Boxplots to True Values
dirichlet.prior

Dirichlet prior for a multinomial distribution
Boom-package

Boom
compare.many.densities

Compare several density estimates.
compare.vector.distribution

Boxplots to compare distributions of vectors
compare.many.ts

Compares several density estimates.
compare.dynamic.distributions

Compare Dynamic Distributions