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Boom (version 0.9.17)

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')

Version

0.9.17

License

LGPL-2.1 | file LICENSE

Maintainer

Steven Scott

Last Published

September 16th, 2026

Functions in Boom (0.9.17)

compare.vector.distribution

Boxplots to compare distributions of vectors
dmvn

Multivariate Normal Density
double.model

Prior distributions for a real valued scalar
mvn.diagonal.prior

diagonal MVN prior
discrete-uniform-prior

Discrete prior distributions
mscan

Scan a Matrix
dirichlet.prior

Dirichlet prior for a multinomial distribution
inverse-wishart

Inverse Wishart Distribution
invgamma

Inverse Gamma Distribution
check.data

Checking data formats
lmgamma

Log Multivariate Gamma Function
is.even

Check whether a number is even or odd.
regression.coefficient.conjugate.prior

Regression Coefficient Conjugate Prior
replist

Repeated Lists of Objects
plot.many.ts

Multiple time series plots
plot.macf

Plots individual autocorrelation functions for many-valued time series
external.legend

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

Normal (scalar Gaussian) prior distribution
rmvn

Multivariate Normal Simulation
mvn.prior

Multivariate normal prior
mvn.independent.sigma.prior

Independence prior for the MVN
sufstat.Rd

Sufficient Statistics
scaled.matrix.normal.prior

Scaled Matrix-Normal Prior
suggest.burn.log.likelihood

Suggest MCMC Burn-in from Log Likelihood
rvectorfunction

RVectorFunction
unit.testing

Utilities for Unit Test Output
uniform.prior

Uniform prior distribution
gamma.prior

Gamma prior distribution
GenerateFactorData

Generate a data frame of all factor data
pairs.density

Pairs plot for posterior distributions.
log.integrated.gaussian.likelihood

Log Integrated Gaussian Likelihood
histabunch

A Bunch of Histograms
markov.prior

Prior for a Markov chain
match_data_frame

MatchDataFrame
plot.dynamic.distribution

Plots the pointwise evolution of a distribution over an index set.
lognormal.prior

Lognormal Prior Distribution
plot.density.contours

Contour plot of a bivariate density.
traceproduct

Trace of the Product of Two Matrices
TimeSeriesBoxplot

Time Series Boxplots
sd.prior

Prior for a standard deviation or variance
normal.inverse.wishart.prior

Normal inverse Wishart prior
thin.matrix

Thin a Matrix
normal.inverse.gamma.prior

Normal inverse gamma prior
wishart

Wishart Distribution
thin

Thin the rows of a matrix
boxplot.mcmc.matrix

Plot the distribution of a matrix
ToString

Convert to Character String
MvnGivenSigmaMatrixPrior

Conditional Multivaraite Normal Prior Given Variance
beta.prior

Beta prior for a binomial proportion
ar1.coefficient.prior

Normal prior for an AR1 coefficient
check

Check MCMC Output
circles

Draw Circles
diff.double.model

DiffDoubleModel
dirichlet-distribution

The Dirichlet Distribution
Intensity

Intensity function for a vector of event times
compare.many.ts

Compares several density estimates.
add.segments

Function to add horizontal line segments to an existing plot
compare.den

Compare several density estimates.
Boom-package

Boom
compare.dynamic.distributions

Compare Dynamic Distributions
boxplot.true

Compare Boxplots to True Values
DetectPosixct

Detect and convert date-time columns in a data frame
compare.many.densities

Compare several density estimates.