Learn R Programming

Boom (version 0.9.15)

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.

Copy Link

Version

Install

install.packages('Boom')

Version

0.9.15

License

LGPL-2.1 | file LICENSE

Maintainer

Steven Scott

Last Published

February 3rd, 2024

Functions in Boom (0.9.15)

compare.many.ts

Compares several density estimates.
Boom-package

Boom
dirichlet-distribution

The Dirichlet Distribution
external.legend

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

Gamma prior distribution
mvn.independent.sigma.prior

Independence prior for the MVN
mvn.prior

Multivariate normal prior
MvnGivenSigmaMatrixPrior

Conditional Multivaraite Normal Prior Given Variance
compare.vector.distribution

Boxplots to compare distributions of vectors
ar1.coefficient.prior

Normal prior for an AR1 coefficient
inverse-wishart

Inverse Wishart Distribution
dirichlet.prior

Dirichlet prior for a multinomial distribution
regression.coefficient.conjugate.prior

Regression Coefficient Conjugate Prior
discrete-uniform-prior

Discrete prior distributions
invgamma

Inverse Gamma Distribution
replist

Repeated Lists of Objects
TimeSeriesBoxplot

Time Series Boxplots
is.even

Check whether a number is even or odd.
compare.dynamic.distributions

Compare Dynamic Distributions
lmgamma

Log Multivariate Gamma Function
compare.many.densities

Compare several density estimates.
beta.prior

Beta prior for a binomial proportion
double.model

Prior distributions for a real valued scalar
dmvn

Multivariate Normal Density
markov.prior

Prior for a Markov chain
traceproduct

Trace of the Product of Two Matrices
boxplot.mcmc.matrix

Plot the distribution of a matrix
match_data_frame

MatchDataFrame
boxplot.true

Compare Boxplots to True Values
normal.inverse.gamma.prior

Normal inverse gamma prior
log.integrated.gaussian.likelihood

Log Integrated Gaussian Likelihood
circles

Draw Circles
normal.inverse.wishart.prior

Normal inverse Wishart prior
lognormal.prior

Lognormal Prior Distribution
GenerateFactorData

Generate a data frame of all factor data
compare.den

Compare several density estimates.
histabunch

A Bunch of Histograms
normal.prior

Normal (scalar Gaussian) prior distribution
mscan

Scan a Matrix
rmvn

Multivariate Normal Simulation
rvectorfunction

RVectorFunction
mvn.diagonal.prior

diagonal MVN prior
suggest.burn.log.likelihood

Suggest MCMC Burn-in from Log Likelihood
sufstat.Rd

Sufficient Statistics
pairs.density

Pairs plot for posterior distributions.
scaled.matrix.normal.prior

Scaled Matrix-Normal Prior
plot.dynamic.distribution

Plots the pointwise evolution of a distribution over an index set.
plot.density.contours

Contour plot of a bivariate density.
sd.prior

Prior for a standard deviation or variance
plot.macf

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

Uniform prior distribution
plot.many.ts

Multiple time series plots
wishart

Wishart Distribution
thin

Thin the rows of a matrix
thin.matrix

Thin a Matrix
ToString

Convert to Character String
add.segments

Function to add horizontal line segments to an existing plot
check.data

Checking data formats
check

Check MCMC Output
diff.double.model

DiffDoubleModel