Learn R Programming
⚠️There's a newer version (0.9.15) of this package.Take me there.

Boom (version 0.9.10)

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')

Monthly Downloads

6,793

Version

0.9.10

License

LGPL-2.1 | file LICENSE

Maintainer

Steven Scott

Last Published

May 27th, 2022

Functions in Boom (0.9.10)

dirichlet-distribution

The Dirichlet Distribution
inverse-wishart

Inverse Wishart Distribution
check

Check MCMC Output
compare.many.ts

Compares several density estimates.
compare.dynamic.distributions

Compare Dynamic Distributions
external.legend

Add an external legend to an array of plots.
compare.many.densities

Compare several density estimates.
histabunch

A Bunch of Histograms
double.model

Prior distributions for a real valued scalar
compare.den

Compare several density estimates.
plot.dynamic.distribution

Plots the pointwise evolution of a distribution over an index set.
mvn.independent.sigma.prior

Independence prior for the MVN
dirichlet.prior

Dirichlet prior for a multinomial distribution
diff.double.model

DiffDoubleModel
compare.vector.distribution

Boxplots to compare distributions of vectors
mvn.diagonal.prior

diagonal MVN prior
invgamma

Inverse Gamma Distribution
lmgamma

Log Multivariate Gamma Function
lognormal.prior

Lognormal Prior Distribution
markov.prior

Prior for a Markov chain
discrete-uniform-prior

Discrete prior distributions
dmvn

Multivariate Normal Density
normal.inverse.wishart.prior

Normal inverse Wishart prior
is.even

Check whether a number is even or odd.
pairs.density

Pairs plot for posterior distributions.
suggest.burn.log.likelihood

Suggest MCMC Burn-in from Log Likelihood
thin

Thin the rows of a matrix
plot.density.contours

Contour plot of a bivariate density.
scaled.matrix.normal.prior

Scaled Matrix-Normal Prior
rvectorfunction

RVectorFunction
wishart

Wishart Distribution
sd.prior

Prior for a standard deviation or variance
sufstat.Rd

Sufficient Statistics
plot.macf

Plots individual autocorrelation functions for many-valued time series
log.integrated.gaussian.likelihood

Log Integrated Gaussian Likelihood
mvn.prior

Multivariate normal prior
GenerateFactorData

Generate a data frame of all factor data
gamma.prior

Gamma prior distribution
normal.inverse.gamma.prior

Normal inverse gamma prior
mscan

Scan a Matrix
match_data_frame

MatchDataFrame
traceproduct

Trace of the Product of Two Matrices
rmvn

Multivariate Normal Simulation
replist

Repeated Lists of Objects
normal.prior

Normal (scalar Gaussian) prior distribution
regression.coefficient.conjugate.prior

Regression Coefficient Conjugate Prior
TimeSeriesBoxplot

Time Series Boxplots
thin.matrix

Thin a Matrix
uniform.prior

Uniform prior distribution
plot.many.ts

Multiple time series plots
add.segments

Function to add horizontal line segments to an existing plot
circles

Draw Circles
ar1.coefficient.prior

Normal prior for an AR1 coefficient
MvnGivenSigmaMatrixPrior

Conditional Multivaraite Normal Prior Given Variance
check.data

Checking data formats
beta.prior

Beta prior for a binomial proportion
boxplot.mcmc.matrix

Plot the distribution of a matrix
boxplot.true

Compare Boxplots to True Values
ToString

Convert to Character String