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LearnBayes (version 2.15.1)

Functions for Learning Bayesian Inference

Description

A collection of functions helpful in learning the basic tenets of Bayesian statistical inference. It contains functions for summarizing basic one and two parameter posterior distributions and predictive distributions. It contains MCMC algorithms for summarizing posterior distributions defined by the user. It also contains functions for regression models, hierarchical models, Bayesian tests, and illustrations of Gibbs sampling.

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Install

install.packages('LearnBayes')

Monthly Downloads

5,667

Version

2.15.1

License

GPL (>= 2)

Maintainer

Jim Albert

Last Published

March 18th, 2018

Functions in LearnBayes (2.15.1)

bayes.model.selection

Bayesian regression model selection using G priors

Log posterior of logit mean and log precision for Binomial/beta exchangeable model

Team records in the 1964 National League baseball season

Simulates from a probit binary response regression model using data augmentation and Gibbs sampling

Bermuda grass experiment data

blinregexpected

Simulates values of expected response for linear regression model

Log posterior of mean and precision for Binomial/beta exchangeable model

binomial.beta.mix

Computes the posterior for binomial sampling and a mixture of betas prior

Computation of posterior residual outlying probabilities for a linear regression model

Simulates values of predicted response for linear regression model

Logarithm of integral of Bayes factor for testing homogeneity of proportions

Selection of Beta Prior Given Knowledge of Two Quantiles

Bird measurements from British islands

Bayes factor against independence assuming alternatives close to independence

Simulates fitted probabilities for a probit regression model

School achievement data

cauchyerrorpost

Log posterior of median and log scale parameters for Cauchy sampling

bradley.terry.post

Log posterior of a Bradley Terry random effects model

Survival experience of women with breast cancer under treatment

bayes.influence

Observation sensitivity analysis in beta-binomial model

Bayes factor against independence using uniform priors

Chemotherapy treatment effects on ovarian cancer

calculus.grades

Calculus grades dataset

Darwin's data on plants

Highest probability interval for a discrete distribution

groupeddatapost

Log posterior of normal parameters when data is in grouped form

Probability density function for multivariate t

Birthweight regression study

discrete.bayes.2

Posterior distribution of two parameters with discrete priors

hearttransplants

Heart transplant mortality data

Donner survival study

Hitting data for Derek Jeter

Gibbs sampling for a hierarchical regression model

Simulation from Bayesian linear regression model

Summarization of a posterior density by the Laplace method

Log posterior with Poisson sampling and normal prior

Selection of Normal Prior Given Knowledge of Two Quantiles

Density function of a histogram distribution

Poll data from 2008 U.S. Presidential Election

Marathon running times

Log posterior density for mean and variance for normal sampling

Florida election data

cancermortality

Cancer mortality data

Independence Metropolis independence chain of a posterior distribution

Simulation from Bayesian normal sampling model

Posterior distribution for a proportion with discrete priors

careertraj.setup

Setup for Career Trajectory Application

Admissions data for an university

Gibbs sampling for a hierarchical regression model

Predictive distribution for a binomial sample with a discrete prior

Contour plot of a bivariate density function

Posterior distribution with discrete priors

mnormt.onesided

Bayesian test of one-sided hypothesis about a normal mean

normal.normal.mix

Computes the posterior for normal sampling and a mixture of normals prior

Random number generation for multivariate normal

The probability density function for the multivariate normal (Gaussian) probability distribution

Game outcomes and point spreads for American football

mnormt.twosided

Bayesian test of a two-sided hypothesis about a normal mean

Random number generation for multivariate t

Logarithm of bivariate normal density

Metropolis within Gibbs sampling algorithm of a posterior distribution

Random walk Metropolis algorithm of a posterior distribution

Log posterior of Poisson/gamma exchangeable model

Logarithm of Howard's dependent prior for two proportions

Log posterior of difference and sum of logits in a 2x2 table

Batting data for Mike Schmidt

Importance sampling using a t proposal density

poisson.gamma.mix

Computes the posterior for Poisson sampling and a mixture of gammas prior

Student dataset

Log posterior for a binary response model with a logistic link and a uniform prior

Log posterior of mean and log standard deviation for Normal/Normal exchangeable model

Rejecting sampling using a t proposal density

Log posterior of a Pareto model for survival data

Posterior predictive simulation from Bayesian normal sampling model

Log posterior with Poisson sampling and gamma prior

Sampling importance resampling

Plot of predictive distribution for binomial sampling with a beta prior

Random number generation for inverse gamma distribution

Predictive distribution for a binomial sample with a beta prior

prior.two.parameters

Construct discrete uniform prior for two parameters

Collapses a matrix by summing over rows

Hitting statistics for ten great baseball players

Bayesian test of a proportion

Goals scored by professional soccer team

Gibbs sampling for a robust regression model

reg.gprior.post

Computes the log posterior of a normal regression model with a g prior.

Simulates from a truncated probability distribution

Data from Stanford Heart Transplanation Program

Bird measurements from British islands

Baseball strikeout data

Random draws from a Dirichlet distribution

Plot of prior, likelihood and posterior for a proportion

Simulated draws from a bivariate density function on a grid

Log posterior of a Weibull proportional odds model for survival data