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rmsb (version 1.1-2)

Bayesian Regression Modeling Strategies

Description

A Bayesian companion to the 'rms' package, 'rmsb' provides Bayesian model fitting, post-fit estimation, and graphics. It implements Bayesian regression models whose fit objects can be processed by 'rms' functions such as 'contrast()', 'summary()', 'Predict()', 'nomogram()', and 'latex()'. The fitting function currently implemented in the package is 'blrm()' for Bayesian logistic binary and ordinal regression with optional clustering, censoring, and departures from the proportional odds assumption using the partial proportional odds model of Peterson and Harrell (1990) .

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Version

Install

install.packages('rmsb')

Monthly Downloads

1,112

Version

1.1-2

License

GPL (>= 3)

Maintainer

Frank Harrell Jr

Last Published

April 13th, 2025

Functions in rmsb (1.1-2)

Highest Posterior Density Interval

print.predict.blrm

Print Predictions for blrm()

Plot Posterior Density of PostF

pdensityContour

Bivariate Posterior Contour

Get a Bayesian Parameter Vector Summary

Distribution Symmetry Measure

The 'rmsb' package.

Bayesian Model Fitting and Stacking for Multiple Imputation

Print Stan Diagnostics

Print blrm() Results

print.blrmStats

Print Details for blrmStats Predictive Accuracy Measures

QR Decomposition Preserving Selected Columns

Variance-Covariance Matrix

Fetch Partial Proportional Odds Parameters

Basic Print for Bayesian Parameter Summary

Function Generator for Mean Y for blrm()

Function Generator for Posterior Probabilities of Assertions

Plot Posterior Densities and Summaries

Make predictions from a blrm() fit

Diagnostic Trace Plots

Get Stan Output

Extract Bayesian Summary of Coefficients

Function Generator for Exceedance Probabilities for blrm()

Compute Indexes of Predictive Accuracy and Their Uncertainties

Compare Bayesian Model Fits

Function Generator for Quantiles of Y for blrm()

Bayesian Binary and Ordinal Logistic Regression