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rmsb (version 0.0.2)

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

1,348

Version

0.0.2

License

GPL (>= 3)

Maintainer

Frank Harrell Jr

Last Published

February 27th, 2021

Functions in rmsb (0.0.2)

PostF

Function Generator for Posterior Probabilities of Assertions
Quantile.blrm

blrm

Bayesian Binary and Ordinal Logistic Regression
as.data.frame.Ocens

Convert Ocens Object to Data Frame to Facilitate Subset
Ocens

Censored Ordinal Variable
Mean.blrm

blrmStats

Compute Indexes of Predictive Accuracy and Their Uncertainties
plot.PostF

Plot Posterior Density of PostF
stanGet

Get Stan Output
distSym

Distribution Symmetry Measure
predict.blrm

tauFetch

Fetch Partial Proportional Odds Parameters
print.blrm

Print blrm() Results
print.blrmStats

Print Details for blrmStats Predictive Accuracy Measures
print.rmsb

Basic Print for Bayesian Parameter Summary
print.predict.blrm

coef.rmsb

Extract Bayesian Summary of Coefficients
rmsb-package

The 'rmsb' package.
vcov.rmsb

Variance-Covariance Matrix
plot.rmsb

Plot Posterior Densities and Summaries
stanDx

Print Stan Diagnostics
stanDxplot

Diagnostic Trace Plots
getParamCoef

Get a Bayesian Parameter Vector Summary
pdensityContour

Bivariate Posterior Contour
HPDint

Highest Posterior Density Interval
ExProb.blrm

selectedQr

QR Decomposition Preserving Selected Columns
compareBmods

Compare Bayesian Model Fits
stackMI

Bayesian Model Fitting and Stacking for Multiple Imputation