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) <https://www.jstor.org/stable/2347760>.

A function for multivariate outlier detection named Modified Stahel-Donoho (MSD) estimators is contained.  The function is for elliptically distributed datasets and recognizes outliers based on Mahalanobis distance. 
    The function is called the single core version in Wada & Tsubaki (2013) <doi:10.1109/CLOUDCOM-ASIA.2013.86> and evaluated with other methods in Wada, Kawano & Tsubaki (2020) <doi:10.17713/ajs.v49i2.872>.

A parallel function for multivariate outlier detection named modified Stahel-Donoho estimators is contained in this package.  The function RMSDp() is for elliptically distributed datasets and recognizes outliers based on Mahalanobis distance. This function is for higher dimensional datasets that cannot be handled by a single core function RMSD() included in 'RMSD' package.  See Wada and Tsubaki (2013) <doi:10.1109/CLOUDCOM-ASIA.2013.86> for the detail of the algorithm.

Display a randomly selected quote about Richard M. Stallman
 based on the collection in the 'GNU Octave' function 'fact()' which was
 aggregated by Jordi Gutiérrez Hermoso based on the (now defunct) site
 stallmanfacts.com (which is accessible only via <http://archive.org>).

Contains several useful navigation helper functions, including easily building
    folder paths, quick viewing dataframes in 'Excel', creating date vectors and changing the
    console prompt to reflect time.

Provides streamlined functions for summarising and visualising 
  regression models fitted with the 'rms' package, in the preferred format for medical journals. 
  The 'modelsummary_rms()' function produces concise summaries for linear, logistic, and 
  Cox regression models, including automatic handling of models containing restricted 
  cubic spline (RCS) terms. The resulting summary dataframe can be easily converted into 
  publication-ready documents using the 'flextable' and 'officer' packages. The 'ggrmsMD()' 
  function creates clear and customizable plots ('ggplot2' objects) to visualise RCS terms.