Harrell Miscellaneous

Contains many functions useful for data analysis, high-level graphics, utility operations, functions for computing sample size and power, simulation, importing and annotating datasets, imputing missing values, advanced table making, variable clustering, character string manipulation, conversion of R objects to Typst, LaTeX, and html code, recoding variables, caching, simplified parallel computing, encrypting and decrypting data using a safe workflow, general moving window statistical estimation, and assistance in interpreting principal component analysis.

Hmisc

Harrell Miscellaneous

Current Goals

• Continue to refine the summaryX class of functions that replace tables with graphics
  • See also bpplotM and tabulr
• See https://hbiostat.org/R/Hmisc/summaryFuns.pdf

Web Sites

• Overall: https://hbiostat.org/R/Hmisc
• CRAN: http://cran.r-project.org/web/packages/Hmisc
• Changelog: https://github.com/harrelfe/Hmisc/commits/master

To Do

• Consider using the haven package for importing SAS, Stata, and SPSS files; haven stores labels as the label attribute of each variable as does Hmisc; it converts date and time variables automatically and allows one to specify a format catalog along with the primary dataset
• See if the readstata13 package has advantages over the foreign package for Stata file import
• Consider creating xl.get using the readxl package to read .xls and .xlsx Excel files
• In impute.transcan, sense if a variable in data is not a factor whereas it was treated as a factor during aregImpute; it should be converted to factor before the line v[sub] <- ... levels(as.integer...)) is run