Statistical Software Recommendations

  • Unfortunately learning time is directly proportional to power and flexibility of software
  • Full power and flexibility is only possible when you go past menus to learn the package's command language
  • Learning the command language allows one to practice reproducible research by being able to re-run entire analysis
  • Excel is not a statistical package, and it contains severe errors. It also is tedious to use, especially when doing repetitive calculations or graphs
  • In order of increasing power and learning time: SigmaStat -> SPSS -> SAS -> Stata -> S-Plus -> R
  • R and S-Plus are superior for exploratory data analysis, graphics, and statistical modeling
  • Stata is superior for statistical modeling and is good for graphics
  • R is free to all from; it uses virtually same commands as S-Plus but has limited menus. R runs faster, has slightly fewer bugs, has more add-on packages available, and is easy to install.
  • An R add-on package R Commander (Rcmdr) adds SPSS-like menus to R
  • There are many excellent online and published texts for learning R, and the Department of Biostatistics holds workshops
  • See StatCompCourse for more information and handouts

-- FrankHarrell - 26 Jun 2004
Topic revision: r2 - 07 Nov 2004, FrankHarrell

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