Rating R Helpers
John Sorkin wrote:
I believe we need to know the following about packages: (1) Does the package do what it purports to do, i.e. are the results valid? (2) Have the results generated by the package been validate against some other statistical package, or hand-worked example? (3) Are the methods used in the soundly based? (4) Does the package documentation refer to referred papers or textbooks? (5) In addition to the principle result, does the package return ancillary values that allow for proper interpretation of the main result, (e.g. lm gives estimates of the betas and their SEs, but also generates residuals)?. (6) Is the package easy to use, i.e. do the parameters used when invoking the package chosen so as to allow the package to be flexible? (7) Are the error messages produced by the package helpful? (8) Does the package conform to standards of R coding and good programming principles in general? (9) Does the package interact will with the larger R environment, e.g. does it have a plot method etc.? (10) Is the package well documented internally, i.e. is the code easy to follow, are the comments in the code adequate? (11) Is the package well documented externally, i.e. through man pages and perhaps other documentation (e.g. MASS and its associated textbook)?
Numbers 1 to 3 are critical. The rest would be very nice to know (and should be part of a rating system), but in the end are more likely to lead to frustration than outright errors ... (i.e., you'll find out soon enough if a package is poorly documented, then you just won't use it).
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