[R] software comparison

[Philippe Grosjean]

Hello,

I just read this paper, and was surprised by:

"This study performs a comparison of the latest versions of nine 
different statistical software packages using StRD. These packages are 
SAS (9.1), SPSS (12.0), Excel (2003), Minitab (14.0), Stata (8.1), Splus 
(6.2), R (1.9.1), JMP (5.0), and StatCrunch (3.0)."

For a paper published in 2007, and submitted in April 2005, this is 
still surprising. If I my calculation is correct, in 2004, they would 
have used R 2.2.x, or something,... not 1.9.1?

Anyway, does someone know if there is a chance 2.5.0 provides some 
improvements in some of the "difficult cases" for R 1.9.1 (for instance, 
improvement of the algorithms for calculating autocorrelation, ANOVA, 
linear regression or non linear regression)?

Best,

Philippe Grosjean

..............................................<°}))><........
  ) ) ) ) )
( ( ( ( (    Prof. Philippe Grosjean
  ) ) ) ) )
( ( ( ( (    Numerical Ecology of Aquatic Systems
  ) ) ) ) )   Mons-Hainaut University, Belgium
( ( ( ( (
..............................................................

Robert McFadden wrote:
> Dear R Users,
> May be you are interested in an article that compares 9 statistical
> softwares (including R).
> Any comments are appreciate.  
> 
> Article:
> Keeling, Kellie B.; Pavur, Robert J."A comparative study of the reliability
> of nine statistical software packages" Computational Statistics and Data
> Analysis, Volume: 51, Issue: 8, 2007, pp. 3811-3831
> 
> 
> Abstract
> The reliabilities of nine software packages commonly used in performing
> statistical analysis are assessed and compared. The
> (American) National Institute of Standards and Technology (NIST) data sets
> are used to evaluate the performance of these software
> packages with regard to univariate summary statistics, one-way ANOVA, linear
> regression, and nonlinear regression. Previous
> research has examined various versions of these software packages using the
> NIST data sets, but typically with fewer software
> packages than used in this study. This study provides insight into a
> relative comparison of a wide variety of software packages
> including two free statistical software packages, basic and advanced
> statistical software packages, and the popular Excel package.
> Substantive improvements from previous software reliability assessments are
> noted. Plots of principal components of a measure of
> the correct number of significant digits reveal how these packages tend to
> cluster for ANOVA and nonlinear regression.
> 
> 
> Best,
> Rob
> 
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