Statistical Software
NIST users may access a list of statistical software programs available at NIST for performing statistical analyses.
NIST datasets for testing non-linear regression routines are available as package NISTnls for the R language and programming environment for statistical modeling, data analysis, and graphics.
- The package itself is not a NIST product: it was created by Douglas Bates, a member of the core group of R Project Contributors
- R is not a NIST product either. R provides a rich assortment of tools for statistical and graphical analysis; it is highly extensible, and Open Source and free. It is widely regarded as the vehicle of choice for research in statistical methods.
The following is a list of SED developed software programs. These are freely downloadable for both NIST and non-NIST users.
- Dataplot - a graphical data analysis program.
- Recipe - a Fortran program for computing regression based tolerance limits.
- Extreme Winds - the extreme winds web site contains several custom Fortran programs and several MATLAB programs for analyzing extreme winds and the effect of winds on building structures.
The following software is available for download. However, it is not supported and is provided on an "as is" basis. There may not be any documentation available (other than that provided in the source code) for this software.
- Omnitab - the ancestor to MINITAB. Note that although we will provide this software on request, OMNITAB is no longer actively developed or supported.
- STSPAC - Fortran subroutines written by former SED staff member Charlie Reeve. In particular, these subroutines include functions for computing the cumulative distribution function and generating random numbers for the doubly non-central F and doubly non-central t distributions.
- DATAPAC - Fortran subroutines written by James Filliben. Most of these subroutines are incorporated into the Dataplot software listed above. DATAPAC has not been actively developed for some time. Of primary interest are some of the probability distribution functions.