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NIST Datasets

NIST Statistical Reference Datasets
(1037 downloads for this version - 11156 downloads for all versions)
A more recent valid version exists: 0.4
Michael Baudin
Owner Organization
Michael Baudin
Creation Date
January 13, 2012
Source created on
Scilab 5.3.x
Binaries available on
Scilab 5.3.x:
Windows 64-bit Windows 32-bit Linux 64-bit Linux 32-bit MacOSX
Install command
--> atomsInstall("nistdataset")

The goal of this toolbox is to provide a collection of datasets 
distributed by NIST.

The NIST Standard Reference Datasets is a collection of datasets. 
The purpose of this project is to improve the accuracy of statistical 
software by providing reference datasets with certified computational 
results that enable the objective evaluation of statistical software.

See the overview in the help provided with this toolbox.


The following is a list of functions in this toolbox.

 * nistdataset_getpath — Returns the path to the current module.
 * nistdataset_read — Reads a dataset from NIST
Moreover, the module provides 34 datasets from NIST in the following 
 * Univariate Summary Statistics (9 datasets)
 * Non Linear Least Squares (25 datasets)

Datasets from other categories are provided on the NIST 
website, which cannot be read by the current toolbox. 
However, it should be straightforward to extend the current 
toolbox to read the other categories of files.


 * Statistical Reference Datasets,
Files (2)
[79.31 kB]
Source code archive

[100.94 kB]
OS-independent binary for Scilab 5.3.x
Binary version
Automatically generated by the ATOMS compilation chain

News (0)
Comments (2)     Leave a comment 
Comment from Michael Baudin -- January 13, 2012, 09:15:35 PM    
Here is the changelog of the v0.3.

nistdataset (0.3)
    * Fixed ticket #626
	nistdataset_read had an accuracy problem.
	Thanks to Eric Dubois for reporting this bug.
	* Added modelFun, numberOfParameters, modelEq and 
	modelPar fields to the data structure.
	The modelFun function evaluates the model function.
	This can be used in regression problems.
	Thanks to Torbjørn Pettersen for providing the basic 
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