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Statistical Software

Data Shaping Solutions offers software with source code and technical support to perform various statistical analyses. Our software is based on efficient, fast, robust proprietary algorithms that can easily handle large datasets. Thanks to creative statistical engineering, our software solutions work well even with non Gaussian data, outliers and ill-conditioned problems, where traditional packages would produce an unstable solution. The source code is well documented and easy to understand or modify.

Our programs are written in platform-independent languages available at no cost on any machine (Perl, C/C++, C#, SAS or Java). There is no licence fee. We currently offer the following packages:

  • Robust Multivariate Regression
    Works well with non Gaussian data or outliers. Allows you to set up bounds on the regression parameters (similar to ridge regression). Does not use matrix inversion, thus numerically stable. Robust parameter estimation based on Monte-Carlo simulations and re-sampling. The source code can easily be modified to perform logistic regression. This package can be used by scientists, programmers, analysts or engineers with limited statistical knowledge. Works on Unix, Linux or Windows. We will help you install the software on your machine, at no cost.

    Competitive advantages:
    • Offered with compact but simple and well documented source code (C, Perl or both).
    • Processes datasets with hundreds of variables.
    • Proprietary algorithm.
    • Performs robust regression. Numerically stable.
    • Performs thousands of regressions in a few seconds.

    Additional modules:

    • Model validation.
    • Confidence intervals and percentiles for regression parameters using bootstrapping.
    • Sensitivity analysis.
    • Using different error minimization criteria.
    • Confidence intervals for arbitrary combinations of regression parameters.

    The first two modules are included in the package.

    Example of possible use:
    To perform a multiple regression where each regression coefficient has the same sign as the correlation between the dependent and associated independent variable. Such regressions are more robust and more meaningful than traditional regressions: factors positively correlated with the response always get a positive coefficient.

    Price: Free. Click here to download.

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