The IMSL Fortran Numerical Library is the standard for high performance computing commercial mathematics and statistics libraries, providing:
“It was much faster to use procedures from the imsl fortran library than create those same procedures on my own.”
Dr. Rasmus Lents,
University of Wisconsin
Professor, Department of Economics
The gold standard for over four decades
With over 1000 fully supported algorithms, the IMSL Fortran Numerical Library is the most comprehensive mathematics and statistics library available. Highly robust and reliable, it contains proven technology that has been thoroughly tested, well documented, continuously maintained, optimized on various platforms and used by developers worldwide for four decades. Instead of writing, testing and documenting complex mathematical and statistical algorithms from scratch or getting them from open source, developers use the algorithms provided in the IMSL Fortran Numerical Library to help accelerate application development and time to market.
The original, easy-to-use Fortran Library
The IMSL Fortran Numerical Library is the most convenient to use Fortran library available today. Features that make your developers more productive include:
- Convenient optional arguments: Allow users to utilize the fast, convenient optional arguments of modern Fortran syntax for all of the relevant algorithms in the library, saving significant coding time and helping to avoid errors
- Backward compatibility: Ensuring that legacy code can run on future releases
- Naming conventions: Function names resemble actual algorithm names, so users do not have to learn and remember special function names
- Over 1,000 algorithms: The most comprehensive math and statistics library available
- Broad platform support: Linux, Unix and Windows
|Data mining and
||Time series and forecasting
||Analysis of variance
||Generalized linear models
|Principal components analysis
||Goodness of fit
||Interpolation and approximation
||Random number generation
|Generalized categorical models
|Bayesian seasonal model
||Design of experiments
||Statistical process control
||Correlations and covariance
||Linear and nonlinear programming
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