Visual Numerics, a Rogue Wave Software Company, is making PyIMSL Studio 1.5 
available for download at no charge for non-commercial use or for commercial 
evaluation.

Learn more about PyIMSL Studio and download at:  
http://www.vni.com/campaigns/pyimslstudioeval

PyIMSL Studio contains both open source and proprietary components that create 
a fully supported and documented platform for analytic prototyping and 
production development.

- For prototyping, a number of open source tools including Python, NumPy, 
Eclipse, matplotlib and commercial components from Visual Numerics, Inc. are 
available for Python, including Python wrappers to the mathematics and 
statistics algorithms in the IMSL Numerical Library which are incorporated in 
the distribution. This combination of tools provides a rich environment for 
prototype development.

- For production deployment, commercial users of PyIMSL Studio also have access 
to the IMSL C Library to allow the development of native C implementations of 
algorithms for high performance production code. Using the IMSL C Library 
provides parity between prototype and production code.

The IMSL Numerical Libraries have been the cornerstone of high-performance and 
desktop computing as well as predictive analytics applications in science, 
technical and business environments for well over three decades. Functional 
areas include:

Mathematics
    * Matrix Operations
    * Linear Algebra
    * Eigensystems
    * Interpolation & Approximation
    * Numerical Quadrature
    * Differential Equations
    * Transforms
    * Nonlinear Equations
    * Optimization
    * Special Functions
    * Finance & Bond Calculations

Statistics
    * Basic Statistics
    * Time Series & Forecasting
    * Multivariate Analysis
    * Nonparametric Tests
    * Correlation & Covariance
    * Regression
    * Analysis of Variance and Designed Experiments
    * Categorical and Discrete Data Analysis
    * Survival and Reliability Analysis
    * Goodness of Fit
    * Distribution Functions
    * Random Number Generation
    * Neural Networks
    * Genetic Algorithm
    * Naïve Bayes


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