Java™ Numerical Library
Advanced analytics and numerical software solutions for Java
The JMSL Numerical Library is the broadest collection of mathematical, statistical, financial, data mining, and charting classes available in 100 percent Java. It enables analytics to be easily and seamlessly embedded in applications, database, BI solutions. It is the only Java programming solution that combines integrated charting with the reliable mathematical and statistical functionality of the industry-leading IMSL Library algorithms. This blend of advanced numerical analysis and visualization on the Java platform allows organizations to gain insight into valuable data and share analysis results across the enterprise quickly.
Mathematical, statistical, and charting functionality in JMSL
The JMSL Library provides robust data analysis and visualization technology for the Java platform and a fast, scalable framework for tailored analytical applications. By leveraging its pre-built algorithms, users can save weeks or months of development effort by embedding JMSL Library functions rather than building new algorithms from scratch. The intuitive object-oriented programming interface allows Java developers to be immediately productive.
The JMSL Library provides a broad range of functionality, from basic algorithms such as linear algebra and regression to advanced neural network forecasting and other data mining, modeling, and prediction technologies. The neural network forecasting classes have tremendous potential for businesses by offering the ability to build predictive models using historical data and training the network to optimize the model over time as more information is obtained. This functionality can be applied to an unlimited set of applications, such as bioinformatics and life sciences, fraud detection, risk management and portfolio optimization, manufacturing yield analysis, and more.
|Data mining and forecasting||Statistics||Mathematics||Charting and finance|
|Decision Trees||Summary Statistics||Optimization||Heat Map and Tree Map Function|
|Regression||Time Series and Forecasting||Matrix Operations||Line, Pie, Scatter, Bar, and Box|
|Vector Auto-Regression||Nonparametric Tests||Linear Algebra||Polar, Area, Contour, and Histogram|
|Apriori Analysis||Analysis of Variance||Eigensystem Analysis||Fully Interactive Capabilities|
|Cluster Analysis||Generalized Linear Models||Interpolation and Approximation||High-Low-Close|
|Kohonen Self Organizing Maps||Goodness of Fit||Integration||Finance and Bond Calculations|
|Support Vector Machine||Distribution Functions||Differential Equations|
|Neural Networks||Random Number Generation||PDEs|
|Automatic ARIMA||Hypothesis Testing||Feynman-Kac Solver|
|ARCH, GARCH||Design of Experiments||Transforms|
|Genetic Algorithms||Statistical Process Control||Nonlinear Equations|
|NaÃ¯ve Bayes||Multivariate Analysis||Linear and Nonlinear Programming|
|Principal Components Analysis||Correlations and Covariance||Special Functions|
|Factor Analysis||Wilcoxon Rank Sum|
|Discriminant Analysis||Maximum Likelihood Estimation|