JMSL Numerical Library

Installation

Documentation

Technical Support Contacts

Licenses

Important Notes

Use of JMSL requires a Java development environment, such as Sun's JDK 1.4. Applets built using JMSL are best run using the Java Plug-In, version 1.4.2 or later.

For Windows-based systems, the user may experience different results between OpenGL and Direct 3D (D3D) print output for JMSL 3D graphs. These differences may be related to differences in video hardware and drivers, as wells as versioning issues of OpenGL and D3D. The user should experiment with each method to determine the optimal method for printing 3D graphs. The default is to use OpenGL. To use D3D set the Java system property j3d.rend to d3d. This can be done on the command line using the option -Dj3d.rend=d3d.

Serialization of JMSL classes will not be compatible with future JMSL releases. The current serialization support is appropriate for short term storage or RMI between applications running the same version of JMSL.

This product includes software developed by the Apache Software Foundation (http://www.apache.org). This applies only to the Crimson XML Parser.

Classes and Methods New to the JMSL Numerical Library Version 4.0

com.imsl.math
Optimization
DenseLP Solves a linear programming problem using an active set strategy.
com.imsl.stat
Basic Statistics
EmpiricalQuantiles Computes empirical quantiles.
Regression
LinearRegression.CaseStatistics Allows for the computation of predicted values, confidence intervals, and diagnostics for detecting outliers and cases that greatly influence the fitted regression.
Probability Distribution Functions and Inverses
Cdf.logNormal Evaluates the standard lognormal cumulative probability distribution function.
Cdf.inverseLogNormal Returns the inverse of the standard lognormal cumulative probability distribution function.
Cdf.logNormalProb Evaluates the standard lognormal probability density function.
Cdf.FProb Evaluates the F probability density function.
cdf.Weibull Evaluates the Weibull cumulative probability distribution function.
Cdf.inverseWeibull Returns the inverse of the Weibull cumulative probability distribution function.
Cdf.WeibullProb Evaluates the Weibull probablity density function.
Cdf.Rayleigh Evaluates the Rayleigh cumulative probability distribution function.
Cdf.inverseRayleigh Returns the inverse of the Rayleigh cumulative probability distribution function.
Cdf.RayleighProb Evaluates the Rayleigh probablity density function.
Cdf.extremeValue Evaluates the extreme value cumulative probability distribution function.
Cdf.inverseExtremeValue Returns the inverse of the extreme value cumulative probability distribution function.
Cdf.extremeValueProb Evaluates the extreme value probability density function.
Cdf.betaProb Evaluates the beta probablity density function.
Cdf.gammaProb Evaluates the gamma probability density function.
Cdf.exponential Evaluates the exponential cumulative probability distribution function.
Cdf.inverseExponential Evaluates the inverse of the exponential cumulative probability distribution function.
Cdf.exponentialProb Evaluates the exponential probability density function.
Cdf.geometric Evaluates the discrete geometric cumulative probability distribution function.
Cdf.geometricProb Evaluates the discrete geometric probability density function.
Cdf.inverseGeometric Returns the inverse of the discrete geometric cumulative probability distribution function.
Cdf.chiProb Evaluates the chi-squared probablity density function.
Cdf.noncentralchi Evaluates the noncentral chi-squared cumulative probability distribution function.
Cdf.inverseNoncentralchi Evaluates the inverse of the noncentral chi-squared cumulative probability distribution function.
Cdf.noncentralstudentsT Evaluates the noncentral Student's t cumulative probability distribution function.
Cdf.inverseNoncentralstudentsT Evaluates the inverse of the noncentral Student's t cumulative probability distribution function.
Cdf.bivariateNormal Evaluates the bivariate normal cumulative probability distribution function.
Cdf.discreteUniform Evaluates the discrete uniform cumulative probability distribution function.
Cdf.inverseDiscreteUniform Returns the inverse of the discrete uniform cumulative probability distribution function.
Cdf.discreteUniformProb Evaluates the discrete uniform probability density function.
Cdf.uniform Evaluates the uniform cumulative probability distribution function.
Cdf.inverseUniform Returns the inverse of the uniform cumulative probability distribution function.
Random Number Generation
Random.nextExtremeValue Generate a pseudorandom number from an extreme value distribution.
Random.nextF Generate a pseudorandom number from the F distribution.
Random.nextRayleigh Generate a pseudorandom number from a Rayleigh distribution.
MersenneTwister Initializes the 32-bit Mersenne Twister generator.
MersenneTwister64 Initializes the 64-bit Mersenne Twister generator.
com.imsl.io
Input/Output
MPSReader Reads a linear programming problem from an MPS file.
com.imsl.chart
Chart 2d
Dendrogram A dendrogram chart for cluster analysis.
com.imsl.chart3d
Chart 3d
AmbientLight An ambient light.
Axis3D An x-axis, y-axis or a z-axis.
AxisBox Box behind the axis.
AxisLabel The labels on an axis.
AxisLine The axis line.
AxisTitle Axis title.
AxisXYZ The axis for an x-y-z chart.
Background Background of the chart.
BufferedPaint A paint object cached into an image image on the canvas.
Canvas3DChart A canvas for rendering a 3d chart.
Chart3D Root node of a 3d chart tree.
ChartLights Default set of lights.
ChartNode3D The base class of all of the nodes in the 3D chart tree.
ColormapLegend Adds a legend for a Colormap gradient to the background of the canvas.
Data Draws a 3D data node.
DirectionalLight A directional light.
JFrameChart3D JFrameChart3D is a JFrame that contains a chart.
MajorTick The major tick marks.
PointLight A point light source.
Surface Surface from a function or from a set of scattered data points.
com.imsl.datamining.neural
Neural Nets
BinaryClassification Classifies patterns into two classes.
MultiClassification Classifies patterns into three or more classes.
FeedForwardNetwork.setEqualWeights Initialize network weights using equal weighting.
FeedForwardNetwork.setRandomWeights Initialize network weights using random weighting.

Changes in JMSL Numerical Library Version 4.0

com.imsl.math
Tranforms
FFT Corrected the equations in the documentation.
Optimization
MinUnconMultiVar Add code to prevent a divide by zero condition.
com.imsl.stat
Basic Statistics
Anova.getP Corrected the problem where 0 was being returned for very small values.
Time Series and Forecasting
ARMA.setBackwardOrigin Error checking was added to avoid an out of bounds exception.
Anova.getDunnSidak Clarify documentation on the description of the return value.
Regression
SelectionRegression Return an error when the mean squared error for the full model is near zero. This avoids bad Cp values being returned.
A check was added to prevent an out of bounds exception.
Random Number Generation
Random.nextGeometric Added an error message to signal when the input arguments are out of range.
Random.nextLogarithmic Added an error message to signal when the input arguments are out of range.
Categorical and Discrete Data Analysis
CategoricalGenLinModel Increase internal workspace arrays to avoid an out of memory error.
com.imsl.finance
Finance
Bond.duration Corrected the initialization of a local variable in the duration calculation.
npv An IllegalArgumentException has been added when rate == -1.
irr Implemented scaling of large input arguments to avoid incorrect results.
com.imsl.chart
Chart 2d
JFrameChart Corrected the class description in the documentation.