JMSL Numerical Library 
java jar lib/gallery.jar
at the command line.)
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 PlugIn, version 1.4.2 or later.
For Windowsbased 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.
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 chisquared probablity density function.  
Cdf.noncentralchi 
Evaluates the noncentral chisquared cumulative probability distribution function.  
Cdf.inverseNoncentralchi 
Evaluates the inverse of the noncentral chisquared 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 32bit Mersenne Twister generator.  
MersenneTwister64 
Initializes the 64bit 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 xaxis, yaxis or a zaxis.  
AxisBox 
Box behind the axis.  
AxisLabel 
The labels on an axis.  
AxisLine 
The axis line.  
AxisTitle 
Axis title.  
AxisXYZ 
The axis for an xyz 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. 
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. 