What's new in NMath Stats 4.2.0
Apr 8, 2016
- Added two way anova with unbalanced designs
- partial least squares discriminant analysis
- auxiliary stats for logistic regressions.
New in NMath Stats 4.1.0 (May 4, 2015)
- Upgraded to Intel MKL 11.2 Update 2 with resulting performance increases.
- Added method SortByColumnHeader() to DataFrame.
- Added methods GetStudentizedResiduals() and GetStandardizedResiduals() to class LinearRegression for getting the (externally) studentized residuals and standardized residuals (also known as the internally studentized residuals), respectively
New in NMath Stats 4.0.0 (Aug 26, 2014)
- Upgraded to Intel MKL 11.1 Update 3 with resulting performance increases
- Added Adaptive Bridge technology to NMath Stats Premium edition, with support for multiple GPUs, per-thread control for binding threads to GPUs, and automatic performance tuning of individual CPU'GPU routing to insure optimal hardware usage
- InverseCDF on NormalDistribution is much faster. We are now using Acklam's algorithm.
- More robust sum of squares implementation for floats and doubles. Leads to more robust variance and standard deviation.
New in NMath Stats 3.6.0 (May 8, 2013)
- Upgraded to Intel MKL 11.0 Update 3 with resulting performance increases.
- Added class LogisticRegression and related classes for performing binomial logistic regression.
- Added classes ProcessCapability, ProcessPerformance, and ZBench for process control statistics, such as Cp, Cpm, Cp, Pp, and Ppk.
- Added function NaNMedium( IDFColumn) to StatsFunctions.
- Throw LicenseException on expired license, rather than Environment.Exit().
New in NMath Stats 3.5.0 (Jul 24, 2012)
- Upgraded to Intel MKL 10.3 Update 11 with resulting performance increases.
- Added class NMathConfiguration for controlling the loading of the NMath
- license key, kernel assembly, and native library. License files are no
- longer used. Logging can be enabled for debugging configuration issues.
- See Chp 1 in the NMath User's Guide for more information.
- Replaced all custom NMath delegate types in the API with Func/Action
- equivalents, and deprecated the older signatures.
- Added classes DoubleFactorAnalysis, FactorAnalysisCorrelation,
- FactorAnalysisCovariance, and supporting types for performing factor
- analysis.
- Added NMathFunctions.FishersExactTest() for computing the Fisher's Exact
- Test p-value for a specified 2 x 2 contingency table.
- Added class OneSampleAndersonDarlingTest for performing an Anderson-Darling
- test of the distribution of one sample.
- Added class ShapiroWilkTest for testing the null hypothesis that a sample
- comes from a normally distributed population.
New in NMath Stats 3.4.0 (Nov 9, 2011)
- Simplified the NMath Stats installer. Updated license keys will be issued
- at upgrade time. A license file must now be deployed with your NMath Stats
- applications.
- Upgraded to Intel MKL 10.3 Update 6 with resulting performance increases.
- Added assembly NMathStatsChartMicrosoft.dll containing class NMathStatsChart, which provides static methods for plotting NMath Stats types using the Microsoft Chart Controls for .NET. (For more information, see whitepaper "NMath Stats Visualization Using the Microsoft Chart Controls.")
- Modified class TDistribution to accept fractional degrees of freedom.
- Added class TwoSampleUnpairedUnequalTTest. Unlike TwoSampleUnpairedTTest, a pooled estimate of the variance is not used.
New in NMath Stats 3.3 (Feb 8, 2011)
- Added chi square hypothesis test. Performance improvements.
New in NMath Stats 3.2 (Jul 31, 2010)
- Now ships with Kruskal-Wallis rank sum test, goodness of fit measures and the johnson distribution.
- Improved support for .NET 4.0 and performance improvements.
New in NMath Stats 3.1 (Nov 16, 2009)
- Now ships with Kmeans clustering, Savitzky-Golay smoothing and Cronbach's alpha.
- Miscellaneous bug fixes and performance improvements.
New in NMath Stats 3.0 (Apr 14, 2009)
- Now ships with non-negative matrix factorization (NMF) clustering, a DataFrame custom debug visualizer and single-precision principal component analysis. Calculations distributed to all cores on multi-core machines. Miscellaneous bug fixes and performance improvements.