Release Notes

For a detailed overview of what’s new in version 9 of Numerics.NET, see the dedicated page What’s New in Version 9.0.

This page lists the improvements since the original release of version 9.

Version 9.0.7

February 2025

New QuadraticDiscriminantAnalysis (QDA) class to classify data using a quadratic decision boundary.
Fligner-Killeen test for equality of variances.
New probability distributions: Folded normal distribution and hyperbolic secant distribution.
New LinearAlgebraPool<T> to provide memory-efficient storage for short-lived vectors and matrices.
Performance improvements enabled by LinearAlgebraPool<T> in many algorithms.
Improved and expanded the documentation of probability distributions and statistical tests, including contingency tables.

February 2025

Added the static Compare class to compare numbers and sequences within a specified tolerance.
Added new special functions: Scaled versions of Airy functions and some Bessel functions, difference between modified Bessel and Struve functions, exponential integral of arbitrary order, inverse of the Digamma function.
Performance improvements, including optimizing the handling of static arrays to minimize allocations and overhead.
Improved consistency in the names of some special functions, including Hankel functions and exponential integrals. The methods with old names have been marked obsolete and will eventually be removed.

January 2025

New IntegrationRules class to create stand-alone integration rules for a variety of weight functions.
Considerably improved the performance of the adaptive numerical integrator.
Methods to compute the Discrete Cosine Transform and its inverse.
Optimized the calculation of the eigenvalues and eigenvectors of a symmetric tridiagonal matrix.
Improvements to the GumbelDistribution class to make a clearer distinction between the variants that model the smallest or the largest extreme value.
New abbreviated aliases (Pdf, Cdf, and InverseCdf) for some static methods of probability distributions.
Various minor bug fixes and performance improvements.

November 2024

New ChiDistribution class to represent the chi distribution and compute probabilities and quantiles.
New HalleySolver class to solve equations using Halley’s method, a third-order root-finding algorithm.
New Span based API’s: polynomial interpolation; mean, standard deviation, skewness and kurtosis estimates.
New extension methods for sampling without replacement.
New left and right tail probabilities for Parameter objects.
Fixed a problem with scaling the rows of sparse matrices.
Fixed a problem when aligning vectors with different indexes with identical values.

November 2024

Support for .NET 9.0 and C# 13 features, including Span-based params lists and collection expressions for vectors and matrices.
New accumulator and aggregator for the Sum of Absolute Differences (SAD) of two sequences.
Many new tensor operators, taking advantage of new .NET 9.0 functionality.
Performance optimizations in the tensor library for ternary operators.

October 2024

New VandermondeMatrix<T> class to represent Vandermonde matrices and solve Vandermode systems of equations efficiently.
New FiniteDifferenceMethod class to represent finite difference methods for approximating derivatives of functions.
Improvements to the Derivative method to support higher-order derivatives, higher order methods, and more.
Fixed an issue where the PartialLeastSquares class would not compute the predicted values correctly under some conditions.
Fixed a regression in the Pricipal Component Analysis class.
Several performance improvements and minor bug fixes.

September 2024

The performance of BigInteger has been improved across the board. The difference is particularly noticeable for small numbers. By extension, BigRational and BigFloat benefit as well.
The scaling and standardization of features for Pricipal Component Analysis has been enhanced. Centering of variables can be disabled, and a new scaling option has bee added that ensures that the maximum absolute value of a variable is 1.
Improvements in the calculation of the nearest correlation matrix. The method is more reliable and now has the option to specify a minimum value for the eigenvalues of the resulting matrix.