Flying Flog Consulting have recently published F# for Numerics. Here’s how they describe the library:

Our newF# for Numericslibrary is a suite of numerical methods that leverage functional programming withF#…

This library implements numerical methods from a variety of different disciplines in a uniform way …:

Local and global function minimization and maximization.

Mean, median, mode, variance, standard deviation, skew, kurtosis, Shannon entropy and other statistical quantities.

Interpolation, curve fitting and regression.

Matrix factorizations including eigenvalue computation.

Numerical integration and differentiation.

Spectral methods including the Fast Fourier Transform.

FFTs now 2× faster.

1D FFTs over both arrays and vectors.

2D FFTs over F# matrices with parallelism to exploit multicores.

Linear, cubic spline and Lagrange polynomial interpolation.

More special functions including sinc, the error function and the probit function.

Faster Mersenne Twister random number generation, particularly over the Normal distribution.

Physical constants.

More worked examples.

The binomial function for combinatorics.

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