Applications and libraries/Mathematics

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This page contains a list of libraries and tools in a certain category. For a comprehensive list of such pages, see Applications and libraries.

Libraries for numerical algorithms and mathematics

Geometric Algorithms
A small Haskell library, that contains algorithms for two-dimensional convex hulls, triangulations of polygons, Voronoi-diagrams and Delaunay-triangulations, the QEDS data structure, kd-trees and range-trees.
Digital Signal Processing
Modules for matrix manpulation, digital signal processing, spectral estimation, and frequency estimation.
Papers by Jerzy Karczmarczuk
Some interesting uses of Haskell in mathematics, including functional differentiation, power series, continued fractions.
DoCon - Algebraic Domain Constructor
A small Computer Algebra System
The HaskellMath library is a sandbox for me to experiment with mathematics algorithms. So far I've implemented a few quantitative finance models (Black Scholes, Binomial Trees, etc) and basic linear algebra functions. Next I might work on either computer algebra or linear programming. All comments welcome!
Wrapper to CLAPACK
High level functional interface to standard linear algebra computations and other numerical algorithms based on the GNU Scientific Library.
Numeric Prelude
Experimental revised framework for numeric type classes.
Exact Real Arithmetic
A portal-like treatment of the topic. There are functional programming materials too, even with downloadable Haskell source.
Probabilistic Functional Programming
The PFP library is a collection of modules for Haskell that facilitates probabilistic functional programming, that is, programming with stochastic values. The probabilistic functional programming approach is based on a data type for representing distributions. A distribution represent the outcome of a probabilistic event as a collection of all possible values, tagged with their likelihood. A nice aspect of this system is that simulations can be specified independently from their method of execution. That is, we can either fully simulate or randomize any simulation without altering the code which defines it.
Mirror of the following numeric modules by Jan Skibinski
Sinc function