MathNet.Numerics.FSharp 2.6.0

Prefix Reserved
.NET Framework 4.0 This package targets .NET Framework 4.0. The package is compatible with this framework or higher.
There is a newer version of this package available.
See the version list below for details. 
dotnet add package MathNet.Numerics.FSharp --version 2.6.0
                    


                    

                
NuGet\Install-Package MathNet.Numerics.FSharp -Version 2.6.0
This command is intended to be used within the Package Manager Console in Visual Studio, as it uses the NuGet module's version of Install-Package. 
<PackageReference Include="MathNet.Numerics.FSharp" Version="2.6.0" />
For projects that support PackageReference, copy this XML node into the project file to reference the package. 
<PackageVersion Include="MathNet.Numerics.FSharp" Version="2.6.0" />
                    


                            Directory.Packages.props
                        

                    

                
<PackageReference Include="MathNet.Numerics.FSharp" />
                    


                            Project file
For projects that support Central Package Management (CPM), copy this XML node into the solution Directory.Packages.props file to version the package. 
paket add MathNet.Numerics.FSharp --version 2.6.0
                    


                    

                
#r "nuget: MathNet.Numerics.FSharp, 2.6.0"
#r directive can be used in F# Interactive and Polyglot Notebooks. Copy this into the interactive tool or source code of the script to reference the package. 
#:package MathNet.Numerics.FSharp@2.6.0
#:package directive can be used in C# file-based apps starting in .NET 10 preview 4. Copy this into a .cs file before any lines of code to reference the package. 
#addin nuget:?package=MathNet.Numerics.FSharp&version=2.6.0
                    


                            Install as a Cake Addin
                        

                    

                
#tool nuget:?package=MathNet.Numerics.FSharp&version=2.6.0
                    


                            Install as a Cake Tool

F# Modules for Math.NET Numerics, the numerical foundation of the Math.NET project, aiming to provide methods and algorithms for numerical computations in science, engineering and every day use. Numerics is the result of merging dnAnalytics with Math.NET Iridium and is intended to replace both. Also includes a portable build supporting .Net 4.5, SL5 and .NET for Windows Store apps.

Product Compatible and additional computed target framework versions.
.NET Framework net40 is compatible.  net403 was computed.  net45 was computed.  net451 was computed.  net452 was computed.  net46 was computed.  net461 was computed.  net462 was computed.  net463 was computed.  net47 was computed.  net471 was computed.  net472 was computed.  net48 was computed.  net481 was computed. 
Compatible target framework(s)
Included target framework(s) (in package)
Learn more about Target Frameworks and .NET Standard.

NuGet packages (39)

Showing the top 5 NuGet packages that depend on MathNet.Numerics.FSharp:

Package Downloads
MathNet.Symbolics

Math.NET Symbolics is a basic open source computer algebra library for .Net and Mono. Written in F# but works well in C# as well. Supports .Net Framework 4.5 or higher and .Net Standard 2.0 or higher, on Windows, Linux and Mac.
FsLab

FsLab is a combination package that supports doing data science with F#. FsLab includes literate scripting converted to HTML and PDF, and by default references Deedle (a data frame library), FSharp.Data (for data access) and XPlot (for visualization). You can optionally add any other nuget packages.
OPTANO.Modeling

The OPTANO Modeling library allows you to use C# as a Modeling language for mathematical optimization (mixed integer programming (MIP) and linear programming (LP)). It has a lightweight footprint and connects to several solvers.
Deedle.Math

Deedle implements an efficient and robust frame and series data structures for manipulating with structured data. It supports handling of missing values, aggregations, grouping, joining, statistical functions and more. For frames and series with ordered indices (such as time series), automatic alignment is also available. This package installs the core Deedle package, Deedle.Math extension and Mathnet.Numerics to extend mathematic functions on Deedle Frames and Series.
Microsoft.Quantum.Research.Simulation

Quantum research libraries for quantum simulation (non-commercial).

GitHub repositories (4)

Showing the top 4 popular GitHub repositories that depend on MathNet.Numerics.FSharp:

Repository Stars
StockSharp/StockSharp
Algorithmic trading and quantitative trading open source platform to develop trading robots (stock markets, forex, crypto, bitcoins, and options).
mathnet/mathnet-numerics
Math.NET Numerics
NethermindEth/nethermind
A robust execution client for Ethereum node operators.
lornshrimp/Lorn.ADSP
这是一个开源的互联网在线广告投放系统。该系统可用于网站、视频播放的所有页面广告、视频广告以及无线客户端、TV广告的管理、播放、定向和统计，包括了业务管理、售前计划管理、广告活动管理、广告引擎、播放器内广告、展示广告、数据及商业智能、人群定向、第三方广告管理等几大模块。采用C#和F#编写而成，并可部署于多种云上。
Version Downloads Last Updated
6.0.0-beta2 811 3/2/2025
6.0.0-beta1 44,777 12/17/2023
5.0.0 1,410,070 4/3/2022
5.0.0-beta02 350 4/3/2022
5.0.0-beta01 21,854 3/6/2022
5.0.0-alpha16 345 2/27/2022
5.0.0-alpha15 340 2/27/2022
5.0.0-alpha14 320 2/27/2022
5.0.0-alpha13 316 2/27/2022
5.0.0-alpha12 335 2/27/2022
5.0.0-alpha11 327 2/27/2022
5.0.0-alpha10 349 2/19/2022
5.0.0-alpha09 328 2/13/2022
5.0.0-alpha08 413 12/23/2021
5.0.0-alpha07 343 12/19/2021
5.0.0-alpha06 351 12/19/2021
5.0.0-alpha05 362 12/19/2021
5.0.0-alpha04 361 12/19/2021
5.0.0-alpha03 365 12/5/2021
5.0.0-alpha02 572 7/11/2021
5.0.0-alpha01 486 6/27/2021
4.15.0 636,496 1/7/2021
4.14.0 2,630 1/1/2021
4.13.0 1,536 12/30/2020
4.12.0 39,974 8/2/2020
4.11.0 136,448 5/24/2020
4.10.0 1,620 5/24/2020
4.9.1 22,362 4/12/2020
4.9.0 76,411 10/13/2019
4.8.1 215,777 6/11/2019
4.8.0 3,392 6/2/2019
4.8.0-beta02 679 5/30/2019
4.8.0-beta01 736 4/28/2019
4.7.0 146,700 11/11/2018
4.6.0 4,654 10/19/2018
4.5.1 35,619 5/22/2018
4.5.0 2,468 5/22/2018
4.4.1 2,938 5/6/2018
4.4.0 15,026 2/25/2018
4.3.0 2,441 2/24/2018
4.2.0 3,807 2/21/2018
4.1.0 3,412 2/19/2018
4.0.0 7,327 2/11/2018
4.0.0-beta07 1,372 2/10/2018
4.0.0-beta06 1,395 2/3/2018
4.0.0-beta05 1,396 1/22/2018
4.0.0-beta04 1,371 1/13/2018
4.0.0-beta03 1,356 1/9/2018
4.0.0-beta02 1,476 1/7/2018
4.0.0-beta01 1,333 1/7/2018
4.0.0-alpha04 1,319 1/5/2018
4.0.0-alpha03 1,316 12/26/2017
4.0.0-alpha02 1,193 11/30/2017
4.0.0-alpha01 1,202 11/26/2017
3.20.2 14,263 1/22/2018
3.20.1 2,667 1/13/2018
3.20.0 54,670 7/15/2017
3.20.0-beta01 1,218 5/31/2017
3.19.0 9,186 4/29/2017
3.18.0 16,272 4/9/2017
3.17.0 11,930 1/15/2017
3.16.0 2,890 1/3/2017
3.15.0 2,454 12/27/2016
3.14.0-beta03 1,249 11/20/2016
3.14.0-beta02 1,201 11/15/2016
3.14.0-beta01 1,233 10/30/2016
3.13.1 73,641 9/6/2016
3.13.0 2,703 8/18/2016
3.12.0 9,585 7/3/2016
3.11.1 16,116 4/24/2016
3.11.0 9,822 2/13/2016
3.10.0 12,522 12/30/2015
3.9.0 5,337 11/25/2015
3.8.0 56,074 9/26/2015
3.7.1 5,276 9/21/2015
3.7.0 18,350 5/9/2015
3.6.0 13,317 3/22/2015
3.5.0 8,856 1/10/2015
3.4.0 3,098 1/4/2015
3.3.0 4,196 11/26/2014
3.3.0-beta2 1,351 10/25/2014
3.3.0-beta1 1,412 9/28/2014
3.2.3 26,830 9/6/2014
3.2.2 2,480 9/5/2014
3.2.1 2,953 8/5/2014
3.2.0 2,456 8/5/2014
3.1.0 5,173 7/20/2014
3.0.2 2,928 6/26/2014
3.0.1 2,540 6/24/2014
3.0.0 12,243 6/21/2014
3.0.0-beta05 1,458 6/20/2014
3.0.0-beta04 1,404 6/15/2014
3.0.0-beta03 1,400 6/5/2014
3.0.0-beta02 1,378 5/29/2014
3.0.0-beta01 4,278 4/14/2014
3.0.0-alpha9 1,472 3/29/2014
3.0.0-alpha8 1,438 2/26/2014
3.0.0-alpha7 8,072 12/30/2013
3.0.0-alpha6 1,567 12/2/2013
3.0.0-alpha5 4,238 10/2/2013
3.0.0-alpha4 1,500 9/22/2013
3.0.0-alpha1 1,422 9/1/2013
2.6.0 15,159 7/26/2013
2.5.0 3,232 4/14/2013
2.4.0 2,911 2/3/2013
2.3.0 3,025 11/25/2012
2.2.1 2,930 8/29/2012
2.2.0 2,737 8/27/2012
2.1.2 7,769 10/9/2011
2.1.1 2,924 10/3/2011
2.1.0.19 3,579 10/3/2011

### New: Linear Curve Fitting

- Linear least-squares fitting (regression) to lines, polynomials and linear combinations of arbitrary functions.
- Multi-dimensional fitting.
- Also works well in F# with the F# extensions.

### New: Root Finding

- Brent's method.
- Bisection method.
- Broyden's method, for multi-dimensional functions.
- Newton-Raphson method.
- Robust Newton-Raphson variant that tries to recover automatically in cases where it would fail or converge too slowly. This modification makes it more robust e.g. in the presence of singularities and less sensitive to the search range/interval.
- All algorithms support a TryFind-pattern which returns success instead of throwing an exception.
- Special case for quadratic functions, in the future to be extended e.g. to polynomials.
- Basic bracketing algorithm
- Also works well in F# with the F# extensions.

### Linear Algebra

- Native eigenvalue decomposition (EVD) support with our MKL packages
- Add missing scalar-vector operations (s-v, s/v, s%v)
- Support for new F# 3.1 row/column slicing syntax on matrices
- Matrices learned proper OfColumn/RowVectors, analog also in F#.
- Documentation Fixes
- BUG: Fixed exception text message when creating a matrix from enumerables (rows vs columns)
- We're phasing out MathNet.Numerics.IO that used to be included in the main package for matrix file I/O for text and Matlab formats. Use the new .Data.Text and .Data.Matlab packages instead.

### Statistics & Distributions

- Spearman Rank Correlation Coefficient
- Covariance function, in Array-, Streaming- and common Statistics.
- Categorical: distribution more consistent, no longer requires normalized pdf/cdf parameters
- Categorical: inverse CDF function
- BUG: Fixed static sampling methods of the `Stable` distribution.

### Misc

- BUG: Fixed a bug in the Gamma Regularized special function where in some cases with large values it returned 1 instead of 0 and vice versa.
- The F# extensions now have a strong name in (and only in) the signed package as well (previously had not been signed).
- Evaluate.Polynomial with new overload which is easier to use.
- Fixed a couple badly designed unit tests that failed on Mono.
- Repository now Vagrant-ready for easy testing against recent Mono on Debian.