From 8b6e75a973f52df1b89df28f686f4a82006ca543 Mon Sep 17 00:00:00 2001 From: Christoph Ruegg Date: Sun, 26 Apr 2020 14:17:19 +0200 Subject: [PATCH] Docs: enforce https on all links --- CONTRIBUTING.md | 2 +- CONTRIBUTORS.md | 24 ++++++------ README.md | 2 +- RELEASENOTES.md | 6 +-- docs/content/Build.md | 10 ++--- docs/content/Generate.md | 2 +- docs/content/IFsharpNotebook.md | 2 +- docs/content/Integration.md | 2 +- docs/content/MKL.md | 6 +-- docs/content/MatrixMarket.md | 4 +- docs/content/Probability.md | 68 ++++++++++++++++----------------- docs/content/Users.md | 64 +++++++++++++++---------------- 12 files changed, 96 insertions(+), 96 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index cd7b73e4..9aa29eda 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -53,7 +53,7 @@ Should you stumble on weird English grammar or wording please do fix it - most o Please avoid starting with a major refactoring or any code reformatting without talking to us first. **Breaking Compatibility:** -We try to follow [semantic versioning](http://semver.org/), meaning that we cannot break compatibility until the next major version. Since Numerics intentionally permits straight access to raw algorithms, a lot of member declarations are public and thus cannot be modified. Instead of breaking compatibility, it is often possible to create a new better version side by side though and mark the original implementation as obsolete and scheduled for removal on the next major version. +We try to follow [semantic versioning](https://semver.org/), meaning that we cannot break compatibility until the next major version. Since Numerics intentionally permits straight access to raw algorithms, a lot of member declarations are public and thus cannot be modified. Instead of breaking compatibility, it is often possible to create a new better version side by side though and mark the original implementation as obsolete and scheduled for removal on the next major version. **Merges:** Please avoid merging mainline back into your pull request branch. If you need to leverage some changes recently added to mainline, consider to rebase instead. In other words, please make sure your commits sit directly on top of a recent mainline master. diff --git a/CONTRIBUTORS.md b/CONTRIBUTORS.md index 668eec0d..7ded2f98 100644 --- a/CONTRIBUTORS.md +++ b/CONTRIBUTORS.md @@ -7,15 +7,15 @@ The Math.NET project is a community effort. We accept contributions and pull req ### Maintainers -- [Christoph Rüegg](http://christoph.ruegg.name/) (@cdrnet) ([keybase.io/cdrnet](https://keybase.io/cdrnet)) +- [Christoph Rüegg](https://christoph.ruegg.name/) (@cdrnet) ([keybase.io/cdrnet](https://keybase.io/cdrnet)) ### Code Contributors *Essentially the output of `git shortlog -sn` in original order. Feel free to add a link to your personal site/blog and/or twitter handle.* -- [Christoph Rüegg](http://christoph.ruegg.name/) (@cdrnet) -- [Marcus Cuda](http://marcuscuda.com/) (@marcuscuda) +- [Christoph Rüegg](https://christoph.ruegg.name/) (@cdrnet) +- [Marcus Cuda](https://marcuscuda.com/) (@marcuscuda) - Jurgen Van Gael (@jvangael) - Scott Stephens - Ignas Anikevicius @@ -124,11 +124,11 @@ This section is incomplete - let us know if we forgot something.* *Math.NET Numerics started by merging the dnAnalytics and Math.NET Iridium projects and their code base.* -- [Marcus Cuda](http://marcuscuda.com/) +- [Marcus Cuda](https://marcuscuda.com/) - Jurgen Van Gael - Patrick van der Velde -- [Christoph Rüegg](http://christoph.ruegg.name/) -- [Joannès Vermorel](http://www.vermorel.com/) +- [Christoph Rüegg](https://christoph.ruegg.name/) +- [Joannès Vermorel](https://www.vermorel.com/) - Matthew Kitchin - Rana Ian - Andrew Kurochka @@ -138,12 +138,12 @@ This section is incomplete - let us know if we forgot something.* *As inspiration, reference or more - depending on the licensing terms* -- [ALGLIB](http://www.alglib.net/): Sergey Bochkanov -- [Boost](http://www.boost.org/): John Maddock -- [Netlib/Cephes Math Library](http://www.netlib.org/cephes/): Stephen L. Moshier -- [Stand-alone code for numerical computing](http://www.johndcook.com/stand_alone_code.html): John D. Cook -- [Miscellaneous Utility Library](http://www.yoda.arachsys.com/csharp/miscutil/): Marc Gravell, Jon Skeet -- [NIST Digital Library of Mathematical Functions](http://www.johndcook.com/stand_alone_code.html) +- [ALGLIB](https://www.alglib.net/): Sergey Bochkanov +- [Boost](https://www.boost.org/): John Maddock +- [Netlib/Cephes Math Library](https://www.netlib.org/cephes/): Stephen L. Moshier +- [Stand-alone code for numerical computing](https://www.johndcook.com/stand_alone_code.html): John D. Cook +- [Miscellaneous Utility Library](https://www.yoda.arachsys.com/csharp/miscutil/): Marc Gravell, Jon Skeet +- [NIST Digital Library of Mathematical Functions](https://www.johndcook.com/stand_alone_code.html) ### Special Thanks diff --git a/README.md b/README.md index fcc51762..dc655970 100644 --- a/README.md +++ b/README.md @@ -13,7 +13,7 @@ Math.NET Numerics is covered under the terms of the [MIT/X11](LICENSE.md) licens * [Source Code](https://github.com/mathnet/mathnet-numerics) * [NuGet & Binaries](https://numerics.mathdotnet.com/Packages.html) | [Release Notes](https://numerics.mathdotnet.com/ReleaseNotes.html) * [Documentation](https://numerics.mathdotnet.com) | [API Reference](https://numerics.mathdotnet.com/api/) -* [Issues & Bugs](https://github.com/mathnet/mathnet-numerics/issues) | [Ideas](http://feedback.mathdotnet.com/forums/2060-math-net-numerics) +* [Issues & Bugs](https://github.com/mathnet/mathnet-numerics/issues) | [Ideas](https://feedback.mathdotnet.com/forums/2060-math-net-numerics) * [Discussions](https://discuss.mathdotnet.com/c/numerics) | [Stack Overflow](https://stackoverflow.com/questions/tagged/mathdotnet) | [Twitter](https://twitter.com/MathDotNet) * [Wikipedia](https://en.wikipedia.org/wiki/Math.NET_Numerics) | [OpenHUB](https://www.openhub.net/p/mathnet-numerics) diff --git a/RELEASENOTES.md b/RELEASENOTES.md index db16e989..d47e3b61 100644 --- a/RELEASENOTES.md +++ b/RELEASENOTES.md @@ -437,7 +437,7 @@ * Update Vagrant setup to official Ubuntu 14.04 LTS box and proper apt-style Mono+F# provisioning. ### 3.0.0-beta01 - 2014-04-01 -* See also: [Roadmap](https://sdrv.ms/17wPFlW) and [Towards Math.NET Numerics Version 3](http://christoph.ruegg.name/blog/towards-mathnet-numerics-v3.html). +* See also: [Roadmap](https://sdrv.ms/17wPFlW) and [Towards Math.NET Numerics Version 3](https://christoph.ruegg.name/blog/towards-mathnet-numerics-v3.html). * **Major release with breaking changes** * All obsolete code has been removed * Reworked redundancies, inconsistencies and unfortunate past design choices. @@ -585,7 +585,7 @@ * BUG: fixing a bug in `ArrayStatistics.Variance` on arrays longer than 46341 entries. ### 2.6.0 - 2013-07-26 -* See also: [What's New in Math.NET Numerics 2.6](http://christoph.ruegg.name/blog/new-in-mathnet-numerics-2-6.html) +* See also: [What's New in Math.NET Numerics 2.6](https://christoph.ruegg.name/blog/new-in-mathnet-numerics-2-6.html) * 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. * Root Finding: * Brent's method. *~Candy Chiu, Alexander Täschner* @@ -617,7 +617,7 @@ * Repository now Vagrant-ready for easy testing against recent Mono on Debian. ### 2.5.0 - 2013-04-14 -* See also: [What's New in Math.NET Numerics 2.5](http://christoph.ruegg.name/blog/new-in-mathnet-numerics-2-5.html) +* See also: [What's New in Math.NET Numerics 2.5](https://christoph.ruegg.name/blog/new-in-mathnet-numerics-2-5.html) * Statistics: Empty statistics now return NaN instead of either 0 or throwing an exception. *This may break code in case you relied upon the previous unusual and inconsistent behavior.* * Linear Algebra: More reasonable ToString behavior for matrices and vectors. *This may break code if you relied upon ToString to export your full data to text form intended to be parsed again later. Note that the classes in the MathNet.Numerics.IO library are more appropriate for storing and loading data.* * Statistics: diff --git a/docs/content/Build.md b/docs/content/Build.md index 4bb9ad58..b8c0f906 100644 --- a/docs/content/Build.md +++ b/docs/content/Build.md @@ -32,7 +32,7 @@ FAKE ---- The fully automated build including unit tests, documentation and api -reference, NuGet and Zip packages is using [FAKE](http://fsharp.github.io/FAKE/). +reference, NuGet and Zip packages is using [FAKE](https://fsharp.github.io/FAKE/). FAKE itself is not included in the repository but it will download and bootstrap itself automatically when build.cmd is run the first time. Note that this step @@ -63,13 +63,13 @@ is *not* required when using Visual Studio or `msbuild` directly. build.cmd All # build, test, docs, api reference (.Net 4.0) If the build or tests fail claiming that FSharp.Core was not be found, see -[fsharp.org](http://fsharp.org/use/windows/) or install the +[fsharp.org](https://fsharp.org/use/windows/) or install the [Visual F# 3.0 Tools](https://go.microsoft.com/fwlink/?LinkId=261286) directly. Dependencies ------------ -We manage NuGet and other dependencies with [Paket](http://fsprojects.github.io/Paket/). +We manage NuGet and other dependencies with [Paket](https://fsprojects.github.io/Paket/). You do not normally have to do anything with Paket as it is integrated into our FAKE build tools, unless you want to actively manage the dependencies. @@ -86,8 +86,8 @@ Documentation ------------- This website and documentation is automatically generated from of a set of -[CommonMark](http://commonmark.org/) structured files in `doc/content/` using -[FSharp.Formatting](http://tpetricek.github.io/FSharp.Formatting/). +[CommonMark](https://commonmark.org/) structured files in `doc/content/` using +[FSharp.Formatting](https://tpetricek.github.io/FSharp.Formatting/). The final documentation can be built by calling `build.sh Docs`. However, for editing and previewing the docs on your local machine it is more diff --git a/docs/content/Generate.md b/docs/content/Generate.md index 28c70b5a..6aeaeff2 100644 --- a/docs/content/Generate.md +++ b/docs/content/Generate.md @@ -11,7 +11,7 @@ Generating Data =============== Numerics is all about analyzing and manipulating numeric data. But unless you can read in data from an external -file, source or e.g. with the excellent [F# Type Providers](http://fsharp.github.io/FSharp.Data/), +file, source or e.g. with the excellent [F# Type Providers](https://fsharp.github.io/FSharp.Data/), you may need to generate synthetic or random data locally, or transform existing data into a new form. The `Generate` class can help you in all these scenarios with a set of static functions generating either an array or an IEnumerable sequence. diff --git a/docs/content/IFsharpNotebook.md b/docs/content/IFsharpNotebook.md index bcd6d2b4..11fae28c 100644 --- a/docs/content/IFsharpNotebook.md +++ b/docs/content/IFsharpNotebook.md @@ -19,7 +19,7 @@ for iPython with IntelliSense and embedded FSharp.Charting. Thanks to its NuGet Installing IF# Notebook ----------------------- -Follow the instructions at [IfSharp/Installation](http://bayardrock.github.io/IfSharp/installation.html). +Follow the instructions at [IfSharp/Installation](https://bayardrock.github.io/IfSharp/installation.html). Essentially: diff --git a/docs/content/Integration.md b/docs/content/Integration.md index 2d5a2a6d..59cd6b05 100644 --- a/docs/content/Integration.md +++ b/docs/content/Integration.md @@ -69,7 +69,7 @@ The basic idea of Gauss-Legendre integration is to approximate the integral of a $$$ \int_a^b f(x) \, dx \approx \sum_{i = 0}^{N - 1} w_i f(x_i) -This algorithm calculates the abscissas and weights for a given order and integration interval. For efficiency, pre-computed abscissas and weights for the orders $ N = 2 - 20, \, 32, \, 64, \, 96, 100, \, 128, \, 256, \, 512, \, 1024$ are used. Otherwise, they are calculated on the fly using Newton's method. For more information on the algorithm see [[Holoborodko, Pavel] ](http://www.holoborodko.com/pavel/numerical-methods/numerical-integration/). +This algorithm calculates the abscissas and weights for a given order and integration interval. For efficiency, pre-computed abscissas and weights for the orders $ N = 2 - 20, \, 32, \, 64, \, 96, 100, \, 128, \, 256, \, 512, \, 1024$ are used. Otherwise, they are calculated on the fly using Newton's method. For more information on the algorithm see [[Holoborodko, Pavel] ](https://www.holoborodko.com/pavel/numerical-methods/numerical-integration/). ### Abscissas and Weights diff --git a/docs/content/MKL.md b/docs/content/MKL.md index 0261ed5f..b5559c8e 100644 --- a/docs/content/MKL.md +++ b/docs/content/MKL.md @@ -92,7 +92,7 @@ libraries into the same folder as the executable is not enough. The safe way is and use `ldconfig` to tell where to look for the libraries. Alternatively you could add the path to `LD_LIBRARY_PATH` or even just copy them to `/usr/lib`. -For details see Mono's [Interop with Native Libraries](http://www.mono-project.com/docs/advanced/pinvoke/#linux-shared-library-search-path). +For details see Mono's [Interop with Native Libraries](https://www.mono-project.com/docs/advanced/pinvoke/#linux-shared-library-search-path). Default Behavior on Mac OS X @@ -101,7 +101,7 @@ Default Behavior on Mac OS X You can configure the search path on one of the environment variables like `DYLD_LIBRARY_PATH` or just copy them e.g. to `/usr/lib`. -For details see Mono's [Interop with Native Libraries](http://www.mono-project.com/docs/advanced/pinvoke/#mac-os-x-framework-and-dylib-search-path). +For details see Mono's [Interop with Native Libraries](https://www.mono-project.com/docs/advanced/pinvoke/#mac-os-x-framework-and-dylib-search-path). To build the MKL native provider for OSX: @@ -158,7 +158,7 @@ This script assumes that the MKL binaries have been copied to the project direct which is also where the NuGet packages place them by default. If you place them somewhere else, adapt the path accordingly. -See also [Loading Native DLLs in F# Interactive](http://christoph.ruegg.name/blog/loading-native-dlls-in-fsharp-interactive.html) +See also [Loading Native DLLs in F# Interactive](https://christoph.ruegg.name/blog/loading-native-dlls-in-fsharp-interactive.html) for more alternatives. LINQPad and assembly shadowing diff --git a/docs/content/MatrixMarket.md b/docs/content/MatrixMarket.md index 193fc3cd..201235ba 100644 --- a/docs/content/MatrixMarket.md +++ b/docs/content/MatrixMarket.md @@ -1,8 +1,8 @@ NIST MatrixMarket Text Files ============================ -MatrixMarket is both a [vast repository of test data](http://math.nist.gov/MatrixMarket/) -and a text-based [exchange file format](http://math.nist.gov/MatrixMarket/formats.html) provided by NIST. +MatrixMarket is both a [vast repository of test data](https://math.nist.gov/MatrixMarket/) +and a text-based [exchange file format](https://math.nist.gov/MatrixMarket/formats.html) provided by NIST. Being text-based makes it convenient to deal with and program against, and also works well with versioning tools like [Git](https://www.git-scm.com/). But other than [CSV](CSV.html) it can also store sparse matrices in a compact way. diff --git a/docs/content/Probability.md b/docs/content/Probability.md index 3069d88b..87c86cb6 100644 --- a/docs/content/Probability.md +++ b/docs/content/Probability.md @@ -54,24 +54,24 @@ for simpler usage scenarios: ### Continuous Distributions -* [Continuous Uniform](http://en.wikipedia.org/wiki/Uniform_distribution_%28continuous%29) -* [Normal](http://en.wikipedia.org/wiki/Normal_distribution) -* [Log Normal](http://en.wikipedia.org/wiki/Log-normal_distribution) -* [Beta](http://en.wikipedia.org/wiki/Beta_distribution) -* [Cauchy](http://en.wikipedia.org/wiki/cauchy_distribution) (Cauchy-Lorentz) -* [Chi](http://en.wikipedia.org/wiki/Chi_distribution) -* [Chi Squared](http://en.wikipedia.org/wiki/Chi-square_distribution) -* [Erlang](http://en.wikipedia.org/wiki/Erlang_distribution) -* [Exponential](http://en.wikipedia.org/wiki/exponential_distribution) -* [Fisher-Snedecor](http://en.wikipedia.org/wiki/F-distribution) (F-Distribution) -* [Gamma](http://en.wikipedia.org/wiki/Gamma_distribution) -* [Inverse Gamma](http://en.wikipedia.org/wiki/inverse-gamma_distribution) -* [Laplace](http://en.wikipedia.org/wiki/Laplace_distribution) -* [Pareto](http://en.wikipedia.org/wiki/Pareto_distribution) -* [Rayleigh](http://en.wikipedia.org/wiki/Rayleigh_distribution) -* [Stable](http://en.wikipedia.org/wiki/Stable_distribution) -* [Stundent-T](http://en.wikipedia.org/wiki/Student%27s_t-distribution) -* [Weibull](http://en.wikipedia.org/wiki/Weibull_distribution) +* [Continuous Uniform](https://en.wikipedia.org/wiki/Uniform_distribution_%28continuous%29) +* [Normal](https://en.wikipedia.org/wiki/Normal_distribution) +* [Log Normal](https://en.wikipedia.org/wiki/Log-normal_distribution) +* [Beta](https://en.wikipedia.org/wiki/Beta_distribution) +* [Cauchy](https://en.wikipedia.org/wiki/cauchy_distribution) (Cauchy-Lorentz) +* [Chi](https://en.wikipedia.org/wiki/Chi_distribution) +* [Chi Squared](https://en.wikipedia.org/wiki/Chi-square_distribution) +* [Erlang](https://en.wikipedia.org/wiki/Erlang_distribution) +* [Exponential](https://en.wikipedia.org/wiki/exponential_distribution) +* [Fisher-Snedecor](https://en.wikipedia.org/wiki/F-distribution) (F-Distribution) +* [Gamma](https://en.wikipedia.org/wiki/Gamma_distribution) +* [Inverse Gamma](https://en.wikipedia.org/wiki/inverse-gamma_distribution) +* [Laplace](https://en.wikipedia.org/wiki/Laplace_distribution) +* [Pareto](https://en.wikipedia.org/wiki/Pareto_distribution) +* [Rayleigh](https://en.wikipedia.org/wiki/Rayleigh_distribution) +* [Stable](https://en.wikipedia.org/wiki/Stable_distribution) +* [Stundent-T](https://en.wikipedia.org/wiki/Student%27s_t-distribution) +* [Weibull](https://en.wikipedia.org/wiki/Weibull_distribution) * [Triangular](https://en.wikipedia.org/wiki/Triangular_distribution) @@ -79,25 +79,25 @@ for simpler usage scenarios: ### Discrete Distributions -* [Discrete Uniform](http://en.wikipedia.org/wiki/Uniform_distribution_%28discrete%29) -* [Bernoulli](http://en.wikipedia.org/wiki/Bernoulli_distribution) -* [Binomial](http://en.wikipedia.org/wiki/Binomial_distribution) -* [Negative Binomial](http://en.wikipedia.org/wiki/Negative_binomial_distribution) -* [Geometric](http://en.wikipedia.org/wiki/geometric_distribution) -* [Hypergeometric](http://en.wikipedia.org/wiki/Hypergeometric_distribution) -* [Poisson](http://en.wikipedia.org/wiki/Poisson_distribution) -* [Categorical](http://en.wikipedia.org/wiki/Categorical_distribution) -* [Conway-Maxwell-Poisson](http://en.wikipedia.org/wiki/Conway%E2%80%93Maxwell%E2%80%93Poisson_distribution) -* [Zipf](http://en.wikipedia.org/wiki/Zipf%27s_law) +* [Discrete Uniform](https://en.wikipedia.org/wiki/Uniform_distribution_%28discrete%29) +* [Bernoulli](https://en.wikipedia.org/wiki/Bernoulli_distribution) +* [Binomial](https://en.wikipedia.org/wiki/Binomial_distribution) +* [Negative Binomial](https://en.wikipedia.org/wiki/Negative_binomial_distribution) +* [Geometric](https://en.wikipedia.org/wiki/geometric_distribution) +* [Hypergeometric](https://en.wikipedia.org/wiki/Hypergeometric_distribution) +* [Poisson](https://en.wikipedia.org/wiki/Poisson_distribution) +* [Categorical](https://en.wikipedia.org/wiki/Categorical_distribution) +* [Conway-Maxwell-Poisson](https://en.wikipedia.org/wiki/Conway%E2%80%93Maxwell%E2%80%93Poisson_distribution) +* [Zipf](https://en.wikipedia.org/wiki/Zipf%27s_law) ### Multivariate Distributions -* [Dirichlet](http://en.wikipedia.org/wiki/Dirichlet_distribution) -* [Inverse Wishart](http://en.wikipedia.org/wiki/Inverse-Wishart_distribution) -* [Matrix Normal](http://en.wikipedia.org/wiki/Matrix_normal_distribution) -* [Multinomial](http://en.wikipedia.org/wiki/Multinomial_distribution) -* [Normal Gamma](http://en.wikipedia.org/wiki/Normal-gamma_distribution) -* [Wishart](http://en.wikipedia.org/wiki/Wishart_distribution) +* [Dirichlet](https://en.wikipedia.org/wiki/Dirichlet_distribution) +* [Inverse Wishart](https://en.wikipedia.org/wiki/Inverse-Wishart_distribution) +* [Matrix Normal](https://en.wikipedia.org/wiki/Matrix_normal_distribution) +* [Multinomial](https://en.wikipedia.org/wiki/Multinomial_distribution) +* [Normal Gamma](https://en.wikipedia.org/wiki/Normal-gamma_distribution) +* [Wishart](https://en.wikipedia.org/wiki/Wishart_distribution) diff --git a/docs/content/Users.md b/docs/content/Users.md index 89c0f842..41eb347d 100644 --- a/docs/content/Users.md +++ b/docs/content/Users.md @@ -7,18 +7,18 @@ Feel free to [add, edit or remove your own work](https://github.com/mathnet/math Open Source ----------- -* [MyMediaLite Recommender System Library](http://www.ismll.uni-hildesheim.de/mymedialite/) +* [MyMediaLite Recommender System Library](https://www.ismll.uni-hildesheim.de/mymedialite/) * [FermiSim, studying potential solutions to the Fermi paradox via computational simulation of models for space colonisation](https://launchpad.net/fermisim) * [Three-Dimensional Model Shape Description and Retrieval Based on LightField Descriptors](https://code.google.com/p/lightfieldretrieval/) * [Virtual Photonics Technology Initiative](https://virtualphotonics.codeplex.com/) * [SharpFE: a lightweight, expandable finite element solver for .net](https://github.com/iainsproat/SharpFE) -* [FsLab: Machine Learning and Data Science with F#](http://fslab.org/) -* [Simple FM Matrix](http://cs.mcgill.ca/~ryang6/simplefmmatrix/) -* [mathlib.net](http://mathlibproject.codeplex.com/) +* [FsLab: Machine Learning and Data Science with F#](https://fslab.org/) +* [Simple FM Matrix](https://cs.mcgill.ca/~ryang6/simplefmmatrix/) +* [mathlib.net](https://mathlibproject.codeplex.com/) * [PhysicsPad](https://github.com/Amichai/PhysicsPad) * [DRFCSharp: Discriminative Random Fields implementation for C#](https://github.com/exitmouse/drfcsharp) * [SharpBench: Benchmarking system for Mono/.Net](https://github.com/ranma42/SharpBench) -* [Behavioral Rating of Dancing Human Crowds based on Motion Patterns](http://emotiondetection.codeplex.com/) +* [Behavioral Rating of Dancing Human Crowds based on Motion Patterns](https://emotiondetection.codeplex.com/) * [NPSS Framework for numerical computations of Laguerre series](https://www.openhub.net/p/npss) * [FIRST Robotics Scout App](https://frcscout.codeplex.com/) * [Improvisation: Automatic Music Composition and Melody Generation](https://github.com/ArmenAg/Improvisation) @@ -32,45 +32,45 @@ Open Source * [Reinforcement Simulator](https://github.com/lg-octaviano/Reinforcement_Simulator) * [Maintenance Game](https://github.com/KaptenJon/MaintenanceGame) * [Monica](https://github.com/zhuazhua/Monica) -* [Math.Net PowerShell](http://mathnetpowershell.codeplex.com/) (unaffiliated) +* [Math.Net PowerShell](https://mathnetpowershell.codeplex.com/) (unaffiliated) * [Math.NET Symbolics](https://symbolics.mathdotnet.com) and other [Math.NET](https://www.mathdotnet.com) projects. Closed Source ------------- -* [C# Pad](http://www.csharppad.com) -* [Colectica](http://www.colectica.com) Data Documentation -* [Instarange Simulation](http://instarange.com) by Instarange (Pty) Ltd -* [SpectraFox](http://www.spectrafox.com/) STM / AFM spectroscopy analysis -* [Colymp](http://colymp.com/) Color Management Software -* [ILNumerics](http://ilnumerics.net/) -* [GazeSpeaker](http://www.gazespeaker.org) Free software to help people with disabilities -* [ModVal.org](http://www.modval.org) Quant model repository for regulatory and internal model validation. +* [C# Pad](https://www.csharppad.com) +* [Colectica](https://www.colectica.com) Data Documentation +* [Instarange Simulation](https://instarange.com) by Instarange (Pty) Ltd +* [SpectraFox](https://www.spectrafox.com/) STM / AFM spectroscopy analysis +* [Colymp](https://colymp.com/) Color Management Software +* [ILNumerics](https://ilnumerics.net/) +* [GazeSpeaker](https://www.gazespeaker.org) Free software to help people with disabilities +* [ModVal.org](https://www.modval.org) Quant model repository for regulatory and internal model validation. * [Passivefilter](https://sites.google.com/site/passivefilter/home) Filter synthesis -* [Qunatellia](http://www.quantellia.com) World Modeler -* [Umberto NXT](http://www.umberto.de) Carbon footprint, resource efficiency, life-cycle assessment, eco-efficiency. -* Agilent Waveform Creator: [Easily create custom Waveform plug-ins with Waveform Creator application software](http://cp.literature.agilent.com/litweb/pdf/5991-3203EN.pdf) (PDF) +* [Qunatellia](https://www.quantellia.com) World Modeler +* [Umberto NXT](https://www.umberto.de) Carbon footprint, resource efficiency, life-cycle assessment, eco-efficiency. +* Agilent Waveform Creator: [Easily create custom Waveform plug-ins with Waveform Creator application software](https://cp.literature.agilent.com/litweb/pdf/5991-3203EN.pdf) (PDF) * Multiple medical hearing care companies Blogs, Tutorials & Examples --------------------------- -* [Yin Zhu: Tutorial: Using Math.NET Numerics in F#](http://msdn.microsoft.com/en-us/library/hh304363.aspx) -* [Don Syme: Getting Started with Math.NET and F# Programming](http://blogs.msdn.com/b/dsyme/archive/2012/07/06/getting-started-with-math-net-and-f-programming.aspx) -* [Libor Tinka: Linear and Nonlinear Least-Squares with Math.NET](http://www.imagingshop.com/articles/least-squares) -* [Carl Nolan: Co-occurrence Approach to an Item Based Recommender](http://code.msdn.microsoft.com/Co-occurrence-Approach-to-57027db7) -* [Gustavo Guerra: F# as a Octave/Matlab Replacement for Machine Learning](http://functionalflow.co.uk/blog/2011/10/27/f-as-a-octavematlab-replacement-for-machine-learning/) -* [Mathias Brandewinder: Simplify data with SVD and Math.NET in F#](http://clear-lines.com/blog/post/Simplify-data-with-SVD-and-MathNET-in-FSharp.aspx) -* [Mathias Brandewinder: Recommendation Engine using Math.NET, SVD and F#](http://clear-lines.com/blog/post/Recommendation-Engine-with-SVD-and-MathNET-in-FSharp.aspx) -* [Thomas Jungblut: Stochastic Logistic Regression in F#](http://codingwiththomas.blogspot.ch/2014/05/stochastic-logistic-regression-in-f.html) -* [Calvin Bottoms: Set-Based Operations: They’re Not Just For Databases](http://calvinbottoms.blogspot.ch/2012/01/set-based-operations-theyre-not-just.html) -* [Chao-Jen Chen: F#: Simulate entire GBM path](http://programmingcradle.blogspot.ch/2012/09/f-simulate-entire-gbm-path.html) -* [Chao-Jen Chen: F#: K-S test on final prices of GBM paths ](http://programmingcradle.blogspot.ch/2012/09/f-k-s-test-on-final-prices-of-gbm-paths.html) -* [Dawid Kowalski: F#, Deedle and Computational Investing](http://dkowalski.com/blog/archive/2014/01/11/f-deedle-and-computational-investing.aspx) -* [Jason Fossen: PowerShell for Math.NET Numerics](http://cyber-defense.sans.org/blog/2015/06/27/powershell-for-math-net-numerics) +* [Yin Zhu: Tutorial: Using Math.NET Numerics in F#](https://msdn.microsoft.com/en-us/library/hh304363.aspx) +* [Don Syme: Getting Started with Math.NET and F# Programming](https://blogs.msdn.com/b/dsyme/archive/2012/07/06/getting-started-with-math-net-and-f-programming.aspx) +* [Libor Tinka: Linear and Nonlinear Least-Squares with Math.NET](https://www.imagingshop.com/articles/least-squares) +* [Carl Nolan: Co-occurrence Approach to an Item Based Recommender](https://code.msdn.microsoft.com/Co-occurrence-Approach-to-57027db7) +* [Gustavo Guerra: F# as a Octave/Matlab Replacement for Machine Learning](https://functionalflow.co.uk/blog/2011/10/27/f-as-a-octavematlab-replacement-for-machine-learning/) +* [Mathias Brandewinder: Simplify data with SVD and Math.NET in F#](https://clear-lines.com/blog/post/Simplify-data-with-SVD-and-MathNET-in-FSharp.aspx) +* [Mathias Brandewinder: Recommendation Engine using Math.NET, SVD and F#](https://clear-lines.com/blog/post/Recommendation-Engine-with-SVD-and-MathNET-in-FSharp.aspx) +* [Thomas Jungblut: Stochastic Logistic Regression in F#](https://codingwiththomas.blogspot.ch/2014/05/stochastic-logistic-regression-in-f.html) +* [Calvin Bottoms: Set-Based Operations: They’re Not Just For Databases](https://calvinbottoms.blogspot.ch/2012/01/set-based-operations-theyre-not-just.html) +* [Chao-Jen Chen: F#: Simulate entire GBM path](https://programmingcradle.blogspot.ch/2012/09/f-simulate-entire-gbm-path.html) +* [Chao-Jen Chen: F#: K-S test on final prices of GBM paths ](https://programmingcradle.blogspot.ch/2012/09/f-k-s-test-on-final-prices-of-gbm-paths.html) +* [Dawid Kowalski: F#, Deedle and Computational Investing](https://dkowalski.com/blog/archive/2014/01/11/f-deedle-and-computational-investing.aspx) +* [Jason Fossen: PowerShell for Math.NET Numerics](https://cyber-defense.sans.org/blog/2015/06/27/powershell-for-math-net-numerics) * [Jason Fossen: TrueRNG Random Numbers with PowerShell and Math.NET Numerics](https://cyber-defense.sans.org/blog/2015/07/24/truerng-usb-random-numbers-powershell-mathnet-numerics) -* [Thomasz Jaskula: Data Science tools in F# through univariante linear regression](http://jaskula.fr/blog/2015/12-02-data-science-tools-in-f-through-univariante-linear-regression/) -* [Christoph Rüegg: Linear Regression With Math.NET Numerics](http://christoph.ruegg.name/blog/linear-regression-mathnet-numerics.html) +* [Thomasz Jaskula: Data Science tools in F# through univariante linear regression](https://jaskula.fr/blog/2015/12-02-data-science-tools-in-f-through-univariante-linear-regression/) +* [Christoph Rüegg: Linear Regression With Math.NET Numerics](https://christoph.ruegg.name/blog/linear-regression-mathnet-numerics.html) Books -----