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Found 37 packages
MathNet.Numerics✓
Math.NET Numerics is the numerical foundation of the Math.NET project, aiming to provide methods and algorithms for numerical computations in science, engineering and every day use. Supports .NET 5.0 or higher, .NET Standard 2.0 and .NET Framework 4.6.1 or higher, on Windows, Linux and Mac.
v5.0.0
↙ 58.4M / total
mathnumericstatisticsprobabilityintegration
MathNet.Numerics.Signed✓
Math.NET Numerics is the numerical foundation of the Math.NET project, aiming to provide methods and algorithms for numerical computations in science, engineering and every day use. Supports .NET 5.0 or higher, .NET Standard 2.0 and .NET Framework 4.6.1 or higher, on Windows, Linux and Mac. This package contains strong-named assemblies for legacy use cases (not recommended).
v5.0.0
↙ 37.0M / total
mathnumericstatisticsprobabilityintegration
MathNet.Numerics.FSharp✓
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. Supports .NET 5.0 or higher, .NET Standard 2.0 and .NET Framework 4.6.1 or higher, on Windows, Linux and Mac.
v5.0.0
↙ 4.0M / total
fsharpF#mathnumericstatistics
DiffSharp
DiffSharp is an automatic differentiation (AD) library. AD allows exact and efficient calculation of derivatives, by systematically invoking the chain rule of calculus at the elementary operator level during program execution. AD is different from numerical differentiation, which is prone to truncation and round-off errors, and symbolic differentiation, which is affected by expression swell and cannot fully handle algorithmic control flow. Using the DiffSharp library, derivative calculations (gradients, Hessians, Jacobians, directional derivatives, and matrix-free Hessian- and Jacobian-vector products) can be incorporated with minimal change into existing algorithms. Diffsharp supports nested forward and reverse AD up to any level, meaning that you can compute exact higher-order derivatives or differentiate functions that are internally making use of differentiation. Please see the API Overview page for a list of available operations. The library is under active development by Atılım Güneş Baydin and Barak A. Pearlmutter mainly for research applications in machine learning, as part of their work at the Brain and Computation Lab, Hamilton Institute, National University of Ireland Maynooth. DiffSharp is implemented in the F# language and can be used from C# and the other languages running on Mono or the .Net Framework, targeting the 64 bit platform. It is tested on Linux and Windows. We are working on interfaces/ports to other languages.
v0.7.7
↙ 56.2K / total
DifferentiationAutomaticSymbolicNumericalOptimization
Dew.Math.Linux
Dew.Math.Linux provides the high-performance numerical computation capabilities of Dew.Math, but with native acceleration binaries compiled for Linux. It is designed for compute clusters, scientific servers, containerized deployment environments, and performance-critical Linux workloads. Core Numerical Capabilities: - Dense linear algebra (BLAS/LAPACK): decomposition and eigenvalue routines optimized for AVX/AVX-512 - Sparse matrix operations with Pardiso and UMFPACK direct solvers and Krylov-based iterative solvers - Complex-valued linear algebra and spectral computations - Polynomial operations, splines, interpolation, approximate function models, and spectral transforms - Probability distributions, stochastic simulation, and random number engines - Special function library suitable for numerical physics, statistics, and differential systems - Optimization algorithms for non-linear fitting, gradient models, linear programming, and statistical inference Performance Architecture: - Linux-native accelerated BLAS/LAPACK libraries - Multithreaded vectorized math with CPU feature dispatch (AVX/AVX2/AVX-512) - Low-overhead memory allocator for stable scaling under parallel workloads - Optional OpenCL GPU integration for Linux compute environments Platform Model: - Contains Linux native runtime binaries - For Windows native acceleration use: Dew.Math - For portable managed-only computation use: Dew.Math.Core Use Dew.Math.Linux for Linux HPC compute nodes, microservice model engines, AI research pipelines, and scalable distributed scientific processing.
v6.3.10
↙ 37.4K / total
dotnetcsharplinuxnativenative-acceleration
Dew.Math
Dew.Math is the Windows-optimized high-performance numerical computation library for .NET. It provides a vectorized matrix and vector math environment with native runtime acceleration, multithreaded execution, and extensive algorithm libraries for scientific, engineering, financial, AI/ML and signal processing workloads. Core Numerical Capabilities: - Dense linear algebra (BLAS, LAPACK): SVD, QR, LQ, LU, eigenvalue problems, least-squares, rank reveals - Sparse matrix support: direct solvers (Pardiso, UMFPACK), iterative solvers (CG, BiCG, GMRES), preconditioning strategies, structured sparse formats - Complex number computation with fully vectorized math operations - Polynomial arithmetic, interpolation, splines, rational approximations, Chebyshev basis transforms - Numerical differentiation, root solving, non-linear systems, ODE support for stiff and non-stiff cases - Probability distributions (over 30 families), random number generators, Monte Carlo methods - Special mathematical functions (Airy, Bessel, Gamma-related, elliptic integrals, Legendre, etc.) Optimization and Modeling: - Non-linear curve fitting with Levenberg-Marquardt and trust-region refinements - Direct and constrained optimization (Simplex/Nelder–Mead, BFGS, Conjugate Gradient, LP, dual-phase simplex, Gomory cutting plane) - Vectorized expression parser for dynamic formula construction and symbolic-style evaluation Performance Architecture: - Native accelerated BLAS/LAPACK kernels with automatic CPU dispatch (AVX, AVX2, AVX-512) - Scalable multithreading with a lock-free memory allocator for low-GC overhead - Optional OpenCL GPU offloading for supported device targets Platform Model: - Contains Windows native acceleration binaries - For Linux native acceleration use: Dew.Math.Linux - For a pure managed, portable edition use: Dew.Math.Core Use Dew.Math when you require **maximum numerical performance on Windows** for HPC, simulation, economic modeling, data analytics, or scientific visualization workflows.
v6.3.10
↙ 54.8K / total
dotnetcsharp
MathNet.Numerics.Providers.MKL✓
Math.NET Numerics is the numerical foundation of the Math.NET project, aiming to provide methods and algorithms for numerical computations in science, engineering and every day use.
v5.0.0
↙ 273.4K / total
MathNet.Numerics.Data.Text✓
Text Data Input/Output Extensions 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.
v5.0.0
↙ 200.3K / total
mathnumericdatatextcsv
Dew.Math.Core
Dew.Math.Core is the portable, pure managed edition of the Dew.Math library. It provides the same high-level matrix and vector programming model, expression parser, probability and optimization toolkit, and special function support without linking to any native runtime components. This enables maximum compatibility across platforms and runtime environments. Core Numerical Capabilities (Managed): - Dense matrix and vector operations with operator overloading and method-based APIs - QR, LU, SVD and eigenvalue routines using high-quality managed linear algebra kernels - Complex number and real-valued computation with full vectorization in managed code - Sparse matrix representations with iterative solver support - Probability distributions, histogramming, random generators, Monte Carlo workflows - Nonlinear optimization, curve fitting, regression models, trust-region and gradient methods - Vectorized expression parsing for symbolic-style expression and simulation pipelines - Polynomial and spline interpolation, numerical integration and differentiation - Special function suite including Bessel, Airy, Gamma-related, Legendre and elliptic functions Portability Model: - No native libraries required (zero unmanaged dependencies) - Runs on Windows, Linux, macOS, iOS, Android, MAUI, Uno, WASM, Unity*, cloud functions and plugins - Targets netstandard2.0, net8.0, and net9.0 Use Dew.Math.Core when you need **maximum portability** in libraries, shared simulation engines, tooling, mobile deployments, WebAssembly environments, or plugin architectures.
v6.3.10
↙ 14.4K / total
mathnumericalvectormatrixcomplex
MathNet.Numerics.Data.Matlab✓
MathWorks MATLAB Data Input/Output Extensions 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.
v5.0.0
↙ 115.3K / total
mathnumericdatamatlab
Mars.Life.Simulations
MARS LIFE is a modelling framework for agent-based simulations. It provides the following features: * Agent definitions * Layer definitions * Integration of GIS spatial data like raster-files (*.asc, *.geotiff) and vector formats (*.shp, *.geojson, *.kml, *.gml) * Representations for temporal data with optional spatial reference (spatiotemporal) * Spatial data-structures and agent-environments for movement and explorations * Methods and algorithms for numerical computations for every day use * Result output-pipeline and simulation result persistence For more details how to use MARS, please use the documentation: https://www.mars-group.org/docs/tutorial/intro
v5.3.1
↙ 1.2M / total
numericsimulationagentgisdata
MathNet.Numerics.Providers.OpenBLAS✓
Math.NET Numerics is the numerical foundation of the Math.NET project, aiming to provide methods and algorithms for numerical computations in science, engineering and every day use.
v5.0.0
↙ 83.5K / total
pkg
NumFlat
A numerical computation library for C#
v1.2.2
↙ 11.1K / total
mathnumericslinearalgebramatrix
CenterSpace.NMath.Standard.Windows.X64
Foundational classes for financial, engineering, and scientific applications, including complex number classes, general vector and matrix classes, structured sparse matrix classes and factorizations, general sparse matrix classes and factorizations, general matrix decompositions, least squares solutions, random number generators, Fast Fourier Transforms (FFTs), numerical integration and differentiation methods, function minimization, curve fitting, root-finding, linear and nonlinear programming. This package also provides functions for statistical computation and biostatistics, including descriptive statistics, probability distributions, combinatorial functions, multiple linear regression, hypothesis testing, analysis of variance, multivariate statistics, partial least squares, and nonnegative matrix factorization. Built on .NET Standard 2.0. Requires a minimum of .NET Standard 2.0, .NET 5, .NET Core 2.0 or .NET Framework 4.6.1. Requires Visual Studio 2015-2019 C++ x64 runtime. Requires x64 hardware.
v7.4.2.16
↙ 954.0K / total
mathstatisticsnumericsprobabilitylinear
CenterSpace.NMath.Standard.Linux.X64
Foundational classes for financial, engineering, and scientific applications, including complex number classes, general vector and matrix classes, structured sparse matrix classes and factorizations, general sparse matrix classes and factorizations, general matrix decompositions, least squares solutions, random number generators, Fast Fourier Transforms (FFTs), numerical integration and differentiation methods, function minimization, curve fitting, root-finding, linear and nonlinear programming. This package also provides functions for statistical computation and biostatistics, including descriptive statistics, probability distributions, combinatorial functions, multiple linear regression, hypothesis testing, analysis of variance, multivariate statistics, partial least squares, and nonnegative matrix factorization. Built on .NET Standard 2.0. Requires a minimum of .NET Standard 2.0, .NET 5, .NET Core 2.0 or .NET Framework 4.6.1. Requires libgcc linux. Requires x64 hardware.
v7.4.2.16
↙ 333.6K / total
mathstatisticsnumericsprobabilitylinear
MathNet.Numerics.FSharp.Signed✓
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. Supports .NET 5.0 or higher, .NET Standard 2.0 and .NET Framework 4.6.1 or higher, on Windows, Linux and Mac. This package contains strong-named assemblies for legacy use cases (not recommended).
v5.0.0
↙ 106.4K / total
fsharpF#mathnumericstatistics
Dew.Signal.Linux
Dew.Signal.Linux is the Linux-native accelerated edition of the Dew.Signal digital signal processing library. It provides a full suite of DSP algorithms built on top of Dew.Math.Linux, delivering high-performance numerical processing with multithreaded AVX/AVX2/AVX-512 hardware acceleration. This package is intended for scientific servers, compute clusters, HPC pipelines, digital instrumentation, real-time data acquisition, industrial analytics, embedded Linux platforms, and cloud CPU workloads. Filter Design and Processing: - IIR filters: Butterworth, Chebyshev I/II, Elliptic, Bessel - Transformations: bilinear, matched-Z, frequency remapping, pole-zero and state-space formulations - FIR filters: window methods, Remez exchange, Hilbert transformers, differentiators, integrators, Savitzky–Golay smoothers, envelope detection - Multirate DSP: decimation, interpolation, half-band polyphase filters, zoom-spectrum workflows Spectral and Frequency-Domain Analysis: - FFT-based spectral estimation and spectrum analyzer infrastructure - Parametric estimators: Yule–Walker, Burg, Covariance, Modified Covariance - Chirp-Z transform, time-frequency spectrograms, bispectrum, bicoherence, coherence, transfer function estimation, phase unwrapping - Real/complex cepstrum and inverse cepstrum - Spectral statistics: noise floor, SFDR, THD, THDN, SINAD, RMS, SNR Signal Modeling, Streaming, and Synthesis: - White, pink, brownian, blue, violet and deterministic test signal generators - Continuous streaming components and dataflow processing units for real-time measurement systems - High-performance convolution, correlation, DCT/IDCT, interpolation and filtering kernels - Spectral forecasting based on controlled peak selection Integration and Platform Model: - Uses Dew.Math.Linux for native-accelerated numerical backend - Does **not** require WinForms or TeeChart (visualization is optional and external) - Suitable for server, embedded, batch compute, containerized, and headless execution Dew.Signal.Linux provides the same API surface as Dew.Signal, but is optimized specifically for Linux compute environments where high throughput and deterministic performance are required.
v6.3.10
↙ 9.6K / total
pkg
FTIRD.NUMIN
FTIRD.NUMIN stands as a paradigm of modern scientific computing in the .NET ecosystem. Meticulously engineered to parallel the elegance of NumPy, this library seamlessly fuses multidimensional array operations with avant-garde numerical algorithms. It empowers developers to transform raw data into insightful intelligence through an intuitive and high-performance computational toolkit.
v1.0.0
↙ 322 / total
numericalscientificmatharrayslinalg
MathNet.Numerics.Providers.CUDA✓
Math.NET Numerics is the numerical foundation of the Math.NET project, aiming to provide methods and algorithms for numerical computations in science, engineering and every day use.
v5.0.0
↙ 16.2K / total
Bonsai.Numerics✓
Bonsai Library providing methods and algorithms for common numerical computations.
v0.10.0
↙ 15.1K / total
BonsaiRxNumerics
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