Search Results for "linux engineers toolkit"
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Showing 11 open source projects for "linux engineers toolkit"
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Statistics101 - Resampling Statistics
Use simulation to perform statistical analyses.
Statistics101 is an Integrated Development Environment (IDE) that uses a simple, powerful language called “Resampling Stats” to develop Monte Carlo programs to analyze and solve statistical problems. The original Resampling Stats language and computer program were developed by Dr. Julian Simon (https://www.juliansimon.com/) and Peter Bruce (https://www.scientificamerican.com/author/peter-bruce/) as a new way to teach Statistics to social science students. Of course, social science students... -
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stkpp
C++ Statistical ToolKit
STK++ (http://www.stkpp.org) is a versatile, fast, reliable and elegant collection of C++ classes for statistics, clustering, linear algebra, arrays (with an Eigen-like API), regression, dimension reduction, etc. Some functionalities provided by the library are available in the R environment as R functions (http://cran.at.r-project.org/web/packages/rtkore/index.html). At a convenience, we propose the source packages on sourceforge. The library offers a dense set of (mostly) template... -
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AlphaPlot
Interactive scientific graphing and data analysis software.
Alpha Plot can generate different types of 2D and 3D plots (such as line, scatter, bar, pie, and surface plots) from data that is either imported from ASCII files, entered by hand, or calculated using formulas. The data is held in spreadsheets which are referred to as tables with column-based data (typically X and Y values for 2D plots) or matrices (for 3D plots). The spreadsheets as well as graphs and note windows are gathered in a project and can be organized using folders. The built-in... -
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fantail-mlkit
The fantail machine learning toolkit (Moved)
Moved to https://github.com/quansun/fantail-ml -
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Adaptive Gaussian Filtering
Machine learning with Gaussian kernels.
Libagf is a machine learning library that includes adaptive kernel density estimators using Gaussian kernels and k-nearest neighbours. Operations include statistical classification, interpolation/non-linear regression and pdf estimation. For statistical classification there is a borders training feature for creating fast and general pre-trained models that nonetheless return the conditional probabilities. Libagf also includes clustering algorithms as well as comparison and validation... -
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MXLib is a C++ wrapper around the Intel® Integrated Performance Primitives (IPP) library and NVidia NPP CUDA library. You can use either IPP code (or a subset of functions that do not require IPP) on the CPU side, or use NPP/CUDA on the GPU side, or use both together. The function syntax is similar to that found in MatLab and the library is designed to make it easy to port your code from MatLab to C++. The idea is to provide Scientists, Engineers, Researchers and other non full-time...
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A collection of small utilities with a connection to cryptography and cryptanalysis. Written for efficiency and a small memory footprint.
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Weibull-based reliability toolkit for R
R package for Weibull analysis on (life-)time observations.
This is a small R package for doing Weibull-based reliability analysis. This R package is now obsolete and has been superseded by 'project Abernethy' on http://r-forge.r-project.org/projects/abernethy/. -
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ShiVaSmiles
A slick interface to see various scale analysis algorithms in action.
The Shape Visualizer is intented to be an exploratory/educational tool making it quick and easy to see how various mathematical methods perform in analysing and selecting the various patterns present on an image. The main website is built as a wiki gathering info and examples on the various implemented algorithms. The combination of the application and its wiki intend to constitute a nice little pedagogic toolkit for multiscale analysis. Feel free to contribute code or wiki content ! -
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The Automated Parameter Estimation and Model Selection Toolkit is a fast, parallelized MCMC engine written in C for Bayesian inference (parameter estimation and model selection).
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