Open Source Windows Statistics Software - Page 3
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Statistics Software for Windows
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Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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mathSuite
Powerful Calculus Environment and Matrices Handling Engine
mathSuite is a very powerful Mathematical Suite which deals principally with complex algebric and geometric operations. It is powered by the fabulous ExprEval C Parser. The main purpose of this project is fast math-oriented algorithm virtualization, with an optimized direct text interface. Also it gives you a very powerful and fast Calculus Environment which let you handle easily interchangeable variable lists, matrices, LOGS and Settings Layouts with optimized Items Lists Managing Engine. You'll also be able to execute your own scriptfiles with a basic math-oriented beta script language. Some feature regarding Matrices and Linear Algebra Operations are LU-Factorization, SVD Decomposition, Rank Calculator, Ill Condition Checking and more... There are also a very powerful Linear System Solver and Basic PRELoaded Functions Integrator Engine. -
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A2RMS Algorithm
Implementation of the A2RMS Algorithm in Matlab
Implementation of the A2RMS Algorithm for univariate densities defined for real values. -
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BACE for gretl
Bayesian Averaging of Classical Estimates
Bayesian Averaging of Classical Estimates package. -
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DataPrep
Python-based data preprocessing tool
DataPrep v0.2 is a Tkinter-based GUI application/tool designed to assist users in data preprocessing, multicollinearity removal, and feature selection for a wide range of applications in Cheminformatics, Bioinformatics, Data Analysis, Feature Selection, Molecular Modeling, Machine Learning, and Quantitative-structure-property relationship (QSPR) studies. It includes functionality to load, process, and save datasets with support for different preprocessing & multicollinearity removal strategies with customizable parameter setting options. -
AI-generated apps that pass security reviewRetool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.
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Immutable Sparse Wave Trees (WaveTree)
Realtime bigdata tool for bit strings up to 2^63 based on AVL forest
Realtime bigdata tool at the bit level based on immutable AVL forest which can be run in memory or, in future versions, as a merkle forest like a blockchain. Main object is a sparse bit string (Bits) that efficiently scales up to 2^63 bits normally compressed as forest has duplicated substrings. Bits objects support reading bit, byte, short, int, or long (Java primitives) at any bit index in 64 bit range. Example: instead of building a class to hold a header and then data, represent all of that as Bits, subranges of them, and ints for sizes of its parts. Expansion ability for other kinds of compression, since Bits is a Java interface. Main functions on bits are substring, concat, number of 0 or 1 bits, and number of bits (size). All those operations can be done millions of times per second regardless of size because the AVL forest reuses existing branches recursively. Theres a scalar (originally for copy/pasting subranges of sounds) and a bit Java package. Sparse n dimensional matrix. -
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LDT
Automatic Time Series Analysis with Stationary VAR Models
LDT is designed for automatic time-series analysis. Current version focuses on stationary vector autoregressive models (VAR) and the related analyses such as forecasting and Granger causality. See the following paper for an application: See http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2818213 -
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1. Create an object-oriented python script that can represent mathematical concepts and their properties. 2. Represent all numeric values exactly. 3. Provide a variety of formats to export or embed representations of the mathematical concepts.
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Math Simulator
A simulation software for numerical techniques.
Math Simulator is a java-based application which simulates various mathematical techniques.It is extremely useful for the Engineering students and for professionals. Most importantly, unlike most others Math Simulator is an Application rather than a java - library. So, user is not required to have any computer background. A Unique feature of Math Simulator is that it allows you to save queries for later reference. Hence you can save the problems along with their results. -
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SPSS i/o in Delphi
Delphi class for reading and writing SPSS SAV files
The TSPSSio class can be used to read a SAV file produced by IBM’s SPSS application, in uncompressed or byte-compressed format. It will allow you to write a SAV file in byte-compressed format. (Byte-compressed format is the defacto standard.) The class and its supporting software is written in Delphi XE and can be compiled without the need for external units. -
Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
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SourceForge-Project-Tracker-Lite
App that allows you to track progress of your sourceforge project.
App that allows you to track progress of your sourceforge project on mobile. Shows stats in real time. -
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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 and Peter Bruce as a new way to teach Statistics to social science students. Of course, social science students aren't the only ones who can benefit. Anyone who wants to learn statistics will find that the resampling approach helps in understanding statistical concepts from the simplest to the most difficult. In addition, professionals who want to use resampling, bootstrapping, or Monte Carlo simulations will find Statistics101 helpful. More information at https://statistics101.sourceforge.io/ -
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minepy
Maximal Information-based Nonparametric Exploration
The minepy homepage has moved to http://minepy.readthedocs.io. The download page is now at https://github.com/minepy/minepy/releases. -
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Implementation in Python of some of the statistical methods provided by "asurv", the survival analysis software.
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ADaMSoft
Open Source and data mining software
ADaMSoft is a free and Open Source Data Mining software developed in Java. It contains data management methods and it can create ready to use reports. It can read data from several sources and it can write the results in different formats. -
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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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ARNU
Arredondamento de Números
Sistema de Apoio Didático Profissional. ARNU - Arredondamento de Números. O aplicativo ARNU tem por objetivo auxiliar na resolução de cálculos de arredondamento matemático, abordados de forma técnica, entregando ao usuário um resultado didático e objetivo, o motivo e as normas utilizadas. -
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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 routines. It is written in C++. -
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A alternative to the SAS Viewer written in SAS/AF. First presented on 2010-10-05 in BASAS meeting. With extended abilities in sorting, filtering, simple frequency count, statistics and various data review oriented features.
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A collection of compact, powerful programs and functions, written in C, for the analysis and transformation of data. No object or structure dependencies, no fancy interfaces - just good tools.
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Assisted dRawing toOl
ARO helps physically disabled students to create mathematical drawings
ARO has been developed to assist physically disabled or injured students who cannot create mathematical drawings on paper. It is intended to make maths up to Key Stage 4 more accessible. AQA has agreed to its use in a GCSE maths exam. Anyone who can use a computer with a mouse to access the internet should be able to use ARO. The processes required mimic those of on paper drawing as far as possible. Possible applications include: Graphs of straight lines and smooth curves Compass and ruler constructions Scatter graphs Stem and leaf displays Box plots Histograms Pie charts Simple diagrams. The structure of each drawing is built from individual components which can be placed on the drawing grid without great accuracy. Each can then be positioned exactly using onscreen buttons. The ruler and the protractor can be aligned with any straight line. Drawings can be created on imported background bitmaps. The author was a mathematics lecturer at Plymouth University. -
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BIL++ is a set of standalone C++ packages for data processing in Bioinformatics (Graph mining, Bayesian networks, Genetic algorithm, Discretization, Gene expression data analysis, Hypothesis testing).
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Benchee
Easy and extensible benchmarking in Elixir
Library for easy and nice (micro) benchmarking in Elixir. Benchee allows you to compare the performance of different pieces of code at a glance. It is also versatile and extensible, relying only on functions. There are also a bunch of plugins to draw pretty graphs and more! Benchee runs each of your functions for a given amount of time after an initial warmup, it then measures their run time and optionally memory consumption. It then shows different statistical values like average, standard deviation etc. The aforementioned plugins like benchee_html make it possible to generate nice-looking HTML reports, where individual graphs can also be exported as PNG images. first runs the functions for a given warmup time without recording the results, to simulate a "warm"/running system. Plugin/extensible-friendly architecture so you can use different formats to display benchmarking results as HTML, markdown, JSON, and more. -
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Binomial Distribution Calculator
A simple to use Binomial Distribution Calculator.
A simple to use binomial distribution calculator: Just enter the sufficient data like number of trials, probability and number of successes. You can display the calculated data in a table or even graphically. Simultaneously, this application computes the expected value and the standard deviation. To use this app you need to have .NET Framework 4 installed. Contact me: lambdapew-dev@yahoo.de -
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BiomeNet
BAYESIAN INFERENCE OF METABOLIC DIVERGENCE AMONG MICROBIAL COMMUNITIES
Metagenomics yields enormous numbers of microbial sequences that can be assigned a metabolic function. Using such data to infer community-level metabolic divergence is hindered by the lack of a suitable statistical framework. Here, we describe a novel hierarchical Bayesian model, called BiomeNet (Bayesian inference of metabolic networks), for inferring differential prevalence of metabolic networks among microbial communities. To infer the structure of community-level metabolic interactions, BiomeNet applies a mixed-membership modelling framework to enzyme abundance information. The basic idea is that the mixture components of the model (metabolic reactions, subnetworks, and networks) are shared across all groups (microbiome samples), but the mixture proportions vary from group to group. Through this framework, the model can capture nested structures within the data. BiomeNet is unique in modeling each metagenome sample as a mixture of complex metabolic systems (metabosystems).
