Open Source Python Statistics Software - Page 2
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Python Statistics Software
View 202 business solutionsBrowse free open source Python Statistics Software and projects below. Use the toggles on the left to filter open source Python Statistics Software by OS, license, language, programming language, and project status.
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That project aims at studying and comparing typical airline IT methods, for instance RM-related algorithms. It works from a Unix/Linux/Mac command-line, and exposes basic APIs. It is being developed in C++, with Python wrappers for some components.
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MCPower
MCPower — simple Monte Carlo power analysis for complex models
MCPower-GUI is a desktop application that provides a graphical interface for the MCPower Monte Carlo power analysis library. It guides users through the full workflow across three tabs: Model setup (formula input with live parsing, CSV data upload with auto-detected variable types, effect size sliders, and correlation editing), Analysis configuration (find power for a given sample size or find the minimum sample size for a target power, with multiple testing correction and scenario analysis), and Results (interactive charts, exportable tables, and auto-generated Python replication scripts). Supports both standard linear models and mixed-effects models. Additional features include analysis history, configurable scenarios, and built-in documentation. -
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That project aims at providing a clean API and a simple implementation, as a C++ library, of an Airline-related Inventory Management system. That library uses the Standard Airline IT C++ object model (http://sf.net/projects/stdair).
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DEAPathways
Differential Expression Analysis for Pathways
This project contains the source code associated with the PLoS Computational Biology publication: "Differential Expression Analysis for Pathways". The paper text can be found here: http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002967 -
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PGAF provides a framework tuned, user-specific genetic algorithms by handling I/O, UI, and parallelism. It is designed for optimizing functions that take a "very long time" to evaluate.
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TAVIA
Open statistics and scientific graphing
TAVIA is a free and open-source desktop application for statistical analysis, scientific graphing, reproducible workflows, and research project management. It combines editable data tables, statistical tests, regression, dose-response analysis, publication-quality graphs, reports, project history, and reproducibility tools in one workspace. Analyses and graphs remain linked to their source data so results can be refreshed when data changes. TAVIA supports Windows, Linux, and macOS, includes a command-line analysis interface, encrypted project files, plugin support, automatic update checking, and built-in documentation. It is designed for researchers, students, laboratories, and analysts who want a local-first environment for managing data, analysis, visualization, and reporting. Licensed under GPL-3.0-only. -
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Implementation in Python of some of the statistical methods provided by "asurv", the survival analysis software.
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CoLabR
A simple user interface to the R software environment.
A simple user interface to the R software environment. It is designed for people new to R and provides the user with easy access to their variables and data along with pointers to the most basic commands. It also allows for multiple people to share data across different sessions on different machines. In this way it also offers a collaborative environment for people to work together in remote locations. -
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ExoPlanet
GUI based toolkit for running common Machine Learning algorithms.
ExoPlanet provides a graphical interface for the construction, evaluation and application of a Machine Learning model in predictive analysis. With the back-end built using the numpy and scikit-learn libraries, as a toolkit, ExoPlanet couples fast and well tested algorithms, a UI designed over the Qt4 framework, and graphs rendered using Matplotlib to provide the user with a rich interface, rapid analytics and interactive visuals. ExoPlanet is designed to have a minimal learning curve, allowing researchers to focus on the applicative aspect of Machine Learning rather than their implementation details. It provides algorithms for unsupervised and supervised learning, which may be done with continuous or discrete labels. Post analysis, the toolkit further automates building the visual representations for the trained model. -
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GUANO
GUANO - Graphical User interface for performing ANalysis Of variance
Free and open source standalone program capable of conducting between, within, and mixed analyses of variance (ANOVA). Provides a simple graphical user interface for specifying analyses and interaction plots (analyses performed by http://code.google.com/p/pyvttbl/). Features: - Capable of high order factorial designs (> 2 factors) - Within and mixed analyses of variance provide corrections for violations of sphericity (Huynh-Feldt, Greenhouse-Geisser, Box) - A variety of data transformations can be applied (log10, reciprocal, arcsine, square-root, and Windsor) - Generalized eta-squared measures of effect size - Post-hoc power analysis (should match G*Power) - Outputs include tables of estimated marginal means - Up to 4-way interaction plots with errorbars (png, svg) - Confidence intervals account for within-subject variability (where applicable; Loftus and Masson, 1994) - Non-proprietary HTML output files - Non-proprietary codebase Gotchas: - Assumes balanced designs -
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This project hosts tools used for analysis of Gaussian Mixture Distributions (GMDs) which are used for statistical signal processing. The tools are libraries for implementing GMD operations and programs used to analyze properties of GMDs.
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A small simulator for Mendelian genetics, genetic drift, natural selection and random mutations, built on matplotlib and wxpython. A graph will be generated that traces the distribution of genotypes at successive generations.
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This project examines techniques to model three-dimensional rigid body motion using the geometric algebra of Dual Quaternions and how such models compare to more traditional models when used in underconstrained filtering applications.
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HMMLab
A Hidden Markov Model editor with support to HTK
HMMLab is a Hidden Markov Model editor oriented on HMMs for speach recognition. It can create, edit, train and visualize HMMs. HMMLab supports loading/saving HMMs from/to HTK files. -
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This program generates customizable hyper-surfaces (multi-dimensional input and output) and samples data from them to be used further as benchmark for response surface modeling tasks or optimization algorithms.
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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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Noisy Channel Simulator
Demonstrate errors in transmission of a file over a noisy channel.
This program was written to dimonstrate errors in transmission for a presentation on Claude Shannon's Noisy Channel Coding Theorem. It takes an input file, the probability of a bit being flipped, and, if specified, the size of the header of the file. The program was intended to take monochrome bitmap files as input, so that each bit refers to a pixel in the image and thus, it would be easy to see errors in the output file, as some of the pixels would be flipped; however, it will work on any input file. -
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PyMC
Bayesian Modeling and Probabilistic Programming in Python
PyMC is a Python library for probabilistic programming focused on Bayesian statistical modeling and machine learning. Built on top of computational tools like Aesara and NumPy, PyMC allows users to define models using intuitive syntax and perform inference using MCMC, variational inference, and other advanced algorithms. It’s widely used in scientific research, data science, and decision modeling. -
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That project aims at studying and comparing typical airline IT methods, for instance RM-related algorithms. It works from a Unix/Linux/Mac command-line, and exposes basic APIs. It is being developed in C++, with Python wrappers for some components.
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VAERity
Uncovering truth in data
VAERity is a free, open source tool to graphically explore the VAERS data set. It aims to eventually expand in scope to allow fast querying of arbitrary large datasets. It utilizes vaex and pandas as required to provide a balance of speed and query flexibility. -
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allink
Software for data analysis, image processing, simulations, solver.
Collection of utilities based on two basics classes: Matematica and VarData. Matematica) performs math operations on vectors and matrices for smoothing, interpolation, convolution, image processing... VarData) manipulate a structure of points connected by links. Addraw) openGL engine. ElPoly) analyze mechanical properties of polymer and membrane like structures. Addyn) perform molecular dynamics and Monte Carlo simulations and has a solver for 4th oder PDE. Avvis) perform all the operation of Matematica on different sets of data visualized on a Qt graphical interface. DrImage) image manipulation on the Matematica filters. The program is intended to use as less as possible external libs (optional: openGL, gsl, fftw, cgal, png, tiff, boost, MPI, Qt...). -
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dAnalytics
dAnalytics is a software made for education in discriminant analysis.
dAnalytics is a software made for educational purposes to work with discriminant analysis. It's a very simple calculator over bi-dimensional populations. You input a file with two columns (one for each variable) and tryout different separations of populations (blue and red) and analyze it's efficiency by observing the eigen value result for the attempted separation. It also gives many information about the statistics both of the input and the separated populations. There's a documentation folder in the software which brings a quick start guide for this software. -
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fpyprog
Program for statistic and visual analysis
Example of program for statistic and visual analysis on Python -
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portas
Pequeno script em Python para provar o problema de Monty Hall
O jogo consiste no seguinte: Monty Hall (o apresentador) apresentava 3 portas aos concorrentes, sabendo que atrás de uma delas está um carro (prémio bom) e que as outras têm prêmios de pouco valor. Na 1ª etapa o concorrente escolhe uma porta (que ainda não é aberta); De seguida Monty abre uma das outras duas portas que o concorrente não escolheu, sabendo à partida que o carro não se encontra aí; Agora com duas portas apenas para escolher — pois uma delas já se viu, na 2ª etapa, que não tinha o prêmio — e sabendo que o carro está atrás de uma delas, o concorrente tem que se decidir se permanece com a porta que escolheu no início do jogo e abre-a ou se muda para a outra porta que ainda está fechada para então a abrir. Qual é a estratégia mais lógica? Ficar com a porta escolhida inicialmente ou mudar de porta? Com qual das duas portas ainda fechadas o concorrente tem mais probabilidades de ganhar? Por quê?
