SOFA is a statistics, analysis, and reporting program with an emphasis on ease of use, learn as you go, and beautiful output.
Machine Learning Python
mlpy is a Python module for Machine Learning built on top of NumPy/SciPy and of GSL. mlpy provides high-level functions and classes allowing, with few lines of code, the design of rich workflows for classification, regression, clustering and feature selection. mlpy is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License version 3. mlpy is available both for Python >=2.6 and Python 3.X.
Model-based Analysis of Genome-wide CRISPR-Cas9 Knockout
Model-based Analysis of Genome-wide CRISPR-Cas9 Knockout (MAGeCK) is a computational tool to identify important genes from the recent genome-scale CRISPR-Cas9 knockout screens technology. For instructions and documentations, please refer to the wiki page. MAGeCK is developed and maintained by Wei Li and Han Xu from Dr. Xiaole Shirley Liu's lab at Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health. We thank the support from Claudia Adams Barr Program in Innovative Basic Cancer Research to develop MAGeCK.
Please participate in the SURVEY on rgedit's future: https://www.surveymonkey.com/s/VNMMJMJ your answers are much appreciated! Gedit (Gnome editor, www.gedit.org) plug-in allowing it to become an easy-to-use and yet light-weight IDE for the statistical programming environment, R (www.r-project.org).
SalStat is a small application for statistical analysis emphasising the sciences and social sciences (particularly Psychology). The project is designed around the user interface which has been designed to be simple to use. Think SPSS, but better!
psignifit is a toolbox to fit psychometric functions and test hypotheses on psychometric data. This is version 3 which will now predominantly support python.
Uranie is CEA's uncertainty analysis platform, based on ROOT
Uranie is a sensitivity and uncertainty analysis plateform based on the ROOT framework (http://root.cern.ch) . It is developed at CEA, the French Atomic Energy Commission (http://www.cea.fr). It provides various tools for: - data analysis - sampling - statistical modeling - optimisation - sensitivity analysis - uncertainty analysis - running code on high performance computers - etc. Thanks to ROOT, it is easily scriptable in CINT (c++ like syntax) and Python.
MinimPy is a desktop application program for sequential allocation of subjects to treatment groups in clinical trials by using the method of minimisation. Comprehensive reference help is available at: http://minimpy.sourceforge.net For those who have difficulty installing MinimPy, an online version is available at: http://qminim.sourceforge.net MinimPy has been full described in the foolowing article: Saghaei, M. and Saghaei, S. (2011) Implementation of an open-source customizable minimization program for allocation of patients to parallel groups in clinical trials. Journal of Biomedical Science and Engineering, 4, 734-739. doi: 10.4236/jbise.2011.411090. Available at: http://www.scirp.org/journal/PaperInformation.aspx?PaperID=8518
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.
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.
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).
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
Software for speech research. It includes programs and libraries for signal processing, along with general purpose scientific libraries. Most of the code is in Python, with C/C++ supporting code. Also, contains code releases corresponding to publishe
A python based Bayesian network implementation. This project aims to provide a single point of entry-solution for searching through available networks matching data and optimizing CPT's.
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.
That project aims at providing a clean API and a simple implementation, as a C++ library, of a Travel-oriented fare engine. It corresponds to the simulated version of the real-world Fare Quote System.
That project aims at providing a clean API and a simple implementation, as a C++ library, of a Travel-oriented Distribution System. It corresponds to the simulated version of the real-world Computerized Reservation Systems (CRS).
MLE survival analysis: Gompertz, Weibull, Logistic and mixed morality.
DeDAY (Demography Data Analyses) is a tool of analyzing demography data. It supports Gompertz, Weibull and Logistic distributions. DeDay also supports mixed mortality models based on these distribution such as the Gompertz-Makeham distribution. Distributions such as Gompertz describes only age-dependent mortality, which increases over time. Mixed mortality models, such as in Gompertz-Makeham distribution, consider a more general case where mortality is consist of both age-dependent and in-dependent mortality. Mixed models partition mortality into exogenous and endogenous components, so that the intrinsic survivorship can be estimated without the interference from extrinsic noise. DeDAY supports both interval-censored data and exact event-time data. Using MLE (Maximum Likelihood Estimate), DeDAY fits statistic model to the data. DeDAY also calculates the variances and the multi-dimensional confidence limits of model parameters. DeDAY is free for academic users.
Facinas: Probabilistic Graphical Models is an extensive set of librairies, algorithms and tools for Probabilistic Inference and Learning and Reasoning under uncertainty. It implements all sort of Probabilistic Graphical Models using discrete and continuous distributions.
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.
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.
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.
A general recommender system with basic models and MRA
Multi-categorization Recommendation Adjusting (MRA) is to optimize the results of recommendation based on traditional(basic) recommendation models, through introducing objective category information and taking use of the feature that users always get the habits of preferring certain categories. Besides this, there are two advantages of this improved model: 1) it can be easily applied to any kind of existing recommendation models. And 2) a controller is set in this improved model to provide controllable adjustment range, which thereby makes it possible to provide optional modes of recommendation aiming different kinds of users.
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.
Set your statistical data free!
Manage statistical data using an editor written in the open source Python programming language and save files in a portable CSV format.