Search Results for "python to executable file"

TEACUP automates many aspects of running TCP performance experiments in a specially-constructed physical testbed. TEACUP enables repeatable testing of different TCP algorithms over a range of emulated network path conditions, bottleneck rate limits and bottleneck queuing disciplines. TEACUP utilises a text-based configuration file to define experiments as combinations of parameters specifying desired network path and end host conditions. When multiple values are provided (e.g. for TCP...

A large repository of open-source programs built for Cygwin, including X11 desktops, language interpreters, multimedia frameworks, cross-compiler toolchains, and much more. Also hosted here is the cygport tool for building Cygwin packages. cygport releases and the Ports Git repositories are hosted here; Ports packages are available from the website.

This project is a graphical user interface to the R statistical programming language designed for use in Clinical Chemistry. It allows the user to perform Passing Bablok, Deming and Linear Regression and to produce high quality images in any file format for publication. The front end is written in Python 3.3 and PyQt4 and the form was designed using Qt4 Designer. The statistical analysis is written in R. The compiled binary was made with cx_freeze. This software is free and open-source...

RIPE (Regulatory network Inference from joint Perturbation and Expression data) is a novel three-step method that integrates both perturbation data and steady state gene expression data in order to estimate a regulatory network. The ripe package is written in R, with additional functionality provided by a MATLAB executable file. The executable file uses a runtime engine called the MATLAB Compiler Runtime (MCR). The executable for different architectures is distributed on this site together...

Hoea is a python module for hierarchical ontology enrichment analysis, which facilitated GO (Gene Ontology)/KO (KEGG Orthology) enrichment analysis at any desktop.

ConDEnSE (Confidential Data Enabled Statistical Exploration) will be a web-based environment for statistical analysis of confidential data from various database sources, based on Plone and R, and using the Jackknife method of confidentiality protection.