+
+

Related Products

  • Teradata VantageCloud
    1,122 Ratings
    Visit Website
  • Google Cloud Run
    347 Ratings
    Visit Website
  • SurveyJS
    62 Ratings
    Visit Website
  • JOpt.TourOptimizer
    10 Ratings
    Visit Website
  • TinyPNG
    60 Ratings
    Visit Website
  • Docket
    59 Ratings
    Visit Website
  • Docmosis
    51 Ratings
    Visit Website
  • Pipedrive
    10,456 Ratings
    Visit Website
  • Apify
    1,441 Ratings
    Visit Website
  • 11x
    69 Ratings
    Visit Website

About

Ruffus is a computation pipeline library for python. It is open-sourced, powerful and user-friendly, and widely used in science and bioinformatics. Ruffus is designed to allow scientific and other analyses to be automated with the minimum of fuss and the least effort. Suitable for the simplest of tasks. Handles even fiendishly complicated pipelines which would cause make or scons to go cross-eyed and recursive. No "clever magic", no pre-processing. Unambitious, the lightweight syntax which tries to do this one small thing well. Ruffus is available under the permissive MIT free software license. This permits free use and inclusion even within proprietary software. It is good practice to run your pipeline in a temporary, “working” directory away from your original data. Ruffus is a lightweight python module for building computational pipelines. Ruffus requires Python 2.6 or higher or Python 3.0 or higher.

About

statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests and statistical data exploration. An extensive list of result statistics is available for each estimator. The results are tested against existing statistical packages to ensure that they are correct. The package is released under the open-source Modified BSD (3-clause) license. statsmodels supports specifying models using R-style formulas and pandas DataFrames. Have a look at dir(results) to see available results. Attributes are described in results.__doc__ and results methods have their own docstrings. You can also use numpy arrays instead of formulas. The easiest way to install statsmodels is to install it as part of the Anaconda distribution, a cross-platform distribution for data analysis and scientific computing. This is the recommended installation method for most users.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Professionals in science and bioinformatics seeking a computation pipeline library solution

Audience

Users and anyone in search of a solution to calculate the estimation of many different statistical models

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

Free
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

ruffus
www.ruffus.org.uk

Company Information

statsmodels
www.statsmodels.org/stable/index.html

Alternatives

Thoa

Thoa

Thoa.io

Alternatives

Categories

Categories

Integrations

Python
Anaconda

Integrations

Python
Anaconda
Claim ruffus and update features and information
Claim ruffus and update features and information
Claim statsmodels and update features and information
Claim statsmodels and update features and information