raamana Goto Github PK

2018-web

acronym

ACRONYM (Acronym CReatiON for You and Me)

afni

Official AFNI source and documentation

aif360

A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

antspy

Advanced Normalization Tools in Python

auto-sklearn

Automated Machine Learning with scikit-learn

autokeras

accessible AutoML for deep learning.

awesome-machine-learning

A curated list of awesome Machine Learning frameworks, libraries and software.

ax

Adaptive Experimentation Platform

braindrles

Its like Tinder, for Brains with Lesions!

categorical-encoding

A library of sklearn compatible categorical variable encoders

causallib

A Python package for modular causal inference analysis and model evaluations

cca_zoo

Canonical Correlation Analysis Model Zoo: Standard: CCA, GCCA, MCCA, TCCA, KCCA, TKCCA, sparse CCA , ridge CCA and elastic CCA, PMD, PLS. Deep: DCCA, DMCCA, DGCCA, DTCCA. DVCCA, DCCAE, SplitAE. Probabilistic: VBCCCA. With simulated data generation and toy datasets.

cli-guidelines

A guide to help you write better command-line programs, taking traditional UNIX principles and updating them for the modern day.

confounds

Conquering confounds and covariates: methods, library and guidance

conwhat

connectome-based white matter atlases for virtual lesion studies

cookiecutter-pypackage

Cookiecutter template for a Python package.

covid-sim

cpython

The Python programming language

cvxpy

A Python-embedded modeling language for convex optimization problems.

datalad

Keep scientific data under control with git and git-annex

dcmstack

DICOM to Nifti conversion with meta data preservation

decodingtrust

A Comprehensive Assessment of Trustworthiness in GPT Models

dependents

Navigate front-end codebases in Sublime Text 2/3

dicom_parser

Facilitates DICOM data access.

dipy

Diffusion MR Imaging in Python

dowhy

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

duecredit

Automated collection and reporting of citations for used software/methods/datasets

eli5

A library for debugging/inspecting machine learning classifiers and explaining their predictions

entrofy

Participant selection for workshops and conferences made easy