NASA-Planetary-Science / sbpy

sbpy is an astropy affiliated package for small-body - asteroid and comet - researchers. It provides functionality that is tailored to the needs of this community, including functionality related...

DOI pending

kellylab / Fireworks

Fireworks is a batch-processing library that provides a simple, modular way to compose preprocessing pipelines for training machine learning models with PyTorch. It facilitates the traditionally...

DOI pending

fboehm / qtl2pleio

R package with functions to perform multivariate, multi-dimensional quantitative trait locus scans in model organism genetics studies.

DOI pending

nvihrs14 / tcherry

The package is meant for learning the structure of the type of graphical models called t-cherry trees from data. The purpose of the package is only to provide functions for learning the structure...

DOI pending

benmaier / netwulf

Python users who work with networks have few good options for visualizing their networks. To date, the most straight-forward thing you can do is use the draw function in networkx, but this...

DOI pending

USCbiostats / fmcmc

A friendly (flexible) MCMC framework that implements multiple chains in parallel computing, automatic stop using convergence monitoring, and user-defined transition kernel functions.

DOI pending

easystats / insight

insight is an R package, aiming to provide an easy, intuitive and consistent accesss to information contained in various models, like model formulas, model terms, information about random effects,...

DOI pending

indigo-dc / DEEPaaS

DEEPaaS API is a Python package that provides a REST API that can be used to easily expose the underlying model functionality over HTTP. Developers can plug their
applications into DEEPaaS API...

DOI pending

osorensen / hdme

R package containing implementations of several modern methods for measurement error correction in regression problems with a large number of variables.

prmiles / pymcmcstat

The Python package pymcmcstat is a tool for performing Bayesian inference on a wide variety of scientific problems in order to quantify model input uncertainty.