|
pymatgen-analysis-defects
|
public |
Defect analysis modules for pymatgen
|
2025-09-27 |
|
eodag-server
|
public |
Earth Observation Data Access Gateway
|
2025-09-27 |
|
eodag
|
public |
Earth Observation Data Access Gateway
|
2025-09-27 |
|
psycopg-c
|
public |
PostgreSQL database adapter for Python
|
2025-09-27 |
|
postgis
|
public |
PostGIS adds geometry, geography, raster and other types to the PostgreSQL database.
|
2025-09-27 |
|
google-cloud-aiplatform
|
public |
Vertex AI API client library
|
2025-09-27 |
|
alibabacloud-adb20211201
|
public |
Alibaba Cloud adb (20211201) SDK Library for Python
|
2025-09-27 |
|
facebook_business
|
public |
Facebook Business SDK
|
2025-09-27 |
|
python-blosc2
|
public |
A fast & compressed ndarray library with a flexible computational engine.
|
2025-09-27 |
|
weaviate-client
|
public |
A python native Weaviate client
|
2025-09-27 |
|
r-rrum
|
public |
Implementation of Gibbs sampling algorithm for Bayesian Estimation of the Reduced Reparameterized Unified Model ('rrum'), described by Culpepper and Hudson (2017) <doi: 10.1177/0146621617707511>.
|
2025-09-27 |
|
r-edina
|
public |
Perform a Bayesian estimation of the exploratory deterministic input, noisy and gate (EDINA) cognitive diagnostic model described by Chen et al. (2018) <doi:10.1007/s11336-017-9579-4>.
|
2025-09-27 |
|
r-distr6
|
public |
An R6 object oriented distributions package. Unified interface for 42 probability distributions and 11 kernels including functionality for multiple scientific types. Additionally functionality for composite distributions and numerical imputation. Design patterns including wrappers and decorators are described in Gamma et al. (1994, ISBN:0-201-63361-2). For quick reference of probability distributions including d/p/q/r functions and results we refer to McLaughlin, M. P. (2001). Additionally Devroye (1986, ISBN:0-387-96305-7) for sampling the Dirichlet distribution, Gentle (2009) <doi:10.1007/978-0-387-98144-4> for sampling the Multivariate Normal distribution and Michael et al. (1976) <doi:10.2307/2683801> for sampling the Wald distribution.
|
2025-09-27 |
|
fs.googledrivefs
|
public |
Pyfilesystem2 implementation for Google Drive
|
2025-09-27 |
|
r-neonutilities
|
public |
Utilities for Working with NEON Data
|
2025-09-27 |
|
r-autoslider.core
|
public |
The normal process of creating clinical study slides is that a statistician manually type in the numbers from outputs and a separate statistician to double check the typed in numbers. This process is time consuming, resource intensive, and error prone. Automatic slide generation is a solution to address these issues. It reduces the amount of work and the required time when creating slides, and reduces the risk of errors from manually typing or copying numbers from the output to slides. It also helps users to avoid unnecessary stress when creating large amounts of slide decks in a short time window.
|
2025-09-27 |
|
mcp
|
public |
Model Context Protocol SDK
|
2025-09-27 |
|
openturns
|
public |
Uncertainty treatment library
|
2025-09-27 |
|
dulwich
|
public |
Python Git Library
|
2025-09-27 |
|
r-soildb
|
public |
A collection of functions for reading data from USDA-NCSS soil databases.
|
2025-09-27 |
|
py-rattler
|
public |
A blazing fast library to work with the conda ecosystem
|
2025-09-27 |
|
pyzstd
|
public |
Python bindings to Zstandard (zstd) compression library
|
2025-09-27 |
|
py_rattler
|
public |
A blazing fast library to work with the conda ecosystem
|
2025-09-27 |
|
r-networkcomparisontest
|
public |
This permutation based hypothesis test, suited for Gaussian and binary data, assesses the difference between two networks based on several invariance measures (e.g., network structure invariance, global strength invariance, edge invariance). Network structures are estimated with l1-regularized partial correlations (Gaussian data) or with l1-regularized logistic regression (eLasso, binary data). Suited for comparison of independent and dependent samples. For dependent samples, only supported for data of one group which is measured twice. See van Borkulo et al. (2017) <doi:10.13140/RG.2.2.29455.38569>.
|
2025-09-27 |
|
r-bootnet
|
public |
Bootstrap methods to assess accuracy and stability of estimated network structures and centrality indices <doi:10.3758/s13428-017-0862-1>. Allows for flexible specification of any undirected network estimation procedure in R, and offers default sets for various estimation routines.
|
2025-09-27 |
