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Package Name Access Summary Updated
pyreadstat public read SAS, SPSS and STATA files into pandas data frames 2025-08-30
r-modelbased public Implements a general interface for model-based estimations for a wide variety of models (see list of supported models using the function 'insight::supported_models()'), used in the computation of marginal means, contrast analysis and predictions. 2025-08-30
publicsuffixlist public publicsuffixlist implement 2025-08-30
mo_pack public Python wrapper to libmo_unpack 2025-08-30
libhomfly public Library to compute the homfly polynomial of a link. 2025-08-30
pari public PARI/GP is a widely used computer algebra system designed for fast computations in number theory 2025-08-30
kepderiv public Keplerian modeling and derivatives for radial velocities and astrometry analyses. 2025-08-30
cutde public 130 million TDEs per second, Python + CUDA TDEs from Nikkhoo and Walter 2015 2025-08-30
uharfbuzz public Streamlined Cython bindings for the harfbuzz shaping engine 2025-08-30
mathics3 public A general-purpose computer algebra system. 2025-08-30
pymc-extras public A home for new additions to PyMC, which may include unusual probability distribitions, advanced model fitting algorithms, or any code that may be inappropriate to include in the pymc repository, but may want to be made available to users. 2025-08-30
oxlint public Linter for oxc 2025-08-30
technical public Different indicators developed or collected for the Freqtrade 2025-08-30
python-flint public Python bindings for Flint and Arb 2025-08-30
lsst-sphgeom public A spherical geometry library. 2025-08-30
meshpy public Triangular and Tetrahedral Mesh Generator 2025-08-30
cythonbiogeme public C++ part of the Biogeme package 2025-08-30
r-tmle public Targeted maximum likelihood estimation of point treatment effects (Targeted Maximum Likelihood Learning, The International Journal of Biostatistics, 2(1), 2006. This version automatically estimates the additive treatment effect among the treated (ATT) and among the controls (ATC). The tmle() function calculates the adjusted marginal difference in mean outcome associated with a binary point treatment, for continuous or binary outcomes. Relative risk and odds ratio estimates are also reported for binary outcomes. Missingness in the outcome is allowed, but not in treatment assignment or baseline covariate values. The population mean is calculated when there is missingness, and no variation in the treatment assignment. The tmleMSM() function estimates the parameters of a marginal structural model for a binary point treatment effect. Effect estimation stratified by a binary mediating variable is also available. An ID argument can be used to identify repeated measures. Default settings call 'SuperLearner' to estimate the Q and g portions of the likelihood, unless values or a user-supplied regression function are passed in as arguments. 2025-08-30
fpsample public Python efficient farthest point sampling (FPS) library 2025-08-30
polyagamma public Efficiently generate samples from the Polya-Gamma distribution using a NumPy/SciPy compatible interface. 2025-08-30
levenshtein public Python extension for computing string edit distances and similarities. 2025-08-30
librealsense public Cross-platform library for Intel® RealSense™ depth and tracking cameras. 2025-08-30
pyrealsense2 public Cross-platform library for Intel® RealSense™ depth and tracking cameras. 2025-08-30
ambit public J. Turney's C++ library for the implementation of tensor product calculations 2025-08-30
pyambit public J. Turney's C++ library for the implementation of tensor product calculations 2025-08-30

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