formulae is a Python library that implements Wilkinson's formulas for mixed-effects models. The main difference with other implementations like Patsy or formulaic is that formulae can work with formulas describing a model with both common and group specific effects (a.k.a. fixed and random effects, respectively).

Statistical computation and diagnostics for ArviZ.

Implements iterative statistics operators for mean, variance, high-order moments, extrema, covariance, threshold, quantile (experimental) and Sobol' indices.

Modular plotting for ArviZ.

Bambi is a high-level Bayesian model-building interface written in Python. It works with the PyMC probabilistic programming framework and is designed to make it extremely easy to fit Bayesian mixed-effects models common in biology, social sciences and other disciplines.

Base ArviZ features and converters.

This package provides Kullback-Leibler projections for Bayesian model selection. Variable selection refers to the process of identifying the most relevant variables in a model from a larger set of predictors. When performing this process, we usually assume that variables contribute unevenly to the outcome, and we want to identify the most important ones. Sometimes we also care about the order in which variables are included in the model.

skpro is a unified framework for tabular probabilistic regression, time-to-event prediction, and probability distributions in Python.

It provides scikit-learn-like, scikit-base compatible interfaces to:

• tabular supervised regressors for probabilistic prediction

• tabular probabilistic time-to-event and survival prediction

• metrics to evaluate probabilistic predictions

• reductions to turn scikit-learn regressors into probabilistic skpro regressors

• building pipelines and composite models

• symbolic probability distributions

PyTorch Forecasting is a PyTorch-based package for forecasting with state-of-the-art deep learning architectures. It provides a high-level API and uses PyTorch Lightning to scale training on GPU or CPU, with automatic logging.

This is a Python library for time series data mining. It provides tools for time series classification, clustering and forecasting.

sktime is a library for time series analysis in Python. It provides a unified interface for multiple time series learning tasks. Currently, this includes forecasting, time series classification, clustering, anomaly/changepoint detection, and other tasks. It comes with time series algorithms and scikit-learn compatible tools to build, tune, and validate time series models.

The package provides systematic time-series feature extraction by combining established algorithms from statistics, time-series analysis, signal processing, and nonlinear dynamics with a robust feature selection algorithm. In this context, the term time-series is interpreted in the broadest possible sense, such that any types of sampled data or even event sequences can be characterised.

skbase provides base classes for creating scikit-learn-like parametric objects, along with tools to make it easier to build your own packages that follow these design patterns.

This package provides a Python implementation of catch22, a collection of 22 time-series features.

Skforecast is a Python library for time series forecasting using statistical and machine learning models. It works with any estimator compatible with the scikit-learn API, including popular options like LightGBM, XGBoost, CatBoost, Keras, and many others.

This package provides a powerful and scalable library that can be used for a variety of time series data mining tasks.

This package provides a Python library for Empirical Mode Decomposition and related spectral analyses.

aeon is an open-source toolkit for time series machine learning. Fully compatible with scikit-learn, it brings together the latest machine learning methods alongside a wide range of classical approaches for tasks such as forecasting, clustering, and classification.

This project is an sklearn extension for machine learning time series or sequences. It provides an integrated pipeline for segmentation, feature extraction, feature processing, and a final estimator compatible with sklearn model evaluation and parameter optimization tools. Seglearn provides a flexible approach to multivariate time series and contextual data for classification, regression, and forecasting problems. Support and examples are provided for learning time series with classical machine learning and deep learning models.

Tadasets provides various utilities for creating and loading data sets that are useful for Topological Data Analysis. Currently, we provide several synthetic data sets with particular topological features.

This package provides a library implementing the Mapper algorithm in Python. KeplerMapper can be used for visualization of high-dimensional data and 3D point cloud data. KeplerMapper can make use of Scikit-Learn API compatible cluster and scaling algorithms.

Scikit-TDA is a home for Topological Data Analysis Python libraries intended for non-topologists. This project aims to provide a curated library of TDA Python tools that are widely usable and easily approachable. It is structured so that each package can stand alone or be used as part of the scikit-tda bundle.

This package provides Python bindings for PHAT, a software library which contains methods for computing the persistence pairs of a filtered cell complex represented by an ordered boundary matrix with Z2 coefficients.