Statistical computation and diagnostics 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.

This package provides a toolbox implementing statistical methods to fit heavy-tailed distributions like power laws.

Mixmod is a software package for Model-Based supervised and unsupervised classification on qualitative, quantitative and mixed data.

Main Statistical functionalities:

• Likelihood maximization with EM, CEM and SEM algorithm

• Parsimonious models

• Selection criteria: BIC, ICL, NEC, CV

This package performs alignment, PCA, and modeling of multidimensional and unidimensional functions using the square-root velocity framework. This framework allows for elastic analysis of functional data through phase and amplitude separation.

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.

pca is a python package to perform PCA and to create insightful plots. The core of PCA is built on sklearn functionality to find maximum compatibility when combining with other packages. But this PCA package can do a lot more. Besides the regular Principal Components, it can also perform SparsePCA, TruncatedSVD, and provide you with the information that can be extracted from the components.

OpenTURNS is a scientific C++ and Python library including an internal data model and algorithms dedicated to the treatment of uncertainties. The main goal of this library is giving to specific applications all the functionalities needed to treat uncertainties in studies.

caerus is Python package to compute the local-minima with the corresponding local-maxima within the given time-frame. This approach is designed to for stock-market valley and peak detection.

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

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.

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.

This package provides a Pytorch/Captum/Tensorflow implementation of Cross-Domain Saliency Maps. The method does not require any model model retraining or modications.

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.

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

pyEntropy is a lightweight library built on top of NumPy that provides functions for computing various types of entropy for time series analysis.

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 high-performance algorithm implementations to build visibility graphs from time series data.

This package provides utilities related to the detection of peaks on 1D data. Includes functions to estimate baselines, finding the indexes of peaks in the data and performing Gaussian fitting or centroid computation to further increase the resolution of the peak detection.

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

Nolds is a small numpy-based library that provides an implementation and a learning resource for nonlinear measures for dynamical systems based on one-dimensional time series.

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

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

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.