tick a machine learning library for Python 3. The focus is on statistical learning for time dependent systems, such as point processes. Tick features also tools for generalized linear models, and a generic optimization tools, including solvers and proximal operators for penalization of model weights. It comes also with a bunch of tools for the simulation of datasets.

Show me examples !Inference and simulation of Hawkes processes, with both parametric and non-parametric estimation techniques and flexible tools for simulation.

Inference and simulation of linear models, including among others linear, logistic and Poisson regression, with a large set of penalization techniques and solvers.

Tools for robust inference. It features tools for outliers detection and models such as Huber regression, among others robust losses.

Inference and simulation for survival analysis, including Cox regression with several penalizations.

Proximal operators for penalization of models weights. Such an operator can be used with (almost) any model and any solver.

A module that provides a bunch of state-of-the-art optimization algorithms, including both batch and stochastic solvers

Basic tools for simulation, such as simulation of model weights and feature matrices.

Some plotting utilities used in tick, such as plots for point processes and solver convergence.

Provides easy access to datasets used as benchmarks in tick.

Some tools for preprocessing, such as features binarization and tools for preprocessing longitudinal features.

Some tools computing specific metrics in tick.

How to use tick from the R software.

You want to contribute ? Here you will find many tips.

The full tick API