Tick: a Python library for statistical learning, with a particular emphasis on time-dependent modelling

TL;DR

Tick is a Python library specialized in time-dependent statistical models, offering fast, efficient optimization tools for point processes, generalized linear models, and survival analysis, suitable for single-node multi-core environments.

Contribution

The library provides a comprehensive, high-performance Python toolkit for time-dependent statistical modeling, integrating C++ optimized algorithms for rapid computations.

Findings

High computational efficiency due to C++ implementation

Supports a wide range of time-dependent models

Enables fast, scalable statistical learning in Python

Abstract

Tick is a statistical learning library for Python~3, with a particular emphasis on time-dependent models, such as point processes, and tools for generalized linear models and survival analysis. The core of the library is an optimization module providing model computational classes, solvers and proximal operators for regularization. tick relies on a C++ implementation and state-of-the-art optimization algorithms to provide very fast computations in a single node multi-core setting. Source code and documentation can be downloaded from https://github.com/X-DataInitiative/tick