A Julia machine learning framework
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Updated
Sep 2, 2024 - Julia
Statistics is a mathematical discipline concerned with developing and studying mathematical methods for collecting, analyzing, interpreting, and presenting large quantities of numerical data. Statistics is a highly interdisciplinary field of study with applications in fields such as physics, chemistry, life sciences, political science, and economics.
A Julia machine learning framework
A Julia package for probability distributions and associated functions.
⚡ Single-pass algorithms for statistics
Generalized linear models in Julia
Basic statistics for Julia
An extensible framework for geospatial data science and geostatistical modeling fully written in Julia
A Julia package for fitting (statistical) mixed-effects models
"Distributions" that might not add to one.
A Julia package for multivariate statistics and data analysis (e.g. dimension reduction)
Hypothesis tests for Julia
StateSpaceModels.jl is a Julia package for time-series analysis using state-space models.
A Bayesian Analysis Toolkit in Julia
Kernel density estimators for Julia
Convenience meta-package to load essential packages for statistics
Beautiful and flexible vizualizations of high dimensional data
Arrays for working with categorical data (both nominal and ordinal)
Statistical bootstrapping library for Julia
Transforms and pipelines with tabular data in Julia
An API for dispatching on the "scientific" type of data instead of the machine type