Looking for something like GLM? Here are 24 alternatives, ranked by how closely they match it on purpose, features and platforms. Of these, 17 are open source.
GLM.jl provides generalized linear models in Julia, with documentation and examples for the package.
| # | Alternative | Pricing | Open source | Platforms |
|---|---|---|---|---|
| 1 | DataDeps | — | Not stated | — |
| 2 | Whylogs | — | Yes | — |
| 3 | gensim | — | Yes | — |
| 4 | Weights & Biases | — | Yes | — |
| 5 | statsmodels | — | Yes | — |
| 6 | GLMNet | — | Not stated | — |
| 7 | Dash | — | Yes | — |
| 8 | Piperider | — | Yes | — |
| 9 | AWS Data Wrangler | — | Yes | — |
| 10 | DataComPy | — | Yes | — |
DataDeps.jl provides reproducible data setup for scientific work, with a Julia package for fetching and using data dependencies.
Machine learning pipelines can use whylogs, an open-source library for logging data quality and model performance.
Python developers can use gensim for topic modeling and related natural language processing tasks.
Weights & Biases supports model training, fine tuning, and management from experimentation through production.
statsmodels provides statistical modeling and econometrics tools in Python.
GLMNet.jl is a Julia wrapper for fitting Lasso and Elastic Net generalized linear models using glmnet.
Python developers can use Dash to build data apps and dashboards without requiring JavaScript.
PipeRider provided code review tools for dbt data and has been superseded by Recce.
AWS Data Wrangler connects Pandas with AWS data services and databases, including Athena, Glue, Redshift, and S3.
DataComPy compares Pandas, Polars, Spark, and Snowpark dataframes, with output that includes comparison statistics.
SynapseML provides distributed machine learning algorithms for Spark through its Python library.
Koalas implements the pandas DataFrame API on Apache Spark and is deprecated; for Apache Spark 3.2 and above, use PySpark directly.
mlxtend adds helper modules for Python data analysis and machine learning libraries.
The Geni project provides a Clojure dataframe library that runs on Apache Spark.
For Python data preparation, DataPrep offers an open-source low-code library to collect, clean, and visualize data.
jSciPy provides Java scientific computing tools for signal processing, FFT, filtering, power spectral density, and DCT.
Class-imbalanced machine learning is supported by imbalanced-ensemble, a toolbox for ensemble methods and datasets.
The tidyverse is an integrated collection of R packages for data science.
TextAnalysis.jl is a Julia package for text and language data, with tools such as corpora, TF-IDF, and topic models.
GaussianMixtures.jl is a Julia package for working with Gaussian mixture models.
liblinear-java provides a Java version of LIBLINEAR for linear classification and regression models.
Distances.jl is a Julia package for calculating distance metrics between vectors, including pairwise and column-wise calculations.
ReadStat.jl is a Julia package that parses data files from Stata, SAS, and SPSS into a ReadStatDataFrame.
HypothesisTests.jl is a Julia package that implements statistical hypothesis tests.
Alternatives are suggested from the purpose and features described in our listings. Different pages for the same product appear once. Read each description to check whether it fits your task. How we choose alternatives · Suggest an alternative
