ExpFamilyPCA.jl
The facts below are what the maker publishes about ExpFamilyPCA.jl, checked and dated. Our editors have not written about it yet.
Plans and prices
From ExpFamilyPCA.jl's own pricing page, checked 12 Oct 2026.
| Plan | Price | What it gives you |
|---|---|---|
| Open-source package | Free | Julia package · MIT license |
The facts
Each one read from ExpFamilyPCA.jl's own pages, with the date it was checked.
What it does
- Custom models
- Users can define EPCA models for custom distributions by specifying mathematical components such as an objective and link function.checked 12 Oct 2026
- Data import formats
- Julia AbstractMatrix datachecked 23 Sep 2026
- Input format
- The fitting and compression methods accept real-valued abstract matrices, with rows representing observations and columns representing features.checked 12 Oct 2026
- Installation
- The project instructs users to install it through Julia's package manager with Pkg.add("ExpFamilyPCA").checked 12 Oct 2026
- Integrations
- The documentation describes using Symbolics.jl for symbolic differentiation and Optim.jl for optimization in custom EPCA specifications.checked 12 Oct 2026
- License
- The GitHub repository identifies the project as MIT licensed.checked 12 Oct 2026
- Model workflow
- EPCA objects provide fit!, compress, and decompress methods for fitting data, compressing new data, and reconstructing data.checked 12 Oct 2026
- Optimization
- The package documents configurable optimization options, including regularization, initialization values, parameter bounds, and iteration controls.checked 12 Oct 2026
- Performance limit
- The documentation warns that fitting may take a long time depending on dataset size and model complexity.checked 12 Oct 2026
- Purpose
- ExpFamilyPCA.jl is a Julia package for exponential family principal component analysis, a generalization of PCA for non-Gaussian data.checked 12 Oct 2026
- Scripting support
- Yeschecked 23 Sep 2026
- Support and contributions
- The project invites users to ask questions and contribute, and directs requests for new compressors or model improvements to GitHub issues.checked 12 Oct 2026
- Supported distributions
- Built-in models cover Bernoulli, Binomial, Continuous Bernoulli, Gamma, Gaussian, Negative Binomial, Pareto, Poisson, and Weibull distributions.checked 12 Oct 2026
- Use cases
- The project describes applications including belief compression, text analysis, denoising, sample debiasing, clustering, and dimensionality reduction.checked 12 Oct 2026
Money
- Free plan
- Yeschecked 23 Sep 2026
