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- ExpFamilyPCA.jl
On the recordRead 12 Oct 202615 facts
ExpFamilyPCA.jl
Plans and prices
Open-source package
Free
Julia package · MIT license
Read 12 Oct 2026 · github.com
On the record
- Custom models
- Users can define EPCA models for custom distributions by specifying mathematical components such as an objective and link function.sisl.github.io · read 12 Oct 2026
- Data import formats
- Julia AbstractMatrix datasisl.github.io · read 23 Sep 2026
- Input format
- The fitting and compression methods accept real-valued abstract matrices, with rows representing observations and columns representing features.sisl.github.io · read 12 Oct 2026
- Installation
- The project instructs users to install it through Julia's package manager with Pkg.add("ExpFamilyPCA").sisl.github.io · read 12 Oct 2026
- Integrations
- The documentation describes using Symbolics.jl for symbolic differentiation and Optim.jl for optimization in custom EPCA specifications.sisl.github.io · read 12 Oct 2026
- License
- The GitHub repository identifies the project as MIT licensed.github.com · read 12 Oct 2026
- Model workflow
- EPCA objects provide fit!, compress, and decompress methods for fitting data, compressing new data, and reconstructing data.github.com · read 12 Oct 2026
- Optimization
- The package documents configurable optimization options, including regularization, initialization values, parameter bounds, and iteration controls.sisl.github.io · read 12 Oct 2026
- Performance limit
- The documentation warns that fitting may take a long time depending on dataset size and model complexity.sisl.github.io · read 12 Oct 2026
- Purpose
- ExpFamilyPCA.jl is a Julia package for exponential family principal component analysis, a generalization of PCA for non-Gaussian data.github.com · read 12 Oct 2026
- Scripting support
- Yessisl.github.io · read 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.sisl.github.io · read 12 Oct 2026
- Supported distributions
- Built-in models cover Bernoulli, Binomial, Continuous Bernoulli, Gamma, Gaussian, Negative Binomial, Pareto, Poisson, and Weibull distributions.sisl.github.io · read 12 Oct 2026
- Use cases
- The project describes applications including belief compression, text analysis, denoising, sample debiasing, clustering, and dimensionality reduction.github.com · read 12 Oct 2026
