BiplotML: Logistic Biplot Estimation Using Machine Learning Algorithms
Implements methods for fitting logistic biplot models to
multivariate binary data. The logistic biplot represents individuals
as points and binary variables as directed vectors in a low-dimensional
subspace; the orthogonal projection of each individual onto a variable
vector approximates the expected probability that the corresponding
characteristic is present. Available fitting methods include conjugate
gradient algorithms, a coordinate descent Majorization-Minimization
(MM) algorithm, and a block coordinate descent algorithm based on data
projection that supports matrices with missing values and allows new
individuals to be projected as supplementary rows without refitting
the model. A cross-validation procedure is provided to select the
number of latent dimensions k.
References: Babativa-Marquez and Vicente-Villardon (2021)
<doi:10.3390/math9162015>;
Vicente-Villardon and Galindo (2006, ISBN:9780470973196).
| Version: |
1.1.1 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
optimx, RSpectra |
| Suggests: |
testthat (≥ 3.0.0), knitr, rmarkdown, dplyr (≥ 1.0.0), tidyr (≥ 1.1.0), ggplot2 (≥ 3.3.2), ggrepel, pracma, mvtnorm |
| Published: |
2026-05-08 |
| DOI: |
10.32614/CRAN.package.BiplotML |
| Author: |
Jose Giovany Babativa-Marquez
 [cre, aut] |
| Maintainer: |
Jose Giovany Babativa-Marquez <jgbabativam at unal.edu.co> |
| BugReports: |
https://github.com/jgbabativam/BiplotML/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/jgbabativam/BiplotML |
| NeedsCompilation: |
no |
| Citation: |
BiplotML citation info |
| Materials: |
NEWS |
| CRAN checks: |
BiplotML results |
Documentation:
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