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)