spfa: Semi-Parametric Factor Analysis

Estimation, scoring, and plotting functions for the semi-parametric factor model proposed by Liu & Wang (2022) <doi:10.1007/s11336-021-09832-8> and Liu & Wang (2023) <doi:10.48550/arXiv.2303.10079>. Both the conditional densities of observed responses given the latent factors and the joint density of latent factors are estimated non-parametrically. Functional parameters are approximated by smoothing splines, whose coefficients are estimated by penalized maximum likelihood using an expectation-maximization (EM) algorithm. E- and M-steps can be parallelized on multi-thread computing platforms that support 'OpenMP'. Both continuous and unordered categorical response variables are supported.

Version: 1.0
Depends: R (≥ 2.10)
Imports: graphics, Rcpp
LinkingTo: Rcpp, RcppArmadillo
Published: 2023-05-26
DOI: 10.32614/CRAN.package.spfa
Author: Yang Liu [cre, aut], Weimeng Wang [aut, ctb]
Maintainer: Yang Liu <yliu87 at umd.edu>
License: MIT + file LICENSE
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: spfa results


Reference manual: spfa.pdf


Package source: spfa_1.0.tar.gz
Windows binaries: r-devel: spfa_1.0.zip, r-release: spfa_1.0.zip, r-oldrel: spfa_1.0.zip
macOS binaries: r-release (arm64): spfa_1.0.tgz, r-oldrel (arm64): spfa_1.0.tgz, r-release (x86_64): spfa_1.0.tgz, r-oldrel (x86_64): spfa_1.0.tgz


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