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.">

spfa: Semi-Parametric Factor Analysis (original) (raw)

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
License: MIT + file
NeedsCompilation: yes
Materials: README NEWS
CRAN checks: spfa results

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