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cff-version: 1.2.0
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type: software
license: MIT
title: 'spfa: Semi-Parametric Factor Analysis'
version: '1.0'
doi: 10.32614/CRAN.package.spfa
abstract: Estimation, scoring, and plotting functions for the semi-parametric factor
  model proposed by Liu & Wang (2022) <https://doi.org/10.1007/s11336-021-09832-8>
  and Liu & Wang (2023) <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.
authors:
- family-names: Liu
  given-names: Yang
  email: yliu87@umd.edu
- family-names: Wang
  given-names: Weimeng
  email: weimengbonnie@gmail.com
repository: https://CRAN.R-project.org/package=spfa
date-released: '2023-04-25'
contact:
- family-names: Liu
  given-names: Yang
  email: yliu87@umd.edu