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 |
| 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 |
Documentation:
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