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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 |
| 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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