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) <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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.0 |
rolling source/ R- | spfa_1.0.tar.gz |
194.3 KiB |
1.0 |
rolling linux/jammy R-4.5 | spfa_1.0.tar.gz |
422.6 KiB |
1.0 |
rolling linux/noble R-4.5 | spfa_1.0.tar.gz |
429.2 KiB |
1.0 |
latest linux/jammy R-4.5 | spfa_1.0.tar.gz |
422.6 KiB |
1.0 |
latest linux/noble R-4.5 | spfa_1.0.tar.gz |
429.2 KiB |
1.0 |
latest source/ R- | spfa_1.0.tar.gz |
194.3 KiB |
1.0 |
2026-04-26 source/ R- | spfa_1.0.tar.gz |
194.3 KiB |
1.0 |
2026-04-23 source/ R- | spfa_1.0.tar.gz |
194.3 KiB |
1.0 |
2026-04-09 windows/windows R-4.5 | spfa_1.0.zip |
824.4 KiB |
1.0 |
2025-04-20 source/ R- | spfa_1.0.tar.gz |
194.3 KiB |