Package: spfa 1.0

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.

Authors:Yang Liu [cre, aut], Weimeng Wang [aut, ctb]

spfa_1.0.tar.gz
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spfa_1.0.tgz(r-4.4-x86_64)spfa_1.0.tgz(r-4.4-arm64)spfa_1.0.tgz(r-4.3-x86_64)spfa_1.0.tgz(r-4.3-arm64)
spfa_1.0.tar.gz(r-4.5-noble)spfa_1.0.tar.gz(r-4.4-noble)
spfa_1.0.tgz(r-4.4-emscripten)spfa_1.0.tgz(r-4.3-emscripten)
spfa.pdf |spfa.html
spfa/json (API)
NEWS

# Install 'spfa' in R:
install.packages('spfa', repos = c('https://yliu87.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.70 score 1 scripts 121 downloads 3 exports 2 dependencies

Last updated 1 years agofrom:0f18b7a1db. Checks:OK: 9. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 26 2024
R-4.5-win-x86_64OKOct 26 2024
R-4.5-linux-x86_64OKOct 26 2024
R-4.4-win-x86_64OKOct 26 2024
R-4.4-mac-x86_64OKOct 26 2024
R-4.4-mac-aarch64OKOct 26 2024
R-4.3-win-x86_64OKOct 26 2024
R-4.3-mac-x86_64OKOct 26 2024
R-4.3-mac-aarch64OKOct 26 2024

Exports:fscoresplotitem.contspfa

Dependencies:RcppRcppArmadillo