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spfa  

Semi-Parametric Factor Analysis
View on CRAN: Click here


Download and install spfa package within the R console
Install from CRAN:
install.packages("spfa")

Install from Github:
library("remotes")
install_github("cran/spfa")

Install by package version:
library("remotes")
install_version("spfa", "1.0")



Attach the package and use:
library("spfa")
Maintained by
Yang Liu
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-05-26
Latest Update: 2023-05-26
Description:
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.
How to cite:
Yang Liu (2023). spfa: Semi-Parametric Factor Analysis. R package version 1.0, https://cran.r-project.org/web/packages/spfa. Accessed 21 Nov. 2024.
Previous versions and publish date:
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Complete documentation for spfa
Functions, R codes and Examples using the spfa R package
Some associated functions: fscores . plotitem.cont . simdata . spfa-package . spfa . spfa_example . 
Some associated R codes: RcppExports.R . fitting.R . plotting.R . scoring.R . simdata.R . spfa-package.R . spfa_example.R .  Full spfa package functions and examples
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