The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
Estimation, scoring, and plotting functions for the semi-parametric factor model proposed by Liu & Wang [(2022)] (https://link.springer.com/article/10.1007/s11336-021-09832-8) and Liu & Wang (2023). 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.
You can install the development version of spfa from GitHub, with:
# install.packages("devtools")
devtools::install_github("wwang1370/spfa")Various examples and help files have been compiled using the knitr package to generate HTML output, and are available on the package help file. User contributions are welcome!
library(spfa)
## basic example code includes fitting an spfa model for response time
# RT <- spfa::simdata[,1:8]
# spfa_example <- spfa(data = RT,
# dimension = rep(0, ncol(RT)),
# discrete = rep(F, ncol(RT)))Liu, Y., & Wang, W. (2022). Semiparametric factor analysis for item-level response time data. Psychometrika, 87 (2), 666-692.
Liu, Y., & Wang, W. (2023). What Can We Learn from a Semiparametric Factor Analysis of Item Responses and Response Time? An Illustration with the PISA 2015 Data. Retrieved from http://arxiv.org/abs/2303.10079
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.