LAWBL
Latent (Variable) Analysis with Bayesian Learning
A variety of models to analyze latent variables based on Bayesian learning: the partially CFA (doi:10.1037/met0000293); generalized PCFA; partially confirmatory IRM (doi:10.1007/s11336-020-09724-3); Bayesian regularized EFA (doi:10.1080/10705511.2020.1854763); Fully and partially EFA.
- Version1.5.0
- R version≥ 3.6.0
- LicenseGPL-3
- Needs compilation?No
- Chen, Guo, Zhang, & Pan, 2020
- Chen, 2020
- Bayesian regularized EFA
- Last release05/16/2022
Documentation
Team
Jinsong Chen
Insights
Last 30 days
This package has been downloaded 213 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 9 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 3,577 times in the last 365 days. That's enough downloads to impress a room full of undergrads. A commendable achievement indeed. The day with the most downloads was Jul 21, 2024 with 145 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
Binaries
Dependencies
- Imports2 packages
- Suggests3 packages