-
Notifications
You must be signed in to change notification settings - Fork 1
/
README.Rmd
87 lines (56 loc) · 4.55 KB
/
README.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
<!-- <!-- Global site tag (gtag.js) - Google Analytics --> -->
<!-- <script async src="https://www.googletagmanager.com/gtag/js?id=G-6XKC5E4PWL"></script> -->
<!-- <script> -->
<!-- window.dataLayer = window.dataLayer || []; -->
<!-- function gtag(){dataLayer.push(arguments);} -->
<!-- gtag('js', new Date()); -->
<!-- gtag('config', 'G-6XKC5E4PWL'); -->
<!-- </script> -->
# LAWBL: Latent (Variable) Analysis With Bayesian Learning
[](https://www.repostatus.org/)
[](https://cran.r-project.org/package=LAWBL)
[](https://cran.r-project.org/package=LAWBL)
[](https://cran.r-project.org/package=LAWBL)
## How to cite the package
Chen, J. (2022). LAWBL: Latent (variable) analysis with Bayesian learning (R package version 1.5.0). Retrieved from [https://CRAN.R-project.org/package=LAWBL](https://CRAN.R-project.org/package=LAWBL)
## Introduction
**LAWBL** represents a partially exploratory-confirmatory approach to model latent variables based on Bayesian learning. Built on the power of statistical learning, it can address psychometric challenges such as parameter specification, local dependence, and factor extraction. Built on the scalability and flexibility of Bayesian inference and resampling techniques, it can accommodate modeling frameworks such as factor analysis, item response theory, cognitive diagnosis modeling and causal or explanatory modeling. The package can also handle different response formats or a mix of them, with or without missingness.
## Features
* Partially CFA (PCFA) for continuous data: regularization of loading specification and local dependence; PCFA with local independence (PCFA-LI); CFA with local dependence (CFA-LD)
* Generalized PCFA (GPCFA) for continuous, categorical, or mixed-type data, with or without missingness; GPCFA with local independence (GPCFA-LI); Generalized CFA with local dependence (GCFA-LD)
* Partially confirmatory item response model (PCIRM) for continuous and dichotomous data with intercept terms; PCIRM-LI; CIRM-LD
* Bayesian regularized EFA (BREFA): factor extraction and parameter estimation in one step; Fully and partially EFA: unknown number of factors without or with partial knowledge
* Estimation using different Bayesian learning methods and MCMC algorithms
* Simulating data based on all aforementioned models
* Plotting trace, density or Gelman-Rubin diagnostics based on eigenvalue
* Summary of all parameters with both point and interval estimates
Please refer to the [online tutorials](https://jinsong-chen.github.io/LAWBL/articles/LAWBL.html) for more details.
## Installation
1) Install the stable version from [CRAN](https://CRAN.R-project.org) with:
``` r
install.packages("LAWBL")
```
2) Install the `devtools` package (if necessary), and install the development version from the Github.
```{r gh-installation, eval = FALSE}
# install.packages("devtools")
devtools::install_github("Jinsong-Chen/LAWBL")
```
## References
Chen, J. (2020). A partially confirmatory approach to the multidimensional item response theory with the Bayesian Lasso. *Psychometrika*. 85(3), 738-774. DOI: 10.1007/s11336-020-09724-3.
Chen, J., Guo, Z., Zhang, L., & Pan, J. (2021). A partially confirmatory approach to scale development with the Bayesian Lasso. *Psychological Methods*. 26(2), 210–235. DOI: 10.1037/met0000293.
Chen, J. (2021). A generalized partially confirmatory factor analysis framework with mixed Bayesian Lasso methods. *Multivariate Behavioral Research*. DOI: 10.1080/00273171.2021.1925520.
Chen, J. (2021). A Bayesian regularized approach to exploratory factor analysis in one step. *Structural Equation Modeling: A Multidisciplinary Journal*. DOI: 10.1080/10705511.2020.1854763.
Chen, J. (2022). Partially confirmatory approach to factor analysis with Bayesian learning: A LAWBL tutorial. *Structural Equation Modeling: A Multidisciplinary Journal*. DOI: 10.1080/00273171.2021.1925520.
Chen, J. (In Press). Fully and partially exploratory factor analysis with bi-level Bayesian regularization. *Behavior Research Methods*.