- Check Jiang and Carter’s (2019) article on Using Hamiltonian Monte Carlo to estimate the log-linear cognitive diagnosis model via Stan
- Estimating log-linear cognitive diagnosis model (LCDM; Henson, Templin, & Willse, 2009) and a variety of widely-used models subsumed by the LCDM, including the deterministic inputs, noisy “and” gate model (DINA; Haertel, 1989; Junker & Sijtsma, 2001; Macready & Dayton, 1977), the deterministic input, noisy “or” gate model (DINO;Templin & Henson, 2006), the deterministic “and” gate model (NIDA; Junker & Sijtsma, 2001), the compensatory reparameterized unified model (CRUM; Hartz, 2002) as well as the noncompensatory reduced reparameterized unified model (NCRUM;DiBello, Stout, & Roussos, 1995) for dichotomous responses
- Specifying customized prior distributions for parameters
- Computing can be achieved in parallel environments
- Estimating models within the LCDM model framework using user-specified design matrix
- Estimating rating scale DCM for ordinal responses
- Providing posterior predictive model checking (PPMC; Gelman, Meng, & Stern 1996), Watanabe-Akaike information criterion (WAIC; Watanabe, 2010) and leave-one-out cross validation (LOO)
- Enabling group invariance estimations with different constraint specifications
To install this package from source:
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Users need to install the rstan in order to execute the functions of StanDCM package.
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Windows users should avoid using space when installing rstan.
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After installing rstan package, users can use the lines beblow to install StanDCM package.
# install.packages("devtools")
devtools::source_url("https://raw.githubusercontent.com/zjiang4/StanDCM/master/R/StanDCM.R")
# To use it in your local machines, please save the StanDCM.R to your local drives, and use "source(.../StanDCM.R)" in R to execute the package functions
# For example, if the StanDCM.R is saved in "C:\\Myfile", users should type source("C:/Myfile/StanDCM.R") in RThe parametric version of DCM R package named GDINA can be found in R CRAN at here