ParMLE
(Package: NPCD) :
Maximum likelihood estimation of item parameters for cognitive diagnostic models.
This function returns maximum likelihood estimates of item parameters for cognitive diagnostic models when examinee ability patterns are known. This function can either be used independently or called in the JMLE function. Currently supported cognitive diagnostic models include the DINA model, the DINO model, the NIDA model, the G-NIDA model, and the R-RUM model.
CDL
(Package: NPCD) :
Log-likelihood for cognitive diagnostic models
This function returns the log-likelihood of a particular examinee's responses to a set of cognitive diagnostic items. Currently supported cognitive diagnostic models include the DINA model, DINO model, NIDA model, G-NIDA model, and R-RUM model. This function is called by the AlphaMLE function and the JMLE function in the package.
This package implements an array of nonparametric and parametric estimation methods for cognitive diagnostic models, including nonparametric classification of examinee attribute profiles, joint maximum likelihood estimation (JMLE) of examinee attribute profiles and item parameters, and nonparametric refinement of the Q-matrix, as well as conditional maximum likelihood estimation (CMLE) of examinee attribute profiles given item parameters and CMLE of item parameters given examinee attribute profiles.
AlphaNP
(Package: NPCD) :
Nonparametric estimation of attribute profiles
This function estimates attribute profiles using nonparametric approaches for both the "AND gate" (conjunctive) and the "OR gate" (disjunctive) cognitive diagnostic models. These algorithms select the attribute profile with the smallest loss function value (plain, weighted, or penalized Hamming distance, see below for details) as the estimate. If more than one attribute profiles have the smallest loss function value, one of them is randomly chosen.
JMLE
(Package: NPCD) :
Joint maximum likelihood estimation of item parameters and examinee attribute profiles
This function returns joint maximum likelihood estimates of item parameters and examinee attribute profiles in cognitive diagnostic models. The algorithm starts from the nonparametric estimation of attribute profiles, implemented by the AlphaNP function, and then iteratively estimates item parameters and attribute profiles using conditional maximum likelihood estimation until the algorithm converges. Currently supported models include the DINA model, the DINO model, he NIDA model, the G-NIDA model, and the R-RUM model.
Qrefine
(Package: NPCD) :
Refine the Q-matrix by minimizing the residual sum of square (RSS)
Refine the Q-matrix by minimizing the residual sum of square (RSS) betweenn the real responses and ideal responses. Examinee attribute profiles are estimated using the nonparametric method (plain Hamming) implemented by AlphaNP.
plot.NPCD
(Package: NPCD) :
Produce diagnostic plots
This function produces diagnostic plots of various outputs generated from the functions in this package, including AlphaNP, AlphaMLE, JMLE, andQrefine.
CDP
(Package: NPCD) :
Probability of correct response for cognitive diagnostic models
This function returns the model-predicted probability of correct response of one item for one person given the item parameters, Q vector, and alpha vector. Currently supported cognitive diagnostic models include the DINA model, DINO model, NIDA model, G-NIDA model, and R-RUM model. This function is called by the ItemFit function in the package.
AlphaMLE
(Package: NPCD) :
Maximum likelihood estimation of attribute profile
This function returns the model-based Maximum likelihood estimator(s) of the cognitive diagnostic attribute profile(s). Currently supported cognitive diagnostic models include the DINA, DINO, NIDA, GNIDA, and R-RUM models.
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