| Name : R-variationalDCM
| |
| Version : 2.0.1
| Vendor : obs://build_opensuse_org/devel:languages:R
|
| Release : lp153.2.2
| Date : 2024-06-14 11:23:45
|
| Group : Development/Libraries/Other
| Source RPM : R-variationalDCM-2.0.1-lp153.2.2.src.rpm
|
| Size : 0.16 MB
| |
| Packager : (none)
| |
| Summary : Variational Bayesian Estimation for Diagnostic Classification Models
|
Description :
Enables computationally efficient parameters-estimation by variational
Bayesian methods for various diagnostic classification models (DCMs).
DCMs are a class of discrete latent variable models for classifying
respondents into latent classes that typically represent distinct
combinations of skills they possess. Recently, to meet the growing need
of large-scale diagnostic measurement in the field of educational,
psychological, and psychiatric measurements, variational Bayesian
inference has been developed as a computationally efficient alternative
to the Markov chain Monte Carlo methods, e.g., Yamaguchi and Okada
(2020a) < doi:10.1007/s11336-020-09739-w>, Yamaguchi and Okada (2020b)
< doi:10.3102/1076998620911934>, Yamaguchi (2020)
< doi:10.1007/s41237-020-00104-w>, Oka and Okada (2023)
< doi:10.1007/s11336-022-09884-4>, and Yamaguchi and Martinez (2023)
< doi:10.1111/bmsp.12308>. To facilitate their applications,
\'variationalDCM\' is developed to provide a collection of
recently-proposed variational Bayesian estimation methods for various
DCMs.
|
RPM found in directory: /packages/linux-pbone/ftp5.gwdg.de/pub/opensuse/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Leap_15.3/x86_64 |
