R-universe search: dina

Showing 14 of total 14 results (show query)

wenchao-ma

GDINA:The Generalized DINA Model Framework

A set of psychometric tools for cognitive diagnosis modeling based on the generalized deterministic inputs, noisy and gate (G-DINA) model by de la Torre (2011) <DOI:10.1007/s11336-011-9207-7> and its extensions, including the sequential G-DINA model by Ma and de la Torre (2016) <DOI:10.1111/bmsp.12070> for polytomous responses, and the polytomous G-DINA model by Chen and de la Torre <DOI:10.1177/0146621613479818> for polytomous attributes. Joint attribute distribution can be independent, saturated, higher-order, loglinear smoothed or structured. Q-matrix validation, item and model fit statistics, model comparison at test and item level and differential item functioning can also be conducted. A graphical user interface is also provided. For tutorials, please check Ma and de la Torre (2020) <DOI:10.18637/jss.v093.i14>, Ma and de la Torre (2019) <DOI:10.1111/emip.12262>, Ma (2019) <DOI:10.1007/978-3-030-05584-4_29> and de la Torre and Akbay (2019).

Maintained by Wenchao Ma. Last updated 2 months ago.

cdm cognitive-diagnosis dcm dina-model dino estimation-models gdina item-response-theory psychometrics openblas cpp

44.3 match 30 stars 8.92 score 94 scripts 6 dependents

alexanderrobitzsch

CDM:Cognitive Diagnosis Modeling

Functions for cognitive diagnosis modeling and multidimensional item response modeling for dichotomous and polytomous item responses. This package enables the estimation of the DINA and DINO model (Junker & Sijtsma, 2001, <doi:10.1177/01466210122032064>), the multiple group (polytomous) GDINA model (de la Torre, 2011, <doi:10.1007/s11336-011-9207-7>), the multiple choice DINA model (de la Torre, 2009, <doi:10.1177/0146621608320523>), the general diagnostic model (GDM; von Davier, 2008, <doi:10.1348/000711007X193957>), the structured latent class model (SLCA; Formann, 1992, <doi:10.1080/01621459.1992.10475229>) and regularized latent class analysis (Chen, Li, Liu, & Ying, 2017, <doi:10.1007/s11336-016-9545-6>). See George, Robitzsch, Kiefer, Gross, and Uenlue (2017) <doi:10.18637/jss.v074.i02> or Robitzsch and George (2019, <doi:10.1007/978-3-030-05584-4_26>) for further details on estimation and the package structure. For tutorials on how to use the CDM package see George and Robitzsch (2015, <doi:10.20982/tqmp.11.3.p189>) as well as Ravand and Robitzsch (2015).

Maintained by Alexander Robitzsch. Last updated 9 months ago.

cognitive-diagnostic-models item-response-theory cpp

29.0 match 22 stars 8.82 score 138 scripts 28 dependents

tmsalab

dina:Bayesian Estimation of DINA Model

Estimate the Deterministic Input, Noisy "And" Gate (DINA) cognitive diagnostic model parameters using the Gibbs sampler described by Culpepper (2015) <doi:10.3102/1076998615595403>.

Maintained by James Joseph Balamuta. Last updated 5 years ago.

armadillo bayesian gibbs-sampler irt item-response-theory psychometrics rcpp rcpparmadillo openblas cpp

65.1 match 14 stars 3.85 score 3 scripts

tmsalab

simcdm:Simulate Cognitive Diagnostic Model ('CDM') Data

Provides efficient R and 'C++' routines to simulate cognitive diagnostic model data for Deterministic Input, Noisy "And" Gate ('DINA') and reduced Reparameterized Unified Model ('rRUM') from Culpepper and Hudson (2017) <doi: 10.1177/0146621617707511>, Culpepper (2015) <doi:10.3102/1076998615595403>, and de la Torre (2009) <doi:10.3102/1076998607309474>.

Maintained by James Joseph Balamuta. Last updated 1 years ago.

cognitive-diagnostic-models psychometrics rcpp rcpparmadillo simulation openblas cpp

10.2 match 4.95 score 15 scripts 2 dependents

khijikata

variationalDCM:Variational Bayesian Estimation for Diagnostic Classification Models

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 (2024) <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.

Maintained by Keiichiro Hijikata. Last updated 1 years ago.

9.6 match 4.00 score

miguel-sorrel

cdcatR:Cognitive Diagnostic Computerized Adaptive Testing

Provides a set of functions for conducting cognitive diagnostic computerized adaptive testing applications (Chen, 2009) <DOI:10.1007/s11336-009-9123-2>). It includes different item selection rules such us the global discrimination index (Kaplan, de la Torre, and Barrada (2015) <DOI:10.1177/0146621614554650>) and the nonparametric selection method (Chang, Chiu, and Tsai (2019) <DOI:10.1177/0146621618813113>), as well as several stopping rules. Functions for generating item banks and responses are also provided. To guide item bank calibration, model comparison at the item level can be conducted using the two-step likelihood ratio test statistic by Sorrel, de la Torre, Abad and Olea (2017) <DOI:10.1027/1614-2241/a000131>.

Maintained by Miguel A. Sorrel. Last updated 3 years ago.

10.3 match 5 stars 3.63 score 17 scripts

tmsalab

edina:Bayesian Estimation of an Exploratory Deterministic Input, Noisy and Gate Model

Perform a Bayesian estimation of the exploratory deterministic input, noisy and gate (EDINA) cognitive diagnostic model described by Chen et al. (2018) <doi:10.1007/s11336-017-9579-4>.

Maintained by James Joseph Balamuta. Last updated 5 years ago.

cognitive-diagnostic-models cpp dina ecdm item-response-theory psychometrics rcpparmadillo openblas cpp

11.0 match 2 stars 3.00 score 1 scripts

fishr-core-team

FSAdata:Data to Support Fish Stock Assessment ('FSA') Package

The datasets to support the Fish Stock Assessment ('FSA') package.

Maintained by Derek Ogle. Last updated 2 years ago.

fish fisheries fisheries-stock-assessment fishr-website stock-assessment

3.3 match 13 stars 5.67 score 285 scripts

jpquast

protti:Bottom-Up Proteomics and LiP-MS Quality Control and Data Analysis Tools

Useful functions and workflows for proteomics quality control and data analysis of both limited proteolysis-coupled mass spectrometry (LiP-MS) (Feng et. al. (2014) <doi:10.1038/nbt.2999>) and regular bottom-up proteomics experiments. Data generated with search tools such as 'Spectronaut', 'MaxQuant' and 'Proteome Discover' can be easily used due to flexibility of functions.

Maintained by Jan-Philipp Quast. Last updated 5 months ago.

data-analysis lip-ms mass-spectrometry omics protein proteomics systems-biology

1.6 match 63 stars 8.51 score 83 scripts

pablo-najera

cdmTools:Useful Tools for Cognitive Diagnosis Modeling

Provides useful tools for cognitive diagnosis modeling (CDM). The package includes functions for empirical Q-matrix estimation and validation, such as the Hull method (Nájera, Sorrel, de la Torre, & Abad, 2021, <doi:10.1111/bmsp.12228>) and the discrete factor loading method (Wang, Song, & Ding, 2018, <doi:10.1007/978-3-319-77249-3_29>). It also contains dimensionality assessment procedures for CDM, including parallel analysis and automated fit comparison as explored in Nájera, Abad, and Sorrel (2021, <doi:10.3389/fpsyg.2021.614470>). Other relevant methods and features for CDM applications, such as the restricted DINA model (Nájera et al., 2023; <doi:10.3102/10769986231158829>), the general nonparametric classification method (Chiu et al., 2018; <doi:10.1007/s11336-017-9595-4>), and corrected estimation of the classification accuracy via multiple imputation (Kreitchmann et al., 2022; <doi:10.3758/s13428-022-01967-5>) are also available. Lastly, the package provides some useful functions for CDM simulation studies, such as random Q-matrix generation and detection of complete/identified Q-matrices.

Maintained by Pablo Nájera. Last updated 1 years ago.

2.5 match 3 stars 3.88 score 17 scripts 1 dependents

cran

tidytidbits:A Collection of Tools and Helpers Extending the Tidyverse

A selection of various tools to extend a data analysis workflow based on the 'tidyverse' packages. This includes high-level data frame editing methods (in the style of 'mutate'/'mutate_at'), some methods in the style of 'purrr' and 'forcats', 'lookup' methods for dict-like lists, a generic method for lumping a data frame by a given count, various low-level methods for special treatment of 'NA' values, 'python'-style tuple-assignment and 'truthy'/'falsy' checks, saving to PDF and PNG from a pipe and various small utilities.

Maintained by Marcel Wiesweg. Last updated 3 years ago.

3.3 match 2.48 score 2 dependents

philchalmers

mirt:Multidimensional Item Response Theory

Analysis of discrete response data using unidimensional and multidimensional item analysis models under the Item Response Theory paradigm (Chalmers (2012) <doi:10.18637/jss.v048.i06>). Exploratory and confirmatory item factor analysis models are estimated with quadrature (EM) or stochastic (MHRM) methods. Confirmatory bi-factor and two-tier models are available for modeling item testlets using dimension reduction EM algorithms, while multiple group analyses and mixed effects designs are included for detecting differential item, bundle, and test functioning, and for modeling item and person covariates. Finally, latent class models such as the DINA, DINO, multidimensional latent class, mixture IRT models, and zero-inflated response models are supported, as well as a wide family of probabilistic unfolding models.

Maintained by Phil Chalmers. Last updated 5 days ago.

irt mirt openblas cpp openmp

0.5 match 212 stars 14.93 score 2.5k scripts 40 dependents

cran

NPCD:Nonparametric Methods for Cognitive Diagnosis

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. Currently the nonparametric methods in the package support both conjunctive and disjunctive models, and the parametric methods in the package support the DINA model, the DINO model, the NIDA model, the G-NIDA model, and the R-RUM model.

Maintained by Yi Zheng. Last updated 5 years ago.

4.0 match 1 stars 1.78 score 2 dependents

haijiangq

Qval:The Q-Matrix Validation Methods Framework

Provide a variety of Q-matrix validation methods for the generalized cognitive diagnosis models, including the method based on the generalized deterministic input, noisy, and gate model (G-DINA) by de la Torre (2011) <DOI:10.1007/s11336-011-9207-7> discrimination index (the GDI method) by de la Torre and Chiu (2016) <DOI:10.1007/s11336-015-9467-8>, the step-wise Wald test method (the Wald method) by Ma and de la Torre (2020) <DOI:10.1111/bmsp.12156>, the Hull method by Najera et al. (2021) <DOI:10.1111/bmsp.12228>, the multiple logistic regression‑based Q‑matrix validation method (the MLR-B method) by Tu et al. (2022) <DOI:10.3758/s13428-022-01880-x>, the beta method based on signal detection theory by Li and Chen (2024) <DOI:10.1111/bmsp.12371> and Q-matrix validation based on relative fit index by Chen et la. (2013) <DOI:10.1111/j.1745-3984.2012.00185.x>. Different research methods and iterative procedures during Q-matrix validating are available.

Maintained by Haijiang Qin. Last updated 24 days ago.

cpp

0.5 match 1 stars 2.24 score 35 scripts