No packages match

ForceChoice - Forced-Choice Modeling Based on Item Response Theory and Cognitive Diagnostic Models

Fits, simulates, and evaluates forced-choice and traditional item response theory (IRT) models for noncognitive assessment. Eight model families are supported, spanning dominance (multidimensional IRT (MIRT) 1PL--4PL; multidimensional generalized partial credit model (MGPCM)), ideal-point unfolding (multidimensional generalized graded unfolding model (MGGUM)), and forced-choice designs (forced-choice multidimensional IRT (FCMIRT), forced-choice generalized graded unfolding model (FCGGUM), Thurstonian IRT (TIRT), forced-choice diagnostic classification model (FCDCM), forced-choice generalized deterministic inputs, noisy "and" gate model (FCGDINA)) that mitigate response biases such as acquiescence and social desirability. Core estimation backends include full Bayesian inference via Hamiltonian Monte Carlo (Stan) and a fast improved stochastic expectation-maximization (iStEM) algorithm suitable for large-scale data; FCGDINA also provides a deterministic expectation-maximization (EM) estimator. Comprehensive model evaluation uses the limited-information M2 family of goodness-of-fit statistics (Maydeu-Olivares and Joe, 2005 <doi:10.1198/016214504000002069>; 2006 <doi:10.1007/s11336-005-1295-9>) together with root mean square error of approximation (RMSEA), comparative fit index (CFI), Tucker-Lewis index (TLI), and standardized root mean square residual (SRMSR).

Last updated

onetbbcpp

3.30 score 372 downloads

EFAfactors - Determining the Number of Factors in Exploratory Factor Analysis

Provides a collection of standard factor retention methods in Exploratory Factor Analysis (EFA), making it easier to determine the number of factors. Traditional methods such as the scree plot by Cattell (1966) <doi:10.1207/s15327906mbr0102_10>, Kaiser-Guttman Criterion (KGC) by Guttman (1954) <doi:10.1007/BF02289162> and Kaiser (1960) <doi:10.1177/001316446002000116>, and flexible Parallel Analysis (PA) by Horn (1965) <doi:10.1007/BF02289447> based on eigenvalues form PCA or EFA are readily available. This package also implements several newer methods, such as the Empirical Kaiser Criterion (EKC) by Braeken and van Assen (2017) <doi:10.1037/met0000074>, Comparison Data (CD) by Ruscio and Roche (2012) <doi:10.1037/a0025697>, and Hull method by Lorenzo-Seva et al. (2011) <doi:10.1080/00273171.2011.564527>, as well as some AI-based methods like Comparison Data Forest (CDF) by Goretzko and Ruscio (2024) <doi:10.3758/s13428-023-02122-4> and Factor Forest (FF) by Goretzko and Buhner (2020) <doi:10.1037/met0000262>. Additionally, it includes a deep neural network (DNN) trained on large-scale datasets that can efficiently and reliably determine the number of factors.

Last updated

openblascppopenmp

2.06 score 1 dependents 38 scripts 388 downloads

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 Hull method by Najera et al. (2021) <DOI:10.1111/bmsp.12228>, the stepwise Wald test method (the Wald method) by Ma and de la Torre (2020) <DOI:10.1111/bmsp.12156>, 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 al. (2013) <DOI:10.1111/j.1745-3984.2012.00185.x>. Different research methods and iterative procedures during Q-matrix validating are available <DOI:10.3758/s13428-024-02547-5>.

Last updated

cpp

1.68 score 1 stars 48 scripts 283 downloads

LCPA - A General Framework for Latent Class and Profile Analysis

Provides a unified framework for finite-mixture latent variable models, including latent class analysis (LCA), latent profile analysis (LPA), latent class/profile analysis with covariates, and latent transition analysis (LTA), within one consistent interface. Estimation methods include the expectation-maximization (EM) algorithm; neural network estimation, which requires 'Python' and its dependent libraries; integration with 'Mplus', which requires an installed copy of 'Mplus'; and stochastic EM (SEM) through the optional 'flexmix', 'Rmixmod', and 'RMixtComp' backends. 'flexmix' and the default 'Rmixmod' path perform configurable warm-up trajectories and promote the best candidates to full SEM replications. 'Rmixmod' additionally exposes its native strategy interface, including chained SEM-to-EM estimation, whereas 'RMixtComp' exposes its native SEM and Gibbs controls without the external warm-up stage. Model assessment includes the Akaike information criterion (AIC), Bayesian information criterion (BIC), Schwarz information criterion (SIC), consistent AIC (CAIC), approximate weight of evidence (AWE), sample-size-adjusted BIC (SABIC), entropy, and average posterior probabilities. Model-comparison procedures include the ordinary likelihood-ratio test, the Mplus TECH11 Vuong-Lo-Mendell-Rubin and adjusted Lo-Mendell-Rubin tests, and fixed-replicate or sequential parametric bootstrap likelihood-ratio tests. Standard errors can be estimated by nonparametric bootstrap, numerical observed information, or analytic observed information based on Louis' identity. Classification-error-adjusted maximum-likelihood and Bolck-Croon-Hagenaars three-step methods support covariates predicting latent membership, initial-status and transition regressions, and latent classes or states predicting continuous and categorical external observed dependent variables. Simulation, posterior-probability, classification-error, extraction, summary, plotting, model-adjustment, and update utilities are also provided for reproducible workflows.

Last updated

openblascppopenmp

1.60 score 420 downloads

LSTMfactors - Determining the Number of Factors in Exploratory Factor Analysis by LSTM

A method for factor retention using a pre-trained Long Short Term Memory (LSTM) Network, which is originally developed by Hochreiter and Schmidhuber (1997) <doi:10.1162/neco.1997.9.8.1735>, is provided. The sample size of the dataset used to train the LSTM model is 1,000,000. Each sample is a batch of simulated response data with a specific latent factor structure. The eigenvalues of these response data will be used as sequential data to train the LSTM. The pre-trained LSTM is capable of factor retention for real response data with a true latent factor number ranging from 1 to 10, that is, determining the number of factors.

Last updated

1.00 score 213 downloads