Showing 25 of total 25 results (show query)
michaelhallquist
MplusAutomation:An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus
Leverages the R language to automate latent variable model estimation and interpretation using 'Mplus', a powerful latent variable modeling program developed by Muthen and Muthen (<https://www.statmodel.com>). Specifically, this package provides routines for creating related groups of models, running batches of models, and extracting and tabulating model parameters and fit statistics.
Maintained by Michael Hallquist. Last updated 2 months ago.
68.7 match 86 stars 12.96 score 664 scripts 13 dependents
data-edu
tidyLPA:Easily Carry Out Latent Profile Analysis (LPA) Using Open-Source or Commercial Software
Easily carry out latent profile analysis ("LPA"), determine the correct number of classes based on best practices, and tabulate and plot the results. Provides functionality to estimate commonly-specified models with free means, variances, and covariances for each profile. Follows a tidy approach, in that output is in the form of a data frame that can subsequently be computed on. Models can be estimated using the free open source 'R' packages 'Mclust' and 'OpenMx', or using the commercial program 'MPlus', via the 'MplusAutomation' package.
Maintained by Joshua M Rosenberg. Last updated 1 years ago.
10.5 match 57 stars 8.71 score 121 scripts
cjvanlissa
tidySEM:Tidy Structural Equation Modeling
A tidy workflow for generating, estimating, reporting, and plotting structural equation models using 'lavaan', 'OpenMx', or 'Mplus'. Throughout this workflow, elements of syntax, results, and graphs are represented as 'tidy' data, making them easy to customize. Includes functionality to estimate latent class analyses, and to plot 'dagitty' and 'igraph' objects.
Maintained by Caspar J. van Lissa. Last updated 6 days ago.
7.6 match 58 stars 10.69 score 330 scripts 1 dependents
yrosseel
lavaan:Latent Variable Analysis
Fit a variety of latent variable models, including confirmatory factor analysis, structural equation modeling and latent growth curve models.
Maintained by Yves Rosseel. Last updated 3 days ago.
factor-analysisgrowth-curve-modelslatent-variablesmissing-datamultilevel-modelsmultivariate-analysispath-analysispsychometricsstatistical-modelingstructural-equation-modeling
3.8 match 453 stars 16.83 score 8.4k scripts 217 dependents
olivierpds
MplusLGM:Automate Latent Growth Mixture Modelling in 'Mplus'
Provide a suite of functions for conducting and automating Latent Growth Modeling (LGM) in 'Mplus', including Growth Curve Model (GCM), Growth-Based Trajectory Model (GBTM) and Latent Class Growth Analysis (LCGA). The package builds upon the capabilities of the 'MplusAutomation' package (Hallquist & Wiley, 2018) to streamline large-scale latent variable analyses. “MplusAutomation: An R Package for Facilitating Large-Scale Latent Variable Analyses in Mplus.” Structural Equation Modeling, 25(4), 621–638. <doi:10.1080/10705511.2017.1402334> The workflow implemented in this package follows the recommendations outlined in Van Der Nest et al. (2020). “An Overview of Mixture Modeling for Latent Evolutions in Longitudinal Data: Modeling Approaches, Fit Statistics, and Software.” Advances in Life Course Research, 43, Article 100323. <doi:10.1016/j.alcr.2019.100323>.
Maintained by Olivier Percie du Sert. Last updated 1 months ago.
16.4 match 4 stars 3.60 score
markustjansen
ThurMod:Thurstonian CFA and Thurstonian IRT Modeling
Fit Thurstonian forced-choice models (CFA (simple and factor) and IRT) in R. This package allows for the analysis of item response modeling (IRT) as well as confirmatory factor analysis (CFA) in the Thurstonian framework. Currently, estimation can be performed by 'Mplus' and 'lavaan'. References: Brown & Maydeu-Olivares (2011) <doi:10.1177/0013164410375112>; Jansen, M. T., & Schulze, R. (in review). The Thurstonian linked block design: Improving Thurstonian modeling for paired comparison and ranking data.; Maydeu-Olivares & Böckenholt (2005) <doi:10.1037/1082-989X.10.3.285>.
Maintained by Markus Thomas Jansen. Last updated 1 years ago.
16.2 match 3.00 score 2 scripts
paul-buerkner
thurstonianIRT:Thurstonian IRT Models
Fit Thurstonian Item Response Theory (IRT) models in R. This package supports fitting Thurstonian IRT models and its extensions using 'Stan', 'lavaan', or 'Mplus' for the model estimation. Functionality for extracting results, making predictions, and simulating data is provided as well. References: Brown & Maydeu-Olivares (2011) <doi:10.1177/0013164410375112>; Bürkner et al. (2019) <doi:10.1177/0013164419832063>.
Maintained by Paul-Christian Bürkner. Last updated 11 months ago.
5.7 match 32 stars 7.11 score 15 scripts 1 dependents
amices
mice:Multivariate Imputation by Chained Equations
Multiple imputation using Fully Conditional Specification (FCS) implemented by the MICE algorithm as described in Van Buuren and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>. Each variable has its own imputation model. Built-in imputation models are provided for continuous data (predictive mean matching, normal), binary data (logistic regression), unordered categorical data (polytomous logistic regression) and ordered categorical data (proportional odds). MICE can also impute continuous two-level data (normal model, pan, second-level variables). Passive imputation can be used to maintain consistency between variables. Various diagnostic plots are available to inspect the quality of the imputations.
Maintained by Stef van Buuren. Last updated 5 days ago.
chained-equationsfcsimputationmicemissing-datamissing-valuesmultiple-imputationmultivariate-datacpp
1.9 match 462 stars 16.50 score 10k scripts 154 dependents
kkholst
lava:Latent Variable Models
A general implementation of Structural Equation Models with latent variables (MLE, 2SLS, and composite likelihood estimators) with both continuous, censored, and ordinal outcomes (Holst and Budtz-Joergensen (2013) <doi:10.1007/s00180-012-0344-y>). Mixture latent variable models and non-linear latent variable models (Holst and Budtz-Joergensen (2020) <doi:10.1093/biostatistics/kxy082>). The package also provides methods for graph exploration (d-separation, back-door criterion), simulation of general non-linear latent variable models, and estimation of influence functions for a broad range of statistical models.
Maintained by Klaus K. Holst. Last updated 2 months ago.
latent-variable-modelssimulationstatisticsstructural-equation-models
2.0 match 33 stars 12.85 score 610 scripts 476 dependents
jeroendmulder
powRICLPM:Perform Power Analysis for the RI-CLPM and STARTS Model
Perform user-friendly power analyses for the random intercept cross-lagged panel model (RI-CLPM) and the bivariate stable trait autoregressive trait state (STARTS) model. The strategy as proposed by Mulder (2023) <doi:10.1080/10705511.2022.2122467> is implemented. Extensions include the use of parameter constraints over time, bounded estimation, generation of data with skewness and kurtosis, and the option to setup the power analysis for Mplus.
Maintained by Jeroen Mulder. Last updated 5 months ago.
4.3 match 4 stars 5.56 score 10 scripts
simongrund1
mitml:Tools for Multiple Imputation in Multilevel Modeling
Provides tools for multiple imputation of missing data in multilevel modeling. Includes a user-friendly interface to the packages 'pan' and 'jomo', and several functions for visualization, data management and the analysis of multiply imputed data sets.
Maintained by Simon Grund. Last updated 1 years ago.
imputationmissing-datamixed-effectsmultilevel-datamultilevel-models
1.8 match 29 stars 12.36 score 246 scripts 153 dependents
kss2k
modsem:Latent Interaction (and Moderation) Analysis in Structural Equation Models (SEM)
Estimation of interaction (i.e., moderation) effects between latent variables in structural equation models (SEM). The supported methods are: The constrained approach (Algina & Moulder, 2001). The unconstrained approach (Marsh et al., 2004). The residual centering approach (Little et al., 2006). The double centering approach (Lin et al., 2010). The latent moderated structural equations (LMS) approach (Klein & Moosbrugger, 2000). The quasi-maximum likelihood (QML) approach (Klein & Muthén, 2007) (temporarily unavailable) The constrained- unconstrained, residual- and double centering- approaches are estimated via 'lavaan' (Rosseel, 2012), whilst the LMS- and QML- approaches are estimated via 'modsem' it self. Alternatively model can be estimated via 'Mplus' (Muthén & Muthén, 1998-2017). References: Algina, J., & Moulder, B. C. (2001). <doi:10.1207/S15328007SEM0801_3>. "A note on estimating the Jöreskog-Yang model for latent variable interaction using 'LISREL' 8.3." Klein, A., & Moosbrugger, H. (2000). <doi:10.1007/BF02296338>. "Maximum likelihood estimation of latent interaction effects with the LMS method." Klein, A. G., & Muthén, B. O. (2007). <doi:10.1080/00273170701710205>. "Quasi-maximum likelihood estimation of structural equation models with multiple interaction and quadratic effects." Lin, G. C., Wen, Z., Marsh, H. W., & Lin, H. S. (2010). <doi:10.1080/10705511.2010.488999>. "Structural equation models of latent interactions: Clarification of orthogonalizing and double-mean-centering strategies." Little, T. D., Bovaird, J. A., & Widaman, K. F. (2006). <doi:10.1207/s15328007sem1304_1>. "On the merits of orthogonalizing powered and product terms: Implications for modeling interactions among latent variables." Marsh, H. W., Wen, Z., & Hau, K. T. (2004). <doi:10.1037/1082-989X.9.3.275>. "Structural equation models of latent interactions: evaluation of alternative estimation strategies and indicator construction." Muthén, L.K. and Muthén, B.O. (1998-2017). "'Mplus' User’s Guide. Eighth Edition." <https://www.statmodel.com/>. Rosseel Y (2012). <doi:10.18637/jss.v048.i02>. "'lavaan': An R Package for Structural Equation Modeling."
Maintained by Kjell Solem Slupphaug. Last updated 8 days ago.
interaction-effectinteraction-effectslatent-moderated-structural-equationslavaan-syntaxlmsmoderationqmlquasi-maximum-likelihoodrlangrlanguagesemstructural-equation-modelingstructural-equation-modelsopenblascppopenmp
2.6 match 6 stars 8.42 score 54 scripts
anthonyraborn
ShortForm:Automatic Short Form Creation
Performs automatic creation of short forms of scales with an ant colony optimization algorithm and a Tabu search. As implemented in the package, the ant colony algorithm randomly selects items to build a model of a specified length, then updates the probability of item selection according to the fit of the best model within each set of searches. The algorithm continues until the same items are selected by multiple ants a given number of times in a row. On the other hand, the Tabu search changes one parameter at a time to be either free, constrained, or fixed while keeping track of the changes made and putting changes that result in worse fit in a "tabu" list so that the algorithm does not revisit them for some number of searches. See Leite, Huang, & Marcoulides (2008) <doi:10.1080/00273170802285743> for an applied example of the ant colony algorithm, and Marcoulides & Falk (2018) <doi:10.1080/10705511.2017.1409074> for an applied example of the Tabu search.
Maintained by Anthony Raborn. Last updated 4 months ago.
ant-colony-optimizationmachine-learning-algorithmsshortformsimulated-annealingtabu-search
4.7 match 10 stars 4.59 score 13 scripts
jhhmuc
holland:Statistics for Holland's Theory of Vocational Choice
Offers a convenient way to compute parameters in the framework of the theory of vocational choice introduced by J.L. Holland, (1997). A comprehensive summary to this theory of vocational choice is given in Holland, J.L. (1997). Making vocational choices. A theory of vocational personalities and work environments. Lutz, FL: Psychological Assessment.
Maintained by Joerg-Henrik Heine. Last updated 4 years ago.
15.4 match 1.00 score 1 scripts
robinhankin
multipol:Multivariate Polynomials
Various utilities to manipulate multivariate polynomials. The package is almost completely superceded by the 'spray' and 'mvp' packages, which are much more efficient.
Maintained by Robin K. S. Hankin. Last updated 2 years ago.
3.3 match 3.26 score 15 scripts 4 dependents
jakewplantz
mplusParallel.automation:Parallel Processing Automation for 'Mplus'
Offers automation tools to parallelize 'Mplus' operations when using 'R' for data generation. It facilitates streamlined integration between 'Mplus' and 'R', allowing users to run and manage multiple 'Mplus' models simultaneously and efficiently in 'R'.
Maintained by Jake Plantz. Last updated 1 years ago.
5.8 match 1.70 score 3 scripts
cran
MplusTrees:Decision Trees with Structural Equation Models Fit in 'Mplus'
Uses recursive partitioning to create homogeneous subgroups based on structural equation models fit in 'Mplus', a stand-alone program developed by Muthen and Muthen.
Maintained by Sarfaraz Serang. Last updated 2 years ago.
9.0 match 1.00 scorecran
mlVAR:Multi-Level Vector Autoregression
Estimates the multi-level vector autoregression model on time-series data. Three network structures are obtained: temporal networks, contemporaneous networks and between-subjects networks.
Maintained by Sacha Epskamp. Last updated 1 years ago.
2.0 match 3.46 score 81 scripts 2 dependents
sooyongl
GRShiny:Graded Response Model
Simulation and analysis of graded response data with different types of estimators. Also, an interactive shiny application is provided with graphics for characteristic and information curves. Samejima (2018) <doi:10.1007/978-1-4757-2691-6_5>.
Maintained by Sooyong Lee. Last updated 2 years ago.
1.8 match 3.70 score 3 scripts
maria-pro
esem:Exploratory Structural Equiation Modeling ESEM
The package is developed to support the tutorial on using ESEM with LSAC dataset. The package uses "tidyverse","psych","lavaan","semPlot" package and provide additional functions to conduct ESEM. The package provides general functions to complete ESEM, including esem_c(), creation of target matrix if it is used make_target(), generation of CFA model syntax esem_cfa_syntax(). A sample data is provided. the package include a sample of SDQ LSAC data in sdq_lsac. The package vignette presents the tutorial demonstrating the use of ESEM on SDQ LSAC data
Maintained by Maria Prokofieva. Last updated 2 years ago.
1.7 match 3 stars 3.48 score 6 scripts
mmansolf
AlignLV:Multiple Group Item Response Theory Alignment Helpers for 'lavaan' and 'mirt'
Allows for multiple group item response theory alignment a la 'Mplus' to be applied to lists of single-group models estimated in 'lavaan' or 'mirt'. Allows item sets that are overlapping but not identical, facilitating alignment in secondary data analysis where not all items may be shared across assessments.
Maintained by Maxwell Mansolf. Last updated 5 months ago.
0.5 match 3.00 score 2 scriptscran
SBSDiff:Satorra-Bentler Scaled Chi-Squared Difference Test
Calculates a Satorra-Bentler scaled chi-squared difference test between nested models that were estimated using maximum likelihood (ML) with robust standard errors, which cannot be calculated the traditional way. For details see Satorra & Bentler (2001) <doi:10.1007/bf02296192> and Satorra & Bentler (2010) <doi:10.1007/s11336-009-9135-y>. This package may be particularly helpful when used in conjunction with 'Mplus' software, specifically when implementing the complex survey option. In such cases, the model estimator in 'Mplus' defaults to ML with robust standard errors.
Maintained by Frank D. Mann. Last updated 7 years ago.
0.8 match 1.30 score 3 scripts