Showing 13 of total 13 results (show query)
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 2 days ago.
factor-analysisgrowth-curve-modelslatent-variablesmissing-datamultilevel-modelsmultivariate-analysispath-analysispsychometricsstatistical-modelingstructural-equation-modeling
454 stars 16.82 score 8.4k scripts 218 dependents
ecmerkle
blavaan:Bayesian Latent Variable Analysis
Fit a variety of Bayesian latent variable models, including confirmatory factor analysis, structural equation models, and latent growth curve models. References: Merkle & Rosseel (2018) <doi:10.18637/jss.v085.i04>; Merkle et al. (2021) <doi:10.18637/jss.v100.i06>.
Maintained by Edgar Merkle. Last updated 9 days ago.
bayesian-statisticsfactor-analysisgrowth-curve-modelslatent-variablesmissing-datamultilevel-modelsmultivariate-analysispath-analysispsychometricsstatistical-modelingstructural-equation-modelingcpp
92 stars 10.84 score 183 scripts 3 dependents
bioc
MOFA2:Multi-Omics Factor Analysis v2
The MOFA2 package contains a collection of tools for training and analysing multi-omic factor analysis (MOFA). MOFA is a probabilistic factor model that aims to identify principal axes of variation from data sets that can comprise multiple omic layers and/or groups of samples. Additional time or space information on the samples can be incorporated using the MEFISTO framework, which is part of MOFA2. Downstream analysis functions to inspect molecular features underlying each factor, vizualisation, imputation etc are available.
Maintained by Ricard Argelaguet. Last updated 5 months ago.
dimensionreductionbayesianvisualizationfactor-analysismofamulti-omics
319 stars 10.02 score 502 scripts
jhorzek
mxsem:Specify 'OpenMx' Models with a 'lavaan'-Style Syntax
Provides a 'lavaan'-like syntax for 'OpenMx' models. The syntax supports definition variables, bounds, and parameter transformations. This allows for latent growth curve models with person-specific measurement occasions, moderated nonlinear factor analysis and much more.
Maintained by Jannik H. Orzek. Last updated 4 months ago.
factor-analysislavaanopenmxstructural-equation-modelingcpp
3 stars 5.93 score 47 scripts
wjschne
ggdiagram:Object-Oriented Diagram Plots with ggplot2
The ggdiagram package creates path diagrams with an object-oriented approach and plots diagrams with ggplot2.
Maintained by W. Joel Schneider. Last updated 5 days ago.
diagramsfactor-analysisggplot2path-analysiss7structural-equation-modeling
32 stars 5.43 score
keefe-murphy
IMIFA:Infinite Mixtures of Infinite Factor Analysers and Related Models
Provides flexible Bayesian estimation of Infinite Mixtures of Infinite Factor Analysers and related models, for nonparametrically clustering high-dimensional data, introduced by Murphy et al. (2020) <doi:10.1214/19-BA1179>. The IMIFA model conducts Bayesian nonparametric model-based clustering with factor analytic covariance structures without recourse to model selection criteria to choose the number of clusters or cluster-specific latent factors, mostly via efficient Gibbs updates. Model-specific diagnostic tools are also provided, as well as many options for plotting results, conducting posterior inference on parameters of interest, posterior predictive checking, and quantifying uncertainty.
Maintained by Keefe Murphy. Last updated 1 years ago.
bayesian-nonparametricsdimension-reductionfactor-analysisgaussian-mixture-modelmodel-based-clustering
7 stars 5.25 score 51 scripts
egeminiani
penfa:Single- And Multiple-Group Penalized Factor Analysis
Fits single- and multiple-group penalized factor analysis models via a trust-region algorithm with integrated automatic multiple tuning parameter selection (Geminiani et al., 2021 <doi:10.1007/s11336-021-09751-8>). Available penalties include lasso, adaptive lasso, scad, mcp, and ridge.
Maintained by Elena Geminiani. Last updated 4 years ago.
factor-analysislassolatent-variablesmultiple-groupoptimizationpenalizationpsychometrics
3 stars 4.48 score 5 scripts
quantmeth
Rnest:Next Eigenvalue Sufficiency Test
Determine the number of dimensions to retain in exploratory factor analysis. The main function, nest(), returns the solution and the plot(nest()) returns a plot.
Maintained by P.-O. Caron. Last updated 8 days ago.
exploratory-data-analysisfactor-analysis
2 stars 4.07 score 13 scripts
jpritikin
pcFactorStan:Stan Models for the Paired Comparison Factor Model
Provides convenience functions and pre-programmed Stan models related to the paired comparison factor model. Its purpose is to make fitting paired comparison data using Stan easy. This package is described in Pritikin (2020) <doi:10.1016/j.heliyon.2020.e04821>.
Maintained by Joshua N. Pritikin. Last updated 2 years ago.
bayesian-inferencefactor-analysispaired-comparisonsstancpp
2 stars 4.00 score
cfwp
FMradio:Factor Modeling for Radiomics Data
Functions that support stable prediction and classification with radiomics data through factor-analytic modeling. For details, see Peeters et al. (2019) <arXiv:1903.11696>.
Maintained by Carel F.W. Peeters. Last updated 5 years ago.
factor-analysismachine-learningradiomics
11 stars 3.74 score 2 scripts
hdarjus
sparvaride:Variance Identification in Sparse Factor Analysis
This is an implementation of the algorithm described in Section 3 of Hosszejni and Frühwirth-Schnatter (2022) <doi:10.48550/arXiv.2211.00671>. The algorithm is used to verify that the counting rule CR(r,1) holds for the sparsity pattern of the transpose of a factor loading matrix. As detailed in Section 2 of the same paper, if CR(r,1) holds, then the idiosyncratic variances are generically identified. If CR(r,1) does not hold, then we do not know whether the idiosyncratic variances are identified or not.
Maintained by Darjus Hosszejni. Last updated 2 years ago.
econometricsfactor-analysislatent-factorsparameter-identificationcpp
1 stars 3.70 score 4 scripts
knickodem
kfa:K-Fold Cross Validation for Factor Analysis
Provides functions to identify plausible and replicable factor structures for a set of variables via k-fold cross validation. The process combines the exploratory and confirmatory factor analytic approach to scale development (Flora & Flake, 2017) <doi:10.1037/cbs0000069> with a cross validation technique that maximizes the available data (Hastie, Tibshirani, & Friedman, 2009) <isbn:978-0-387-21606-5>. Also available are functions to determine k by drawing on power analytic techniques for covariance structures (MacCallum, Browne, & Sugawara, 1996) <doi:10.1037/1082-989X.1.2.130>, generate model syntax, and summarize results in a report.
Maintained by Kyle Nickodem. Last updated 1 years ago.
cross-validationfactor-analysispsychometricsscale-development
7 stars 3.54 score 7 scripts
teebusch
mifa:Multiple Imputation for Exploratory Factor Analysis
Impute the covariance matrix of incomplete data so that factor analysis can be performed. Imputations are made using multiple imputation by Multivariate Imputation with Chained Equations (MICE) and combined with Rubin's rules. Parametric Fieller confidence intervals and nonparametric bootstrap confidence intervals can be obtained for the variance explained by different numbers of principal components. The method is described in Nassiri et al. (2018) <doi:10.3758/s13428-017-1013-4>.
Maintained by Tobias Busch. Last updated 4 years ago.
2 stars 3.00 score 5 scripts