Package: semTools 0.5-6.941

Terrence D. Jorgensen

semTools: Useful Tools for Structural Equation Modeling

Provides tools for structural equation modeling, many of which extend the 'lavaan' package; for example, to pool results from multiple imputations, probe latent interactions, or test measurement invariance.

Authors:Terrence D. Jorgensen [aut, cre], Sunthud Pornprasertmanit [aut], Alexander M. Schoemann [aut], Yves Rosseel [aut], Patrick Miller [ctb], Corbin Quick [ctb], Mauricio Garnier-Villarreal [ctb], James Selig [ctb], Aaron Boulton [ctb], Kristopher Preacher [ctb], Donna Coffman [ctb], Mijke Rhemtulla [ctb], Alexander Robitzsch [ctb], Craig Enders [ctb], Ruben Arslan [ctb], Bell Clinton [ctb], Pavel Panko [ctb], Edgar Merkle [ctb], Steven Chesnut [ctb], Jarrett Byrnes [ctb], Jason D. Rights [ctb], Ylenio Longo [ctb], Maxwell Mansolf [ctb], Mattan S. Ben-Shachar [ctb], Mikko Rönkkö [ctb], Andrew R. Johnson [ctb]

semTools_0.5-6.941.tar.gz
semTools_0.5-6.935.zip(r-4.5)semTools_0.5-6.935.zip(r-4.4)semTools_0.5-6.935.zip(r-4.3)
semTools_0.5-6.935.tgz(r-4.4-any)semTools_0.5-6.935.tgz(r-4.3-any)
semTools_0.5-6.935.tar.gz(r-4.5-noble)semTools_0.5-6.941.tar.gz(r-4.4-noble)
semTools_0.5-6.941.tgz(r-4.4-emscripten)semTools_0.5-6.941.tgz(r-4.3-emscripten)
semTools.pdf |semTools.html✨
semTools/json (API)
NEWS

# Install 'semTools' in R:
install.packages('semTools', repos = c('https://simsem.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Bug tracker:https://github.com/simsem/semtools/issues

Datasets:
  • dat2way - Simulated Dataset to Demonstrate Two-way Latent Interaction
  • dat3way - Simulated Dataset to Demonstrate Three-way Latent Interaction
  • datCat - Simulated Data set to Demonstrate Categorical Measurement Invariance
  • exLong - Simulated Data set to Demonstrate Longitudinal Measurement Invariance
  • simParcel - Simulated Data set to Demonstrate Random Allocations of Parcels

On CRAN:

90 exports 74 stars 7.30 score 6 dependencies 31 dependents 155 mentions 724 scripts 10.6k downloads

Last updated 3 months agofrom:8c35480e9f. Checks:OK: 1. Indexed: yes.

TargetResultDate
Doc / VignettesOKSep 13 2024

Exports:auxiliaryAVEbsBootMisscalculate.D2cfa.2stagecfa.auxiliarycfa.michisqSmallNclipboardcombinequarkcompareFitcompRelSEMdiscriminantValidityefa.ekcefaUnrotatefindRMSEApowerfindRMSEApowernestedfindRMSEAsamplesizefindRMSEAsamplesizenestedfitmeasuresfitMeasuresfmifunRotategrowth.2stagegrowth.auxiliarygrowth.mihisthtmtimposeStartindProdkdkurtosislavaan.2stagelavaan.auxiliarylavaan.milavTestLRT.milavTestScore.milavTestWald.miloadingFromAlphalongInvariancelrv2ordmardiaKurtosismardiaSkewmaximalReliameasEq.syntaxmeasurementInvariancemeasurementInvarianceCatmiPowerFitmodificationindices.mimodificationIndices.mimodindices.mimonteCarloCImoreFitIndicesmvrnonnormnetnullRMSEAoblqRotateorthogonalizeorthRotateparcelAllocationpartialInvariancepartialInvarianceCatPAVrankingpermuteMeasEqplausibleValuesplotProbeplotRMSEAdistplotRMSEApowerplotRMSEApowernestedpoolMAllocprobe2WayMCprobe2WayRCprobe3WayMCprobe3WayRCquarkreliabilityreliabilityL2residualCovariaterunMIsaveFilesem.2stagesem.auxiliarysem.mishowsingleParamTestskewsplitSampleSSpowertukeySEMtwostage

Dependencies:lavaanMASSmnormtnumDerivpbivnormquadprog

Partial Invariance

Rendered frompartialInvariance.Rnwusingutils::Sweaveon Sep 13 2024.

Last update: 2014-10-01
Started: 2014-08-28

Readme and manuals

Help Manual

Help pageTopics
Implement Saturated Correlates with FIMLauxiliary cfa.auxiliary growth.auxiliary lavaan.auxiliary sem.auxiliary
Calculate average variance extractedAVE
Class For the Results of Bollen-Stine Bootstrap with Incomplete DataBootMiss-class hist,BootMiss-method show,BootMiss-method summary,BootMiss-method
Bollen-Stine Bootstrap with the Existence of Missing DatabsBootMiss
Small-_N_ correction for chi^2 test statisticchisqSmallN
Copy or save the result of 'lavaan' or 'FitDiff' objects into a clipboard or a fileclipboard saveFile
Combine the results from the quark functioncombinequark
Build an object summarizing fit indices across multiple modelscompareFit
Composite Reliability using SEMcompRelSEM
Simulated Dataset to Demonstrate Two-way Latent Interactiondat2way
Simulated Dataset to Demonstrate Three-way Latent Interactiondat3way
Simulated Data set to Demonstrate Categorical Measurement InvariancedatCat
Calculate discriminant validity statisticsdiscriminantValidity
Class For Rotated Results from EFAEFA-class show,EFA-method summary,EFA-method
Empirical Kaiser criterionefa.ekc
Simulated Data set to Demonstrate Longitudinal Measurement InvarianceexLong
Find the statistical power based on population RMSEAfindRMSEApower
Find power given a sample size in nested model comparisonfindRMSEApowernested
Find the minimum sample size for a given statistical power based on population RMSEAfindRMSEAsamplesize
Find sample size given a power in nested model comparisonfindRMSEAsamplesizenested
Class For Representing A Template of Model Fit ComparisonsFitDiff-class show,FitDiff-method summary,FitDiff-method
Fraction of Missing Information.fmi
Assessing Discriminant Validity using Heterotrait-Monotrait Ratiohtmt
Specify starting values from a lavaan outputimposeStart
Make products of indicators using no centering, mean centering, double-mean centering, or residual centeringindProd orthogonalize
Generate data via the Kaiser-Dickman (1962) algorithm.kd
Finding excessive kurtosiskurtosis
'emmeans' Support Functions for 'lavaan' Modelsemm_basis.lavaan lavaan2emmeans recover_data.lavaan
Find standardized factor loading from coefficient alphaloadingFromAlpha
Calculate Population Moments for Ordinal Data Treated as Numericlrv2ord
Finding Mardia's multivariate kurtosismardiaKurtosis
Finding Mardia's multivariate skewnessmardiaSkew
Calculate maximal reliabilitymaximalRelia
Syntax for measurement equivalencemeasEq.syntax
Class for Representing a Measurement-Equivalence Modelas.character,measEq.syntax-method measEq.syntax-class show,measEq.syntax-method summary,measEq.syntax-method update,measEq.syntax-method
Modification indices and their power approach for model fit evaluationmiPowerFit
Monte Carlo Confidence Intervals to Test Functions of Parameter EstimatesmonteCarloCI monteCarloMed
Calculate more fit indicesmoreFitIndices
Generate Non-normal Data using Vale and Maurelli (1983) methodmvrnonnorm
Nesting and Equivalence Testingnet
Class For the Result of Nesting and Equivalence TestingNet-class show,Net-method summary,Net-method
Calculate the RMSEA of the null modelnullRMSEA
Class for a lavaan Model Fitted to Multiple Imputationsanova,OLDlavaan.mi-method coef,OLDlavaan.mi-method fitMeasures,OLDlavaan.mi-method fitmeasures,OLDlavaan.mi-method fitted,OLDlavaan.mi-method fitted.values,OLDlavaan.mi-method nobs,OLDlavaan.mi-method OLDlavaan.mi-class resid,OLDlavaan.mi-method residuals,OLDlavaan.mi-method show,OLDlavaan.mi-method summary,OLDlavaan.mi-method vcov,OLDlavaan.mi-method
Random Allocation of Items to Parcels in a Structural Equation ModelparcelAllocation
Partial Measurement Invariance Testing Across GroupspartialInvariance partialInvarianceCat
Parcel-Allocation Variability in Model RankingPAVranking
Permutation Randomization Tests of Measurement Equivalence and Differential Item Functioning (DIF)permuteMeasEq
Class for the Results of Permutation Randomization Tests of Measurement Equivalence and DIFhist,permuteMeasEq-method permuteMeasEq-class show,permuteMeasEq-method summary,permuteMeasEq-method
Plausible-Values Imputation of Factor Scores Estimated from a lavaan ModelplausibleValues
Plot a latent interactionplotProbe
Plot the sampling distributions of RMSEAplotRMSEAdist
Plot power curves for RMSEAplotRMSEApower
Plot power of nested model RMSEAplotRMSEApowernested
Combine sampling variability with parcel-allocation variability by pooling results across M parcel-allocationspoolMAlloc
Probing two-way interaction on the no-centered or mean-centered latent interactionprobe2WayMC
Probing two-way interaction on the residual-centered latent interactionprobe2WayRC
Probing three-way interaction on the no-centered or mean-centered latent interactionprobe3WayMC
Probing three-way interaction on the residual-centered latent interactionprobe3WayRC
Quarkquark
Residual-center all target indicators by covariatesresidualCovariate
semTools: Useful Tools for Structural Equation ModelingsemTools-package semTools
Simulated Data set to Demonstrate Random Allocations of ParcelssimParcel
Single Parameter Test Divided from Nested Model ComparisonsingleParamTest
Finding skewnessskew
Randomly Split a Data Set into HalvessplitSample
Power for model parametersSSpower
Tukey's WSD post-hoc test of means for unequal variance and sample sizetukeySEM
Fit a lavaan model using 2-Stage Maximum Likelihood (TSML) estimation for missing data.cfa.2stage growth.2stage lavaan.2stage sem.2stage twostage
Class for the Results of 2-Stage Maximum Likelihood (TSML) Estimation for Missing Dataanova,twostage-method coef,twostage-method fitted,twostage-method fitted.values,twostage-method nobs,twostage-method resid,twostage-method residuals,twostage-method show,twostage-method summary,twostage-method twostage-class vcov,twostage-method