Functions

List of all current functionality

clipboard(): Copy the output of methods (e.g., summary()) for lavaan objects into the clipboard, which can be pasted in other programs such as Excel
saveFile(): Save the attributes of the lavaan object into a file
compareFit(): Compare fit measures across multiple nested or nonnested lavaan outputs

skew() and kurtosis(): Univariate skewness and excessive kurtosis
mardiaSkew() and mardiaKurtosis(): Mardia's multivariate skewness and kurtosis
mvrnonnorm(): Convenience function to generate nonnormal data using Vale and Maurelli (1983) method

chisqSmallN(): chi-squared test statistic (or difference) adjusted for small sample size
moreFitIndices() and nullRMSEA(): Calculate more fit indices from lavaan objects: Gamma Hat (GFI*), Adjusted Gamma Hat (AGFI*), Corrected Akaike Information Criterion (AICc), Stochastic Information Criterion (SIC), Corrected Bayesian Information Criterion (BIC*), Hannan-Quinn Information Criterion (HQC), and the RMSEA of the null model
miPowerFit(): Model evaluation method provided by Satorra, Saris, & van der Weld (2009) that uses modification indices and the power of modification indices
singleParamTest(): Test each constraint that defines the differences between nested models

measurementInvariance() and measurementInvarianceCat(): Multiple-group measurement invariance Test for continuous and categorical indicators, respectively
partialInvariance() and partialInvarianceCat(): A range of partial invariance tests across multiple groups for continuous and categorical indicators, respectively
longInvariance(): Longitudinal Measurement Invariance Test. This function currently can test only one factor in different timepoints
permuteMeasEq(): Find the null hypothesis distribution of fit indices in measurement invariance across groups using the permutation method

SSpower(): Power analysis for each parameter using Satorra and Bentler's (1985) method
findRMSEApower(): Find the power of rejecting bad models or retaining good models using RMSEA given sample size (MacCallum, Browne, & Suguwara, 1996)
plotRMSEApower(): Plot the power of rejecting bad models or retaining good models using RMSEA given a range of sample size
plotRMSEAdist(): Visualize the sampling distribution of RMSEA
findRMSEAsamplesize(): Find the minimum sample size given the desired power using RMSEA

findRMSEApowernested(): Find the power of rejecting a pair of different models or retaining a pair of models similar models using a pair of RMSEA values given sample size (MacCallum, Browne, & Cai, 2006)
plotRMSEApowernested(): Plot the power of rejecting a pair of different models or retaining a pair of models similar models using a pair of RMSEA values given a range of sample size
findRMSEAsamplesizenested(): Find the minimum sample size given the desired power using a pair of RMSEA

auxiliary(): Automatically add auxiliary variables to a lavaan model using full information maximum likelihood
runMI(): Fit structural equation models to multiply imputed data. The current function can optionally multiply impute missing values in the data, or users can feed the imputed data directly, which is recommended for users to have more control over the imputation process. The model is then fitted to each imputed dataset using lavaanList(), and results are returned in a lavaan.mi object. Methods for lavaan.mi objects are available to pool standard results (see a list on the class?lavaan.mi help page).
lavTestScore.mi() and modindices.mi(): Analogous to lavTestScore() and modindices() in the lavaan package, but for pooling results across multiple imputations (i.e., for lavaan.mi objects)
twostage(): Two-stage maximum likelihood in SEM. The current function will implement 2-stage ML (optionally using auxiliary variables) using lavaan to fit the model(s) in each step
fmi(): Find fractions of missing information (FMI) for summary statistics (means and (co)variances of continuous variables, thresholds and polychoric/polyserial correlations of ordered variables) from a single incomplete data set (using missing = "FIML") or a list of imputed data sets
bsBootMiss(): Model-based (Bollen-Stine) bootstrap with incomplete data
quark() and combinequark(): Principal component method to reduce the number of auxiliary variables for use in FIML estimation

indProd() and orthogonalize(): Creating indicator products without centering, with mean centering (Marsh, Wen, & Hau, 2004), with double mean centering (Lin et al., 2010), or with residual centering (Little, Bovaird, & Widaman, 2006) by all possible combinations or match-paired methods (Marsh et al., 2004)
probe2WayMC(): Probing two-way latent interaction with mean or double-mean centering
probe3WayMC(): Probing three-way latent interaction with mean or double-mean centering
probe2WayRC(): Probing two-way latent interaction with residual centering
probe3WayRC(): Probing three-way latent interaction with residual centering
plotProbe(): Plot the simple intercepts and slopes of latent interaction

efaUnrotate(): Fitting an unrotated EFA model as a lavaan model
orthRotate(), oblqRotate(), and funRotate(): Orthogonal or oblique rotation of standardized loadings obtained from the unrotated solution fitted by efaUnrotate()
ekc(): Identify the number of factors to extract based on the Empirical Kaiser Criterion (EKC)

compRelSEM(): Reliability of unit-weighted composites (e.g., sum scores or scale means) per construct. Also available for multidimensional, multilevel, and higher-order constructs, measured by continuous or categorical indicators (when enabled by lavaan). Former functions reliability() and reliabiltyL2() are now deprecated.
maximalRelia(): Maximal reliability, which is the reliability of weighted summed scores such that the weights provide the maximum value of reliability (Li, 1997).

parcelAllocation(), PAVranking(), and poolMAlloc(): Parcel allocation variability investigation (Sterba, 2011; Sterba & MacCallum, 2010; Sterba & Rights, 2016)

plausibleValues(): Draw a distribution of plausible values of factor scores, to be treated as multiple imputations.
imposeStart(): Use parameter estimates from a lavaan output as starting values of another analysis model
monteCarloCI(): Mediation analysis using the Monte Carlo method (Selig & Preacher, 2012)
splitSample(): Randomly split samples into two different halves: a training and a (cross-)validation sample
loadingFromAlpha(): Estimate standardized factor loadings given a coefficient alpha when factor loadings are equally constrained (tau-equivalence)
tukeySEM(): Calculate Tukey's WSD post-hoc test for multiple group means comparison
kd(): Generate data based on the Kaiser-Dickman (1962) algorithm
net(): Test whether models estimated with ML are equivalent or nested
htmt(): Investigate the discriminant validity using Heterotrait-Monotrait Ratio