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

measEq.syntax(): Automates writing lavaan model syntax for various levels of measurement equivalence/invariance, across independent groups as well as dependent/repeated measurements (e.g., longitudinal or dyadic factor models)
partialInvariance() and partialInvarianceCat(): A range of partial invariance tests across multiple groups for continuous and categorical indicators, respectively
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
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

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).
maximalRelia(): Maximal reliability, which is the reliability of weighted summed scores such that the weights provide the maximum value of reliability (Li, 1997).
AVE(): The average variance extracted (i.e., average factor-variance saturation across indicators, analogous to communality) is not a reliability function, but was formerly included among indices returned by the deprecated reliability() function.

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
ekc(): Identify the number of factors to extract in EFA based on the Empirical Kaiser Criterion (EKC)