sem
(Package: sem) :
General Structural Equation Models
sem fits general structural equation models (with both observed and unobserved variables). Observed variables are also called indicators or manifest variables; unobserved variables are also called factors or latent variables. Normally, the generic function (sem) is called directly with a semmod first argument produced by specifyModel, specifyEquations, or cfa, invoking the sem.semmod method, which in turn sets up a call to the sem.default method; thus, the user may wish to specify arguments accepted by the semmod and default methods. Similarly, for a multigroup model, sem would normally be called with a semmodList object produced by multigroupModel as its first argument, and would then generate a call to the code msemmod method.
residuals.sem
(Package: sem) :
Residual Covariances for a Structural Equation Model
These functions compute residual covariances, variance-standardized residual covariances, and normalized residual covariances for the observed variables in a structural-equation model fit by sem.
The default optimizer used by sem is optimizerSem, which employs compiled code and is integrated with the objectiveML and objectiveGLS objective functions; optimizerSem, written by Zhenghua Nie, is a modified version of the standard R nlm optimizer, which was written by Saikat DebRoy, R-core, and Richard H. Jones. The other functions call optimizers (nlm, optim, or nlminb), to fit structural equation models, and are called by the sem function. The user would not normally call these functions directly, but rather supply one of them in the optimizer argument to sem. Users may also write them own optimizer functions. msemOptimizerNlm is for fitting multigroup models, and also adapts the nlm code.
These functions calculate standardized regression coefficients for structural equation models. The function stdCoef is simply an abbreviation for standardizedCoefficients.
These functions return objective functions suitable for use with optimizers called by sem. The user would not normally call these functions directly, but rather supply one of them in the objective argument to sem. Users may also write their own objective functions. objectiveML and objectiveML2 are for multinormal maximum-likelihood estimation; objectiveGLS and objectiveGLS2 are for generalized least squares; and objectiveFIML2 is for so-called “full-information maximum-likelihood” estimation in the presence of missing data. The FIML estimator provides the same estimates as the ML estimator when there is no missing data; it can be slow because it iterates over the unique patterns of missing data that occur in the data set. objectiveML and objectiveGLS use compiled code and are therefore substantially faster. objectiveML2 and objectiveGLS2 are provided primarily to illustrate how to write sem objective functions in R. msemObjectiveML uses compiled code is for fitting multi-group models by multinormal maximum likelihood; msemObjectiveML2 is similar but doesn't use compiled code. msemObjectiveGLS uses compiled code and is for fitting multi-group models by generalized least squares.
sem-deprecated
(Package: sem) :
Deprecated Functions in the sem Package
These functions are provided for compatibility with older versions of the sem package only, and may be removed eventually. Although an effort has been made to insure backwards-compatibility, commands that worked in versions of the sem package prior to version 2.0-0 will not necessarily work in version 2.0-0 and beyond, or may not work in the same manner.
pathDiagram creates a description of the path diagram for a structural-equation-model or SEM-specification object to be processed by the graph-drawing program dot.
providing 
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