پکیج های مربوط به معادلات ساختاری و تحلیل عاملی را در نرم افزار R

در لینک زیر می توانید پکیج های مربوط به معادلات ساختاری و تحلیل عاملی را در نرم افزار R را مشاهده کنید:

http://cran.r-project.org/web/views/Psychometrics.html

Ordinary factor analysis (FA) Principal component analysis (PCA) can be fitted with prcomp() (based on svd(), preferred) as well as princomp() (based on eigen() for compatibility with S-PLUS). Additional rotation methods for FA based on gradient projection algorithms Non-graphical solution to the Cattell scree test. Some graphical PCA representations	GPArotation nFactors psy GPArotation nFactors	1
general (i.e., latent-variable) SEMs by FIML, and structural equations in observed-variable models by 2SLS Categorical variables in SEMs Observed-variables models, including nonlinear simultaneous-equations models Partial least-squares estimation Graphical models	sem polycor systemfit pls gR SocialSciences	2
path analysis confirmatory factor analysis structural equation modeling growth curve models lavaan model syntax which allows users to express their models in a compact way and allows for ML, GLS, WLS, robust ML using Satorra-Bentler corrections, and FIML for data with missing values. meanstructures and multiple groups standardized solutions, fit measures, modification indices	lavaan	3
complex survey structural equation modeling (SEM) structural equation models (SEM) factor analysis Multivariate regression models with latent variables and many other latent variable models while correcting estimates, standard errors, and chi-square-derived fit measures for a complex sampling design.  clustering, stratification sampling weights, and finite population corrections	lavaan.survey	4
structural equation models censored and dichotomous variables via a probit link formulation	lava lava.tobit	5
structural equation models using partial least squares (PLS) Segmentation trees in PLS path modeling.	semPLS plspm pathmox	6
Monte Carlo simulations	simsem	7
a package of add on functions that can aid in fitting SEMs in R	semTools	8
path diagrams and visual analysis for outputs of various SEM packages	semPlot	9
tests of difference in fit for mean and covariance structure models	SEMModComp	10
factor analysis based on a genetic algorithm for optimization This makes it possible to impose a wide range of restrictions on the factor analysis model, whether using exploratory factor analysis, confirmatory factor analysis, or a new estimator called semi-exploratory factor analysis (SEFA).	FAiR	11
FA and PCA with supplementary individuals and supplementary quantitative/qualitative variables Sampling from the posterior for ordinal and mixed factor models	FactoMineR MCMCpack	12
nonlinear PCA (aka categorical PCA) nonlinear canonical correlation analysis (models of the Gifi-family).	homals	13
Independent component analysis (ICA)	fastICA	14
robust principal components	pcaPP	15
parallel analysis of continuous, ordered (including dichotomous/binary as a special case) or mixed type of data associated with a principal components analysis	pcaPA	16
functions such as fa.parallel() and VSS() for estimating the appropriate number of factors/components as well as ICLUST() for item clustering	psych	17
An interface between the EQS software for SEM and R	REQS	18
estimation of a wide variety of advanced multivariate statistical models  a library of functions and optimizers that allow you to quickly and flexibly define an SEM model and estimate parameters given observed data.	OpenMX ( link )	19
to automate latent variable model estimation and interpretation using Mplus.	MplusAutomation	20