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

پکیج های مربوط به Correspondence Analysis در نرم افزار R

در لینک زیر می توانید پکیج های مربوط به Correspondence Analysis را در نرم افزار را مشاهده کنید:

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

comprises two parts, one for simple correspondence analysis and one for multiple and joint correspondence analysis. Within each part, functions for computation, summaries and visualization in two and three dimensions are provided, including options to display supplementary points and perform subset analyses	ca	1	Correspondence Analysis (CA)
Simple and canonical CA  different scaling methods such as standard scaling, Benzecri scaling, centroid scaling, and Goodman scaling two- and three-dimensional joint plots including confidence ellipsoids  alternative plotting possibilities in terms of transformation plots, Benzecri plots, and regression plots	anacor	2
interactive Biplots	BiplotGUI	3
Homogeneity analysis aka multiple CA and various Gifi extensions Hull plots, span plots, Voronoi plots, star plots, projection plots	homals	4
Simple and multiple correspondence analysis	MASS	5
functions covering, e.g., principal components, simple and multiple, fuzzy, non-symmetric, and decentered correspondence analysis	ade4	6
predictive and symmetric co-correspondence analysis (CoCA) models	cocorresp	7
several factor analytic methods CA including supplementary row and/or column points and multiple correspondence analysis (MCA) with supplementary individuals, supplementary quantitative variables and supplementary qualitative variables	FactoMineR	8
basic ordination methods, including non-metric multidimensional scaling The constrained ordination methods include constrained analysis of proximities, redundancy analysis, and constrained (canonical) and partially constrained correspondence analysis	vegan	9
SVD based multivariate exploratory methods such as PCA, CA, MCA (as well as a Hellinger form of CA)	ExPosition	10

پکیج های مربوط به نظریه سوال-پاسخ در نرم افزار R

در لینک زیر می توانید پکیج های مربوط به نظریه سوال-پاسخ در نرم افزار را مشاهده کنید:

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

extended Rasch models, i.e. the ordinary Rasch model for dichotomous data (RM), the linear logistic test model (LLTM), the rating scale model (RSM) and its linear extension (LRSM), the partial credit model (PCM) and its linear extension (LPCM) using conditional ML estimation	eRm	1	Item Response Theory (IRT):
simple RM functions for estimating Birnbaum's 2- and 3-parameter models based on a marginal ML approach graded response model for polytomous data linear multidimensional logistic model	ltm	2
unidimensional and multidimensional item response models multifaceted models latent regression models drawing plausible values.	TAM	3
analysis of dichotomous and polytomous response data using unidimensional and multidimensional latent trait models under the IRT paradigm Exploratory and confirmatory models with quadrature (EM) or stochastic (MHRM) methods Confirmatory bi-factor and two-tier analyses for modeling item testlets  Multiple group analysis and mixed effects designs for detecting differential item functioning and modelling item and person covariates.	mirt	4
functions for Nominal Response Model and the Nested Logit Model for multiple-choice items and other polytomous response formats. uni- and multidimensional item response models (especially for locally dependent item responses) and some exploratory methods (DETECT, LSDM, model-based reliability) in sirt	mcIRT	5
multidimensional polytomous Rasch model Mueller's continuous rating scale model	pcIRT	6
IRT models under (1) multidimensionality assumption, (2) discreteness of latent traits, (3) binary and ordinal polytomous items	MultiLCIRT	7
Conditional maximum likelihood estimation via the EM algorithm and information-criterion-based model selection in binary mixed Rasch models mixture Rasch models, including the dichotomous Rasch model, the rating scale model, and the partial credit model	mRm psychomix  mixRasch	8
Calibration of item and ability parameters unidimensional and multidimensional methods such as Mean/Mean, Mean/Sigma, Haebara, and Stocking-Lord methods for dichotomous (1PL, 2PL and 3PL) and/or polytomous (graded response, partial credit/generalized partial credit, nominal, and multiple-choice model) items. The multidimensional methods include the Reckase-Martineau method and extensions of the Haebara and Stocking-Lord method.	plink	9
direct, chain and average (bisector) equating coefficients with standard errors using Item Response Theory (IRT) methods for dichotomous items	equateIRT
calibrates the parameters for Samejima's Continuous IRT Model via EM algorithm and Maximum Likelihood compute item fit residual statistics, to draw empirical 3D item category response curves, to draw theoretical 3D item category response curves, and to generate data under the CRM for simulation studies	EstCRM
DIF in dichotomously scored items uniform and non-uniform DIF effects can be detected	difR
logistic regression framework for detecting various types of DIF	lordif
penalty approach to DIF in Rasch models with multiple (metric) covariates	DIFlasso	10
functions to perform Raju, van der Linden and Fleer's (1995) Differential Item and Item Functioning analyses functions to use the Monte Carlo Item Parameter Replication (IPR) approach for obtaining the associated statistical significance tests cut-off points	DFIT	12
computarized adaptive testing using IRT methods.	catR	13
maximum likelihood estimates and pseudo-likelihood estimates of parameters of Rasch models for polytomous (or dichotomous) items and multiple (or single) latent traits Robust standard errors for the pseudo-likelihood estimates	plRasch	14
multilevel Rasch model Functions for mixed-effects models with crossed or partially crossed random effects  Polytomous models Tree-structured item response models of the GLMM family	lme4, nlme, MCMCglmm ordinal lme4  irtrees	15
Nonparametric IRT analysis automated item selection algorithm, and various checks of model assumptions Forward Search for Mokken scale analysis. It detects outliers, it produces several types of diagnostic plots.	mokken fwdmsa	16
nonparametric item and option characteristic curves using kernel smoothing. smoothing bandwidth using cross-validation and a variety of exploratory plotting tools.	KernSmoothIRT	17
construction of exact Rasch model tests by generating random zero-one matrices with given marginals.	RaschSampler	18
Simple Rasch computations such a simulating data and joint maximum likelihood	MiscPsycho	19
estimate multidimensional subject parameters (MLE and MAP) such as personnal pseudo-guessing, personal fluctuation, personal inattention assess person fit identify misfit type  generate misfitting response patterns make correction while estimating the proficiency level considering potential misfit at the same time	irtProb	20
classification accuracy and consistency under Item Response Theory only works for 3PL IRT models (or 2PL or 1PL) and only for independent cut scores	cacIRT	21
simple common interface to the estimation of item parameters in IRT models for binary responses with three different programs (ICL, BILOG-MG, and ltm, and a variety of functions useful with IRT models.	irtoys	22
cognitive diagnosis models (DINA, DINO, GDINA, RRUM, LCDM, pGDINA, mcDINA) general diagnostic model (GDM) structured latent class analysis (SLCA)	CDM	23
Gaussian ordination, related to logistic IRT Maximum likelihood estimation through canonical correspondence analysis	VGAM	24
multilevel IRT models   joint hierarchically built up likelihood for estimating a two-parameter normal ogive model for responses and a log-normal model for response times	mlirt cirt	25
Bayesian approaches for estimating item and person parameters by means of Gibbs-Sampling Bayesian IRT and roll call analysis	MCMCpack pscl	26
commands to drive the dot program from graphviz to produce a graph useful in deciding whether a set of binary items might have a latent scale with non-crossing ICCs.	latdiag	27
to factor out logic and math common to IRT fitting, diagnostics, and analysis	rpf	28
examine classification accuracy and consistency under IRT models	classify	29
graphical tools for plotting item-person maps	WrightMap	30