سایت inside-R سایتی است شامل مجموعه منابع پروژه R. آدرس سایت:
مثلاً در لینک زیر از پکیج CDM، شاخص اطلاعات کولبک-لیبلر در مدل های شناختی-تشخیصی که در پکیج نیز آوره شده است بررسی شده است.
http://www.inside-r.org/packages/cran/CDM/docs/cdi.kli
پکیج های مربوط به تحلیل ساختار دانش در نرم افزار R
در لینک زیر می توانید پکیج های مربوط به تحلیل ساختار دانش در نرم افزار R را مشاهده کنید:
http://cran.r-project.org/web/views/Psychometrics.html
Functions and example datasets for the psychometric theory of knowledge spaces. Data analysis methods and procedures for simulating data and transforming different formulations in knowledge space theory. |
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Basic functionality to generate, handle, and manipulate deterministic knowledge structures based on sets and relations Functions for fitting probabilistic knowledge structures |
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پکیج های مربوط به دیگر نرم افزارهای مربوط به روانسنجی در نرم افزار R در لینک زیر می توانید پکیج های مربوط به دیگر نرم افزارهای مربوط به روانسنجی در نرم افزار R را مشاهده کنید: http://cran.r-project.org/web/views/Psychometrics.html
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Infrastructure for psychometric modeling such as data classes (e.g., for paired comparisons) and basic model fitting functions (e.g., for Rasch and Bradley-Terry models). |
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Recursive partitioning based on psychometric models, employing the general MOB algorithm (from package party) Currently, only Bradley-Terry trees are provided. |
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Psychometric mixture models based on flexmix infrastructure (at the moment Rasch mixture models and Bradley-Terry mixture models). |
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Functions for non-IRT equating under both random groups and nonequivalent groups with anchor test designs Mean, linear, equipercentile and circle-arc equating as are methods for univariate and bivariate presmoothing of score distributions Specific equating methods currently supported include Tucker, Levine observed score, Levine true score, Braun/Holland, frequency estimation, and chained equating. |
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IRT and non-IRT based statistical indices proposed in the literature for detecting answer copying on multiple-choice examinations |
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Latent class analysis with random effects Function lca() Polytomous variable latent class analysis |
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computation of simple, more-sample, and stepwise configural frequency analysis (CFA). |
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Coefficents for interrater reliability and agreements |
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Generates design matrices for analysing real paired comparisons and derived paired comparison data (Likert type items / ratings or rankings) using a loglinear approach Fits loglinear Bradley-Terry model (LLBT) exploting an eliminate feature Computes pattern models for paired comparisons, rankings, and ratings. Some treatment of missing values (MCAR and MNAR). |
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Bradley-Terry models for paired comparisons Elimination-by-aspects models |
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Psychophysical data Functions to estimate the contribution of the n scales to the judgment by a maximum likelihood method under several hypotheses of how the perceptual dimensions interact. |
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Functions and example datasets for Fechnerian scaling of discrete object sets Computes Fechnerian distances among objects representing subjective dissimilarities, and other related information |
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Functions for nonparametric estimation of a psychometric function and for estimation of a derived threshold and slope, and their standard deviations and confidence intervals |
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Confidence intervals for standardized effect sizes |
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parametric and nonparametric causal mediation analysis conduct sensitivity analysis for certain parametric models |
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Functions for data screening, testing moderation, mediation, and estimating power |
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Social networks with relations at different levels. Multiple networks data sets with routines that combine algebraic structures like the partially ordered semigroup with the existing relational bundles found in multiple networks. |
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Visualizing data as networks |
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Social Relations Analyses for round robin designs Functionality of the SOREMO software New functions like the handling of missing values, significance tests for single groups, or the calculation of the self enhancement index. |
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Fitting and testing multinomial processing tree models, a class of statistical models for categorical data with latent parameters package. The link probabilities of a tree-like graph and represent the cognitive processing steps executed to arrive at observable response categories Analysis of multinomial processing tree (MPT) models. |
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Beta regression for modeling beta-distributed dependent variables, e.g., rates and proportions |
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functions to compare two correlations based on either dependent or independent groups |
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A set of tools that implement profile analysis and cross-validation techniques |
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A GUI for entering test items and obtaining raw and transformed scores. |
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در لینک زیر می توانید پکیج های مربوط به نظریه کلاسیک آزمون در نرم افزار R را مشاهده کنید:
http://cran.r-project.org/web/views/Psychometrics.html
score multiple-choice responses reliability analyses item analyses transform scores onto different scales |
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Functions for correlation theory, meta-analysis (validity generalization), reliability, item analysis, inter-rater reliability, and classical utility are contained |
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Functions to statistically compare two or more alpha coefficients based on either dependent or independent groups of individuals |
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Calculates and plots the step-by-step Cronbach-Mesbach curve. |
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Cronbach alpha, kappa coefficients intra-class correlation coefficients (ICC) Functions for ICC computation |
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A number of routines for scale construction and reliability analysis useful for personality and experimental psychology |
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Computes measures from generalizability theory |
(not on CRAN) |
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در لینک زیر می توانید پکیج های مربوط به مقیاس پردازی چندبعدی را در نرم افزار R را مشاهده کنید:
http://cran.r-project.org/web/views/Psychometrics.html
multidimensional scaling (MDS) based on stress minimization by means of majorization: Simple smacof on symmetric dissimilarity matrices, smacof for rectangular matrices (unfolding models), smacof with constraints on the configuration, three-way smacof for individual differences (including constraints for idioscal, indscal, and identity), and spherical smacof (primal and dual algorithm). |
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multiway method to decompose a tensor (array) of any order, as a generalisation of SVD also supporting non-identity metrics and penalisations. 2-way SVD with these extensions Some other multiway methods: PCAn (Tucker-n) and PARAFAC/CANDECOMP with extensions. |
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functionalities for computing classical MDS using the cmdscale() function. Sammon mapping sammon() non-metric MDS isoMDS() |
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Non-metric MDS with metaMDS() Function nmds() Some routines Function for metric MDS |
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Principal coordinate analysis with capscale()pco() and with dudi.pco() |
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maximum likelihood difference scaling (MLDS). |
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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
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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 |
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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 |
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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 |
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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 |
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structural equation models censored and dichotomous variables via a probit link formulation |
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structural equation models using partial least squares (PLS) Segmentation trees in PLS path modeling. |
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Monte Carlo simulations |
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a package of add on functions that can aid in fitting SEMs in R |
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path diagrams and visual analysis for outputs of various SEM packages |
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tests of difference in fit for mean and covariance structure models |
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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). |
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FA and PCA with supplementary individuals and supplementary quantitative/qualitative variables Sampling from the posterior for ordinal and mixed factor models |
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nonlinear PCA (aka categorical PCA) nonlinear canonical correlation analysis (models of the Gifi-family). |
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Independent component analysis (ICA) |
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robust principal components |
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parallel analysis of continuous, ordered (including dichotomous/binary as a special case) or mixed type of data associated with a principal components analysis |
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functions such as fa.parallel() and VSS() for estimating the appropriate number of factors/components as well as ICLUST() for item clustering |
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An interface between the EQS software for SEM and R |
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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 ) |
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to automate latent variable model estimation and interpretation using Mplus. |
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