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Parametric IRT

Important Functions in eRm

  • RM(data), PCM(data), RSM(data), LTM(data), LPCM(data), LRSM(data), LLRA(data,mpoints,groups): Model fitting functions
  • summary(obj), residuals(obj), coef(obj), anova(obj): Generics
  • gofIRT(obj), LRtest(obj), Waldtest(obj), NPtest(obj): Test and Statistics for Model fit
  • plotICC(obj), plotGOF(obj), plotDIF(obj), plotPImap(obj), plotPWmap(obj),plotGR(obj), plotTR(obj): Plot functions
  • person.parameter(obj): Extract person parameters
  • item_info(obj), test_info(obj): item or scale information
  • person.fit(obj), item.fit(obj): person and item fit statistics

Important Functions in ltm

  • rasch(data), ltm(data), tpm(data), grm(data), gpcm(data) : Model fitting functions (with constraints)
  • GoF(obj), margins(obj), unidimtest(obj): Tests for Model fit
  • plot(obj),summary(obj), residuals(obj), coef(obj), anova(obj): Generics
  • factor.scores(obj): Extract person parameters
  • information(obj): item or scale information
  • person.fit(obj), item.fit(obj): person and item fit statistics

Note: Not all functions are necessarily working with all object classes.

Important Functions in mirt

  • mirt(data,model,itemtype), bfactor(data), mixedmirt(data,model,formula): Model fitting functions for different itemtypes
  • mirt.model(): Model set up for confirmatory IRT and constraints
  • multipleGroups(data,model,itemtype),DIF(obj): Multiple group and DIF functions
  • wald(obj): Test and Statistics for Model fit
  • itemplot(obj,options): Plot functions (many different options)
  • plot(obj),summary(obj), fitted(obj), residuals(obj), coef(obj), anova(obj): Generics
  • fscores(obj): Extract person parameters
  • iteminfo(obj), testinfo(obj), expected.item(obj): item or scale information
  • M2(obj), itemfit(obj): person and item fit statistics

Nonparametric IRT

Important Functions in KernSmoothIRT

  • ksIRT(data,key,weights): Model fitting function. format specifies whether multiple choice (1), rating scale or partial credit (2) or nominal treatment (3) or allows mixed formats. Other scoring rules can be specified with weights.
  • plot(obj,plottype): Plot functions (many different options)
  • subjthetaML(obj): Extract person parameters
  • subjEIS(obj), subjETS(obj), subjscoreML(obj), subjOCC(obj): subject-wise scores (expected item and test, ML scores; OCC gives all incl. observed)

Important Functions in mokken

  • coefH(data): Scalability coefficients
  • aisp(data): Scale partitioning
  • check.iio(data),check.pmatrix(data),check.restscore(data), check.monotonicity(data): Assumption checks

DIF

Important Functions in difR

  • difLord(), difGenLord() (u and nu DIF in two and multiple groups)
  • difRaju() (un and nu DIF for two groups)
  • difLRT() (u DIF in Rasch models)
  • dichoDif(),genDichoDif(): Convenience function to check DIF (all methods for 2 or more groups)

Important Functions in psychotree

  • raschtree(formula),pctree(formula), rstree(formula): Model-based recursive partitioning of models
  • plot(obj),summary(obj),coef(obj): Generics

Mixtures

Important Functions for Mixtures

  • mRm::mrm(data,cl=groups) Mixed dichotomous Rasch models
  • psychomix::raschmix(formula, data, k=groups) Mixed dichotomous Rasch models with possible concomitant variables
  • mixRasch::mixRasch(data,n.c=groups) Dichotomous and polytomous Rasch models.
  • plot(obj),summary(obj),coef(obj): Generics