`empirical.poly.collapse`

argument added to`itemfit()`

to plot expected score functions for polytomous items (suggested by Keri Brady)SRMSR now reported in

`M2()`

for GGUMs (suggested by Bo on the mirt-package forum)`weights`

argument added to`estfun.AllModelClass`

to allow for the inclusion of`survey.weights`

to calculate the scores`DIF()`

now simplifies the output by default rather than returning lists from`anova()`

. Wald tests are always simplifiedWhere applicable, RMSEA statistics are reported in

`itemfit()`

for tests that return suitable X2 and df componentsFix negative TLI and CFI values when using the C2 statistic from the

`M2()`

function (reported by Jake Kraska and Charlie Iaconangelo)Fix delta method SEs for

`'gpcm'`

itemtype (reported by Lennart Schneider)

When lower/upper bounded parameters are included the default optimizer is now ‘nlminb’ rather than ‘L-BFGS-B’. This is mainly due to the instability in the ‘L-BFGS-B’ algorithm which is prone to converging instantly for unknown reasons

`mdirt()`

gains a`item.Q`

list to specify Q-matrices at the item-category level for each item`createItem()`

functions gain an optional argument to the function definitions to allow for list-specified data from functions such as`mirt()`

via a silent`mirt(..., customItemsData)`

argumentlattice

`auto.key`

default now reports lines rather than points. This is now more consistent when, for example, color theme is changed to black and white in the trellis windowAdded Differential Response Function (DRF) statistics from upcoming publication (Chalmers, accepted) in a new function entitled

`DRF()`

. These are related to compensatory and non-compensatory measures of response bias for DIF, DBF, and DTF available from the SIBTEST framework but for IRT model fitted within the multiple-group estimation framework`structure`

argument added to`mdirt()`

function to allow log-linear models for simplifying the profile probability model computationsexport internally used

`traditional2mirt()`

function to transform a small selection of classical IRT parameterizations into the slope-intercept formfix

`survey.weights`

input for multiple group models (reported by Leigh Allison)fix

`itemtype = "rsm"`

block restriction when items contain unequal category lengths (reported by Aiden Loe)`SIBTEST()`

computation of beta coefficient changed to match Shealy and Stout’s (1993) form of`p_k * (Y_R - Y_F)`

(was previously`p_k * (Y_F - Y_R)`

; reported by Craig Wells). As well,`Jmin`

default is increased to 5 to avoid conservative Type I error behavior in longer testsFix negative chi-square differences in

`DIF()`

function due to non-converged sub-models (reported by Daniel McKelvey)

`M2()`

function gains a`type`

input to distinguish between the univariate-bivariate collapsed M2* statistic and the bivariate only collapsed C2 statistic (Cai and Monro, 2014). C2 can be useful for polyomous items when there are too few degrees of freedom to compute the fully collapsed M2*`multipleGroup()`

gains the`dentype`

argument to allow for mixture IRT models to be fitted (e.g.,`dentype = 'mixture-3'`

fits a three-class mixture model). This also allow modifications such as the zero-inflated IRT model to be fitted`technical`

gains a`zeroExtreme`

logical flag to assign survey weights of 0 to extreme response patterns (FALSE by default). This may be required when Woods’ extrapolation-interpolation method is used with empirical histograms to avoid ill defined extrapolated densities`fscores()`

,`itemfit()`

,`M2()`

, and`residuals()`

gain a`use_dentype_estimate`

argument to compute EAP-based scores whenever the latent trait density was estimated (e.g., via empirical histograms)Empirical histograms can now be now scaled to [0,1] using Woods’ extrapolation-interpolation method via the input

`dentype = 'empiricalhist_Woods'`

. Degrees of freedom updated to reflect this change, and 121 quadrature points are used instead of the previous 199 for better stabilitySemi-parametric Davidian curve estimation of the shape of the latent trait distribution in unidimensional IRT models was contributed by Oguzhan Ogreden, as well the associated components used within this framework (such as the interpolation-extrapolation method described by Woods, 2006). This estimation method is available through the new

`dentype`

input. mirt also now links to the`dcurver`

package to obtain the associated computation functions in the EM algorithm`M2()`

,`itemfit()`

,`SIBTEST()`

, and`fscores()`

gain an`na.rm`

logical to remove rows of missing data`fscores()`

gains a`append_response.pattern`

logical to indicate whether response patterns via the`response.pattern`

input should be appended to the factor score resultsnew

`dentype`

argument added to estimation-based functions to specify the density structure of the latent traits (default is`'Gaussian'`

). This update breaks the previous`empiricalhist`

logical option`anova()`

will accept a single fitted model object and return information related to AIC, BIC, log-likelihood, etcHannan–Quinn (HQ) Criterion added to

`anova()`

Added multidimensional version of sequential response model (e.g., Tutz, 1990). Includes

`itemtype = 'sequential'`

for the multidimensional 2PL variant, and`itemtype = 'Tutz'`

for the Rasch variantPrinting IRT parameters via

`coef(mod, IRTpars = TRUE)`

now computes the delta method for the`g`

and`u`

terms as well. Interpreting these is generally not recommended due to their bounded parameter nature (CIs can be outside the range [0,1]), but are included for posterity`createItem()`

gains a`bytecompile`

flag to indicate whether the internal functions should be byte-compiled before using (default is TRUE)Special

`GROUP`

location holder in`mirt.model()`

to index the group-level hyper-parameter terms`key2binary()`

gains a`score_missing`

flag to indicate whether missing values should be scored as 0 or left as NA`createItem()`

gains support for`derivType = 'symbolic'`

and`derivType.hss = 'symbolic'`

to symbolically compute the gradient/Hessian functions (template code-base contributed by Chen-Wei Liu)`createItem()`

gains a`derivType.hss`

argument to distinguish gradient from Hessian numerical computations`mdirt()`

gains support for`createItem()`

inputsMore plotting points added to default

`plot()`

and`itemplot()`

generics to create smoother traceline functions

fix

`simdata()`

bug for new`ggum`

itemtypefix new grouping syntax specification in

`mirt.model()`

when combining START and FIXED (reported by Garron Gianopulos)fix

`IRTpars = TRUE`

input when itemtype was`Rasch`

(reported by Benjamin Shear)

`mod2values()`

and passing`pars = 'values'`

now return`data.frame`

objects without any factor variables (previously the defaults to`data.frame()`

were used, which created factors for all categorical variables by default)Add

`monopoly`

itemtype to fit unidimensional monotonic polynomial item response model for polytomous data (see Falk and Cai, 2016)Add

`ggum`

itemtype to fit unidimensional/multidimensional graded unfolding model (e.g., Roberts & Laughlin, 1996). Special thanks to David King for providing the necessary C++ derivative functions and starting valuesSquare brackets can now be included in the

`mirt.model()`

syntax to indicate group-specific constraints, priors, starting/fixed values, and so on. These are all of the general form`"CONSTRAIN [group1, group2] = ..."`

or`"FIXED [group1] = ..."`

Added delta method for several classical IRT parameterization (via

`coef(model, IRTpars = TRUE)`

) when a suitable information matrix was previously estimated`numDeriv`

dependency removed because`numerical_deriv()`

now supports a local Richardson extrapolation type. For best accuracy, this is now used as the default throughout the package`createItem()`

and`lagrange()`

now use Richardson extrapolation as default instead of the less accurate forward/central difference method`estfun()`

function added to extract gradient information directly from fitted objects (contributed by Lennart Schneider)`simdata()`

gains an`equal.K`

argument to redraw data until \(K\) categories are populated for a given itemFix initialization of

`fscores()`

when using ‘MH’ plausible value imputations (reported by Charlie Iaconangelo)Various other small bug fixes and performance improvements, fixes for Solaris compatibility, and run a small number of examples during R CMD check

`mdirt()`

now supports latent regression covariate predictors. Associated function (e.g.,`fscores()`

) also include the latent regression information for discrete models by default`SIBTEST()`

replaced with the asymptotic sampling distribution version of CSIBTEST described by Chalmers (accepted)`calcNull`

set to`FALSE`

by defaultSandwich ACOV estimate now uses the Oakes estimate in the computations rather than the intensive Louis form (which require low-level coding of the item-level Hessian terms). Added a new

`SE.type = 'sandwich.Louis'`

for the original sandwich VCOV estimate in the previous version of mirtfix latent regression models with QMCEM and MCEM algorithms (reported by Seongho Bae)

`fscores()`

gains a`max_theta`

argument to apply upper/lower bounds to iterative searching algorithms (issue reported by Sebastian Born), and a`start`

input to set the starting values as well (primarily useful in mirtCAT to reduce iterations)`alabama`

package optimizer no longer used. Replaced with generic interface from`nloptr`

package to support numerous optimizers with greater control instead. Associated inputs (e.g.,`alabama_args`

) replaced as wellExport missing S4 methods for external R packages to import

MDIFF and MDISC no longer in normal ogive metric (1.702 scaling value removed)

added

`QMC`

option to`residuals()`

for`LD`

and`LDG2`

methods. Also globally set the number of QMC points to 5000 throughout the package for consistency`info_if_converged`

and`logLik_if_converged`

added to`technical`

list to indicate whether the information matrix and stochastic log-likelihood should be computed only when the model converges. Default is now`TRUE`

for bothadded

`'MCEM'`

method for Monte Carlo EM. An associated`MCEM_draws`

function added to the`technical`

list as well to control the number of draws throughout the EM cyclessupport for information matrix computations for QMCEM method added (e.g., Oakes, crossprod, Louis)

globally improve numerical efficiency of QMC methods, including the QMCEM estimator

include missing data values in

`itemfit()`

for parametric bootstrap methods to replicate missing data patternensure that nest-logit models have at least 3 categories (reported by Seongho Bae)

convergence set to FALSE if any

`g > u`

is found in the 4PL modelin verbose console output the log-posterior is printed when priors are included in the EM (previously was only the marginal likelihood)

various bug fixes to SIBTEST, particularly for very small sample sizes

`anova()`

LRT comparison gains a`bounded`

logical to indicate whether a bounded parameter is being compared, as well as a`mix`

argument to indicate the mixture of chi-squared distributionsMH-RM estimation

`optimizer`

argument can now be modified to`BFGS`

,`L-BFGS-B`

, and`NR`

instead of the default`NR1`

a distinction between the

`NR`

optimizer in the EM and MH-RM applications is included, where the MH-RM now defaults to`NR1`

to indicate a single Newton-Raphson update that uses an RM filtered Hessian term`method = 'SEM'`

added to perform the stochastic EM algorithm (first two stages of the MH-RM algorithm setup).

Alternatively, setting`technical = list(NCYCLES = NA)`

when using the MH-RM algorithm now returns the stochastic EM resultsadded

`multidim_matrix`

option to`iteminfo()`

to expose computation of information matricesbounded parameter spaces handled better when using the NR optimizer

various bug fixes and performance improvements

`SE.type = 'Oakes'`

set as the new default when computing standard errors via the ACOV matrix when using the EM algorithmnew

`SE.type = 'Oakes'`

to compute Oakes’ 1999 form of the observed information matrix using central difference approximation. Applicable for all IRT models (including customized IRT types)added support for

`gpcmIRT`

and`rsm`

itemtypes for traditional generalized partial credit model and Rasch rating scale model (which may be modified for a generalized rating scale model by freeing the slope parameters)`SE.type = 'Fisher'`

now supports the inclusion of latent distribution hyper-parameters. Officially, all SE-types now provide proper hyper-parameter influence in the information matriceswrapped various output objects as

`mirt_df`

,`mirt_matrix`

, and`mirt_list`

class to avoid the need for passing a`digits`

argument for rounding output in the console. Now, returned objects are never rounded, which makes writing Monte Carlo simulation code safer in that rounded results will not appear in the resultsadded Stone’s (2000) fit statistics and forthcoming PV-Q1 fit statistics to

`itemfit()`

- patched underflow bug in
`fscores()`

when EAP estimates were used in extremely long (1000+ item) tests. Error now reported when this happens. Using MAP estimates in these extreme situations is essentially equivalent and now recommended

add information about the number of freely estimated parameters to

`print()`

genericin

`plot()`

,`auto.key`

is only disabled when`facet_items = FALSE`

for dichotomous items. Also, adjusted ordering of`plot(mod, type = 'itemscore')`

to reflect actual item ordering in the dataStretched the theoretical bounds of the y-axis for score-based functions in

`plot()`

and`itemplot()`

(e.g., 3PL models will now always stretch to S(theta) = 0)`plot(mod, type = 'score')`

not supports the`which.items`

input to make expected score plots for bundles of itemspenalized term added to EM algorithm estimation subroutines to help keep the covariance matrix of the latent trait parameters positive definite in the M-step (helps convergence properties of the optimizers, especially ‘L-BFGS-B’). To turn this penalized term off use

`technical = list(keep_vcov_PD = FALSE)`

added

`type = 'itemscore'`

to`plot()`

generic to plot faceted version of the item scoring functions. Particularly useful when investigating DIF with`multipleGroup()`

better support for

`splines`

itemtype in multiple-group models

fix problem with ‘EAPsum’ in

`fscores()`

when`response.pattern`

input supplied (reported by Eva de Schipper)`plot(mod, type = 'rxx')`

now uses the latent variance in the computations (reported by Amin Mousavi)fix syntax input when customized IRT models are supplied

`df`

adjustment for the`S_X2`

item-fit statistic for models where the latent trait hyper-parameters have been estimated`itemfit()`

and`personfit()`

properly detect dichotomous Rasch models which have been defined with the constrained slopes approachargument

`'fit_stats'`

now used in`itemfit()`

to replace longer list of logicals (e.g.,`itemfit(mod, S_X2 = FALSE, X2 = TRUE, infit = FALSE, ...)`

). Now fit stats are explicitly requested through a character vector input. Default still uses the S_X2 statisticwhen using

`'lnorm'`

prior lower bound automatically set to 0, and with`'beta'`

prior the lower and upper bounds are set to [0,1]`mdirt()`

now uses`optimizer = 'nlminb'`

by defaultrevert using default ‘penalized version of the BFGS algorithm’ instead of L-BFGS-B when box-constraints are used (introduced in version 1.19)

Neale & Miller 1997 approximation added to

`PLCI()`

(default still computes exact PL CIs)`type = 'score'`

supported for multiple group models in`itemplot()`

added

`poly2dich`

function to quickly change polytomous response data to a comparable matrix of dichotomous response data

a penalized version of the BFGS algorithm is now used instead of the L-BFGS-B when upper and lower bounds are included (provides more robust estimates)

the variances of the orthogonal factors in

`bfactor()`

can now be freely estimated. This allows modeling of designs such as the testlet response model (example included in the documentation)new

`spline`

itemtype to model B-spline response functions for dichotomous models. Useful for diagnostic purposes after detecting item-misfit. Additional arguments can be passed to the`spline_args`

list input to control the behaviour of the splines for each item. Currently limited to unidimensional models only`fscores()`

gains a`plausible.type`

argument to select between normal approximation PVs or Metropolis-Hastings samples (suggested by Yang Liu)`mdirt()`

has been modified to support DINA, DINO, located latent class, and other diagnostic classification models. Additionally, the`customTheta`

input required to build customized latent class patterns has been changed from the previously cumbersome

`mdirt(..., technical = list(customTheta = Theta))`

to simply`mdirt(..., customTheta = Theta)`

`simdata()`

gains a`prob.list`

input to supply a list of matrices with probability values to be sampled from (useful when specialized response functions outside the package are required)`simdata()`

supports ‘lca’ itemtypes for latent class model generationimproved M2 accuracy when latent trait variances are estimated

corrected behaviour of

`M2()`

when linear constraints are applied (M2 test was previously too conservative when constraints were used). This affects single as well as multiple-group models (reported by Rudolf Debelak)add plausible values for latent class and related models estimated from

`mdirt()`

`multipleGroup()`

throws proper error when vertical scaling is not identified correctly due to NAsS-X2 itemfit statistic fix when very rare expected categories appear (reported by Seongho Bae)

`mdirt()`

function now includes explicit parameters for the latent class intercepts (in log-form). This implies that correct standard errors can be computed using various methods (e.g., SEM, Richardson, etc)new

`customGroup()`

function to define hyper-parameter objects for the latent trait distributions (generally assumed to be Gaussian with a mean and covariance structure)new

`boot.LR()`

function to perform a parametric bootstrap likelihood-ratio test between nested models. Useful when testing nested models which contain bounded parameters (e.g., testing a 3PL versus a 2PL model)adjust the

`lagrange()`

function to use the full information matrix (was previously only a quasi-lagrange approximation)greatly improved speed in

`simdata()`

, consequently changes the default seed

fix crash error in

`mirtmirt()`

for multidimensional models with lr.random effects (reported by Diah Wihardini)`expbeta`

prior starting values fix by setting to the mean of the prior rather than the mode (reported by Insu Paek)

`itemfit()`

function reworked so that all statistics have their own input flag (e.g.,`Zh = TRUE`

,`infit = TRUE`

, etc). Additionally, only S-X2 is computed by default and X2/G2 (and the associated graphics and tables) are computed using 10 fixed binsadded

`empirical.table`

argument to return tables of expected/observed values for`X2`

and`G2`

`group.bins`

and`group.fun`

argument added to`itemfit()`

to control the size of the bins and the central tendancy function for`X2`

and`G2`

computations`'expbeta'`

option added to implement a beta prior specifically for the`g`

and`u`

parameters which internally have been transformed to logits (performes the back transformation before computing the values)check whether multiple-group models contain enough data to estimate parameters uniquely when no constraints are applied

set the starting values the same when using

`parprior`

list or`mirt.model()`

syntax (reported by Insu Paek)`empirical_ES()`

function added for effect size estimates in DIF/DBF/DTF analyses (contributed by Adam Meade)

standardized loadings not correct when factor correlations included in confirmatory models (reported by Seongho Bae)

`MDISC`

and`MDIFF`

values were missing the 1.702 multiplicitive constant (reported by Yi-Ling Cheng)fix information trace-lines in multiple-group plots (reported by Conal Monaghan)

suppress standard errors in exploratory models when

`rotate != 'none'`

(suggested by Hao Wu)sequential schemes in

`DIF()`

generated the wrong results (reported by Adam Meade)`M2()`

was not properly accounting for latent variance terms (reported by Ismail Cuhadar)

enable

`lr.random`

input to`mixedmirt()`

for multilevel-IRT models which are not from the Rasch familyadd common

`vcov()`

and`logLik()`

methodslatent regression EM models now have standard error computation supporte with the ‘complete’, ‘forward’, ‘central’, and ‘Richardson’ methods

new

`areainfo()`

function to compute the area under information curves within specified ranges (suggested by Conal Monaghan)`method = 'BL'`

supported for multiple-group models. As well,`SE.type = 'numerical'`

included to return the observed-data ACOV matrix from the call to`optim()`

(can only be used when the`BL`

method is selected)new

`SE.type = 'FMHRM'`

to compute information matrix based on a fixed number of MHRM draws, and an associated`technical = list(MHRM_SE_draws)`

argument has been added to control the number of drawsadded

`lagrange`

(i.e., score) test function for testing whether parameters should be freed in single and multiple group models estimated with the EM algorithm`numerical_deriv`

function made available for simple numerical derivatives, which may be useful when defining fast custom itemtype derivative terms`SE.type`

used to compute the ACOV matrix gained three numerical estimates for the forward difference (‘forward’), central difference (‘central’), and Richardson extropolation (‘Richardson’)

- SE methods based on the Louis (1982) computations no longer contain NA placeholders for the latent trait hyper-parameters

added SIBTEST and crossed-SIBTEST procedures with the new function

`SIBTEST()`

added

`empirical_plot`

function for building empirical plots (with potential smoothing) when conditioning on the total scoremore low-level elements included in

`extract.mirt()`

functionadded

`grsmIRT`

itemtype for classical graded rating scale form (contributed by KwonHyun Kim)added missing analytic Hessian terms when

`gpcm_mats`

are used (contributed by Carl Falk)

- fixed row-removal bug when using
`technical = list(removeEmptyRows = TRUE)`

(reported by Aaron Kaat)

- the structure of the output objects now contains considerably fewer S4 slots, and instead are organized into more structured list elements such as
`Data`

,`Model`

,`Fit`

, and so on. Additionally, the information matrix has slot has been removed in favour of providing the asymptotic covariance matrix (a.k.a., the inverse of the information matrix)

added

`extract.mirt()`

function to allow more convenient extracting of internal elements`crossprod`

SE.type now incorporates latent variable information (replaces NA placeholders)changed the default

`full.scores = FALSE`

argument to`TRUE`

in`fscores()`

added

`profile`

argument to`plot()`

for`mdirt()`

objects so that profile plots can be generated`converge_info`

option added to`fscores()`

to return convergence informationadd

`removeEmptyRows`

option to`technical`

list

return a vector of

`NA`

s when WLE estimation has a Fisher information matrix with a determinant of 0 (reported by Christopher Gess)fix df in multiple-group models with crossed between/within constrains (reported by Leah Feuerstahler)

compute residuals when responses are sparse, and return

`NaN`

when residual could not be computed (reported by Aaron Kaat)

adjust plausible values format for multiple group objects

`simdata()`

gains a`model`

input to impute data from pre-organized models (useful in conjunction with mirtCAT or to generate datasets from already converged models). Also gains a`mins`

argument to specify what the lowest category should be for each item if`model`

is not supplied (default is 0)number of

`SEMCYCLES`

increased from 50 to 100 in the MH-RM algorithm, and RM gain rate changed from`c(.15, .65)`

to`c(.1, .75)`

further improve item fit statistics when using imputations

facet plots now try to keep the items in their respective order

panel theme for lattice plots changed from default to a lighter blue colour, and legend now automatically placed on the right hand side rather than the top

- fix for Q3 computations (noticed by Katherine Castellano)

when using prior distributions, starting values now automatically set equal to the mode of the prior distribution, and appropriate lower and upper parameter bounds are supplied

added

`NEXPLORE`

term to`mirt.model()`

to specify exploratory models via the syntaxadd

`itemGAM()`

function to provide a non-linear smoother for better understanding mis-functioning items (and without loosing established precision by reverting to purely non-parametric IRT methods)category scores are now automatically recoded to have spaces of 1, and a message is printed if/when this occurs

added

`MDISC()`

and`MDIFF()`

functionsthe inclusion of prior parameter distributions will now report the log-posterior rather than the log-likelihood. Functions such as

`anova()`

will also report Bayesian criteria rather than the previous likelihood-based model comparison statistics`impute`

argument in`itemfit()`

and`M2()`

now use plausible values instead of point estimates`START`

syntax element in`mirt.model()`

now supports multiple parameters, and`FIXED`

argument added to declare parameters as ‘fixed’ at their staring valuesadded

`LBOUND`

and`UBOUND`

syntax support in`mirt.model()`

report proper lower and upper bounds in starting values data frame and from

`mod2values()`

`invariance`

argument to`bfactor()`

now automatically indexes the second-tier factors to make multiple-group testing with`bfactor()`

easierremove

`rotate`

and`Target`

arguments from model objects, and pass these only through axillary functions such as`summary()`

,`fscores()`

, etc`model`

based arguments now can be strings, which are passed to`mirt.model()`

. This is now the preferred method for defining models syntactically, though the previous methods will still workintegration range (

`theta_lim`

) globally set to`c(-6, 6)`

, and number of default quadrature nodes have systematically increased in parameter estimation functions. This will slightly change some numerical results, but provides more consistence throughout the packageadd

`theta_lim`

arguments to various functionsbetter control of QMC grid, and more effective usage for higher dimensions

internal code organization now makes it easier to add user defined

`itemtype`

s (which can be natively added into the package, if requested)

fix conservative imputation standard errors in

`itemfit()`

and`M2()`

(reported by Irshad Mujawar)fixed plausible value draws for multidimensional latent regression models (reported by Tongyun Li)

don’t allow crossprod, Louis, or sandwich information matrices when using custom item types (reported by Charlie Rutgers)

when using

`coef(mod, printSE=TRUE)`

the`g`

and`u`

parameters are relabeled to`logit(g)`

and`logit(u)`

to represent the internal labelsadded various facet plots for three dimensional models to

`plot()`

genericsupport

`optimizer = 'nlminb'`

, and pass optimizer control arguments to a`contol`

listadded

`fixef()`

function to extract expected values implied by the fixed effect parameters in latent regression modelsadded

`gpcm_mats`

argument to estimation functions for specifying a customize scoring pattern for multidimensional generalized partial credit modelsadded

`custom_theta`

input to`fscores()`

for including customized integration gridsadd a

`suppress`

argument to`residuals()`

and`M2()`

to suppress local dependence values less than this specific valueprint a message in

`DIF()`

and`DTF()`

when hyper-parameters are not freely estimated in focal groupsconstraits for hetorogenous item names added to

`mirt.model()`

syntaxWLE support for multidimensional models added

added

`'SEcontour'`

argument to`plot()`

genericuse NA’s in

`fscores()`

when response patterns contain all NA responses (suggested by Tomasz Zoltak)

S-X2 in

`itemfit()`

now returns appropriate values for multiple-group modelsmultidimensional plausible value imputation fix (reported by KK Sasa)

`plot(..., type = 'infotrace')`

for multiple group objects fixed (reported by Danilo Pereira)

`fscores()`

nows accepts`method = "plausible"`

to draw a single plausible value set`plot()`

default type is now`score`

, and will accept rotation arguments for exploratory models (default rotation is`'none'`

)`imputeMissing()`

supports a list of plausible values to generate multiple complete datasetsnew

`custom_den`

input to`fscores()`

to use custom prior density functions for Bayesian estimatesmore optimized version of the ‘WLE’ estimator in

`fscores()`

empirical reliability added when

`method = 'EAPsum'`

in`fscores()`

new

`START`

argument in`mirt.model()`

for specifying simple starting values one parameter at a time

fix carryover print-out error in

`summary()`

when confirmatory models were estimatedbound contraints not were not included for group hyper-parameters (reported by KK Sasa)

improved estimation efficiency when using MH-RM algorithm. As a result, the default seed was changed, therefore results from previous versions will be slightly different

objects of class ‘ExploratoryClass’ and ‘ConfirmatoryClass’ have been merged into a single class ‘SingleGroupClass’ with an

`exploratory`

logical slotthe

`technical = list(SEtol)`

criteria for approximating the information matrix was lowered to 1e-4 in`mixedmirt()`

to provide better standard error estiamtes

`boot.mirt`

now uses the optimizer used to estimate the model (default previously was EM)`mixedmirt`

now supports interaction effects in random intercepts, including cross-level interactionsadded

`averageMI()`

function to compute multiple imputation averages for the plausible values methodology using Rubin’s 1987 methodplausible value imputation now available in

`fscores()`

using the new`plausible.draws`

numeric inputadd

`return.models`

argument to`DIF()`

to return estimated models with free/constrained parameterslatent regression models added to

`mixedmirt()`

for non-Rasch models using the new`lr.formula`

input`mirt.model()`

syntax can now define within individual item equality constraints by using more than 1 parameter specification name in the syntaxlatent regression models added to

`mirt()`

function by using the new`covdata`

and`formula`

inputsadded confidence envelope plots to

`PLCI.mirt`

, and throw warnings when intervals could not be located`coef()`

now accepts a`simplify`

logical, indicating whether the items should be collapsed to a matrix and returned as a list of length 2 (suggested by Michael Friendly)

bias correction in variance estimates

`mixedmirt`

when random effects are included (reported by KK Sasa)fix missing data imputation bug in

`itemfit()`

(reported by KK Sasa)M2 statistic for bifactor/two-tier models was overly conservative

better checks for numerical underflow issues

use triangle 0’s for identifying exploratory IFA models. As such, standard errors/condition numbers for exploratory models can be estimated again

`sirt`

package added to suggests list. Special thanks to Alexander Robitzsch (author of`sirt`

) for developing useful wrapper functions for mirt such as`mirt.wrapper.coef()`

,`tam2mirt()`

, and

`lavaan2mirt()`

. As well, many examples in`sirt`

demonstrate the possibility of estimating specialized IRT models with`mirt`

, such as the: Ramsay quotient, latent class, mixed Rasch, located latent class, probabilistic Guttman, nonparametric, discrete graded membership, and multidimensional IRT discrete traits, DINA, and Rasch copula models.exploratory IRT models are no longer rotated by default in

`coef()`

, and now requires an explicit`rotate`

argumentcomputation of

`S_X2`

statistic in`itemfit`

now much more stable for polytomous item typessupport for the

`plink`

package now unofficially dropped because it was removed from CRANdata inputs are now required to have category spacing codings exactly equal to 1 (e.g., [0, 1, 2, …]; patterns such as [0, 2, 3] which are implicitly missing spaces are now invalid)

`mdirt`

function added to model discrete latent variables such as latent class analysis for dichotomous and polytomous items. Can be used to model several other discrete IRT models as well, such as the located latent class model, multidimensional IRT with discrete traits, DINA models, etc. See the examples and documentation for detailsaxillary support for

`DiscreteClass`

objects added to`itemfit()`

,`M2()`

,`fscores()`

,`wald()`

, and`boot.mirt()`

the S-X2 statistic available in

`itemfit()`

has been generalized to include multidimensional modelsthe method

`'QMCEM'`

has been added for quasi-Monte Carlo integration in`mirt()`

and`multipleGroup()`

for estimating higher dimensional models with greater accuracy (suggested by Alexander Robitzsch). Several axillary function such as`fscores()`

,`itemfit()`

, and`M2()`

also now contain an`QMC`

argument (or will accept one through the … argument) to use the same integration scheme for better accuracy in higher dimensional modelsnonlinear parameter constraints for EM estimation can be specified by using the

`Rsolnp`

and`alabama`

packages by passing`optimizer = 'solnp'`

and`optimizer = 'alabama'`

, as well as the relevant package arguments through the`solnp_ags`

and`alabama_ags`

list inputs`itemnames`

argument added to`mirt.model()`

to allow model specifications using raw item names rather than location indicators`accelerate`

argument changed from logical to character vector, now allowing three potential options: ‘Ramsay’ (default), ‘squarem’, and ‘none’ for modifying the EM acceleration approach

fixed bug in

`bfactor()`

starting values when NAs were specified in the`model`

argumentadjust overly optimistic termination criteria in EM algorithm

for efficiency, the Hessian is no longer computed in

`fscores()`

unless it is required in the returned objectestimation with

`method = 'MHRM'`

now requires and explicitly`SE=TRUE`

call to compute the information matrix. The matrix is now computed using the ML estimates rather than approximated sequentially after each iteration (very unstable), and therefore a separate stage is performed. This provides much better accuracy in the computations

new

`extract.group()`

function to extract a single group object from an objects previously returned by`multipleGroup()`

return the SRMSR statistic in

`M2()`

along with the residual matrix (suggested by Dave Flora)accept

`Etable`

default input in`customPriorFun`

(suggested by Alexander Robitzsch)vignette files for the package examples are now hosted on Github and can be accessed by following the link mentioned in the vignette location in the index or

`?mirt`

help fileE-step is now computed in parallel (if available) following a

`mirtCluster()`

definitionrun no M-step optimizations by passing

`TOL = NaN`

. Useful to have the model converge instantly with all parameters exactly equal to the starting valuesconfidence envelope plots in

`itemplot()`

generate shaded regions instead of dotted lines, and confidence interval plots added to`plot()`

generic through the`MI`

inputpasses to

`fscores()`

slightly more optimized for upcoming mirtCAT package release`method = 'EAPsum'`

argument to`fscores()`

support for multidimensional models

fix forcing all SEs MHRM information matrix computations to be positive

`imputeMissing()`

crash fix for multiple-group modelsfix divide-by-0 bug in the E-step when number of items is large

fix crash in EM estimation with

`SE.type = 'MHRM'`

calculating the information matrix for exploratory item factor analysis models has been disabled since the rotational indeterminacy of the model results in improper parameter variation

changed default

`theta_lim`

to`c(-6,6)`

and number of quadrature defaults increased as well`@Data`

slot added for organizing data based arguments. Removed several data slots from estimated objects as a consequenceremoved ‘Freq’ column when passing a

`response.pattern`

argument to`fscores()`

increase number of Mstep iterations proportionally in quasi-Newton algorithms as the estimation approaches the ML location

‘rsm’ itemtype removed for now until optimized version is implemented

link to

`mirt`

vignettes on Github have been registered with the`knitr`

package and are now available through the package index`optimizer`

argument added to estimation function to switch the default optimizer. Multiple optimizers are now available, including the BFGS (EM default), L-BFGS-B, Newton-Raphson, Nelder-Mead, and SANNnew

`survey.weights`

argument can be passed to parameter estimation functions (i.e.,`mirt()`

) to apply so-called stratification/survey-weights during estimation`returnList`

argument added to`simdata()`

to return a list containing the S4 item objects, Theta matrix, and simulated datasupport custom item type

`fscores()`

computations when`response.pattern`

is passed instead of the original data`impute`

option for`itemfit()`

and`M2()`

to estimate statistics via plausible imputation when missing data are presentmultidimensional ideal-point models added for dichotomous items

M2* statistic added for polytomous item types

Bock and Lieberman (

`'BL'`

) method argument added (not recommend for serious use)

large bias correction in information matrix and standard errors for models that contain equality constraints (standard errors were too high)

drop dimensions fix for nested logit models

default

`SE.type`

changed to`crossprod`

since it is better at detecting when models are not identified compared to`SEM`

, and is generally much cheaper to compute for larger modelsM-step optimizer now automatically selected to be ‘BFGS’ if there are no bounded parameters, and ‘L-BFGS-B’ otherwise. Some models will have notably different parameter estimates because of this, but should have nearly identical model log-likelihoods

better shiny UI which adapts to the itemtype specifically, and allows for classical parameter inputs (special thanks to Jonathan Lehrfeld for providing code that inspired both these changes)

scores.only option now set to

`TRUE`

in`fscores()`

`type = 'score'`

for plot generics no longer adjusts the categories for expected test scoresM-step optimizer in EM now deters out-of-order graded response model intercepts (was a problem if the startvalues were too far from the ML estimate in graded models)

`return.acov`

logical added to`fscores()`

to return a list of matrices containing the ACOV theta values used to compute the SEs (suggested by Shiyang Su)`printCI`

logical option to`summary()`

to print confidence intervals for standardized loadingsnew

`expected.test()`

function, which is an extension of`expected.item()`

but for the whole test`mirt.model()`

syntax supports multiple * combinations in`COV =`

for more easily specifying covariation blocks between factors. Also allows variances to be freed by specifying the same factor name, e.g.,`F*F`

`full.scores.SE`

logical option for`fscores()`

to return standard errors for each respondentmultiple imputation (MI) option in

`fscores()`

, useful for obtaining less biased factor score estimates when model parameter variability is large (usually due to smaller sample size)group-level (i.e., means/covariances) equality constrains are now available for the EM algorithm

`theta_lim`

input to`plot()`

,`itemplot()`

, and`fscores()`

for modifying range of latent values evaluated

`personfit()`

crash for multipleGroup objects since itemtype slot was not filled (reported by Michael Hunter)fix crash in two-tier models when correlations are estimated (reported by David Wu)

R 3.1.0 appears to evaluate List objects differently at the c level causing strange behaviour, therefore slower R versions of some internal function (such as mirt:::reloadPars()) will be used until a patch is formed

behaviour of

`mvtnorm::dmvnorm`

changed as of version 0.9-9999, causing widely different convergence results. Similar versions of older mvtnorm functions are now implemented instead

`fitIndices()`

replaced with`M2()`

function, and currently limited to only dichotomous items of class ‘dich’`bfactor()`

default SE.type set to ‘crossprod’ rather than ‘SEM’generalized partial credit models now display fixed scoring coefs

`TOL`

convergence criteria moved outside of the`technical`

input to its own argument`restype`

argument to`residuals()`

changed to`type`

to be more consistent with the packageremoved

`fitted()`

since`residuals(model, type = 'exp')`

gives essentially the same outputmixedmirt has

`SE`

set to`TRUE`

by default to help construct a more accurate information matrixif not specified, S-EM

`TOL`

dropped to`1e-6`

in the EM, and`SEtol = .001`

for each parameter to better approximate the information matrix

two new

`SE.type`

inputs: ‘Louis’ and ‘sandwich’ for computing Louis’ 1982 computation of the observed information matrix, and for the sandwich estimate of the covariance matrix`as.data.frame`

logical option for`coef()`

to convert list to a row-stacked data.frame`type = 'scorecontour'`

added to`plot()`

for a contour plot with the expected total scores`type = 'infotrace'`

added to`itemplot()`

to plot trace lines and information on the same plot, and`type = 'tracecontour'`

for a contour plot using trace lines (suggested by Armi Lantano)`mirt.model()`

support for multi-line inputsnew

`type = 'LDG2'`

input for`residuals()`

to compute local dependence stat based on G2 instead of X2, and`type = 'Q3'`

added as wellS-EM computation of the information matrix support for latent parameters, which previously was only effective when estimation item-level parameters. A technical option has also been added to force the information matrix to be symmetric (default is set to

`TRUE`

for better numerical stability)new

`empirical.CI`

argument in`itemfit()`

used when plotting confidence intervals for dichotomous items (suggested by Okan Bulut)`printSE`

argument can now be passed to`coef()`

for printing the standard errors instead of confidence intervals. As a consequence,`rawug`

is automatically set to`TRUE`

(suggested by Olivia Bertelli)second-order test and condition number added to estimated objects when an information matrix is computed

`tables`

argument can be passed to`residuals()`

to return all observed and expected tables used in computing the LD statistics

using

`scores.only = TRUE`

for multipleGroup objects returns the correct person ordering (reported by Mateusz Zoltak)`read.mirt()`

crash fix for multiple group analyses objects (reported by Felix Hansen)updated math for

`SE.type = 'crossprod'`

`facet_items`

argument added to plot() to control whether separate plots should be constructed for each item or to merge them onto a single plotthree dimensional models supported in

`itemplot()`

for types`trace`

,`score`

,`info`

, and`SE`

new DIF() function to quicky calculate common differential item functioning routines, similar to how IRTLRDIF worked. Supports likelihood ratio testings as well as the Wald approach, and includes forward and backword sequential DIF searching methods

added a

`shiny = TRUE`

option to`itemplot()`

to run the interactive shiny applet. Useful for instructive purposes, as well as understanding how the internal parameters of mirt behave`type = 'trace'`

and`type = 'infotrace'`

support added to`plot`

generic for multiple group objects`fscores(..., method = 'EAPsum')`

returns observed and expected values, along with general fit statistics that are printed to the console and returned as a ‘fit’ attributeremoved multinomial constant in log-likelihood since it has no influence on nested model comparisons

`SE.type = 'crossprod'`

and`Fisher`

added for computing the parameter information matrix based on the variance of the Fisher scoring vector and complete Fisher information matrix, respectively`customPriorFun`

input to technical list now available for utilizing user defined prior distribution functions in the EM algorithmempirical histogram estimation now available in

`mirt()`

and`multipleGroup()`

for unidimensional models. Additional plot`type = 'empiricalhist'`

also added to the`plot()`

genericre-implement

`read.mirt()`

with better consistency checking between the`plink`

package

starting values for

`multipleGroup()`

now returns proper estimated parameter information from the`invariance`

input argumentremove

`as.integer()`

in MultipleGroup df slotpass proper item type when using custom pattern calles in

`fscores()`

return proper object in personfit when gpcm models used

`GenRandomPars`

logical argument now supported in the`technical = list()`

input. This will generate random starting values for freely estimated parameters, and can be helpful to determine if obtained solutions are local minimumsseperate

`free_var`

and`free_cov`

invariance options available in multipleGroupnew

`CONSTRAIN`

and`CONSTRAINB`

arguments in`mirt.model()`

syntax for specifying equality constraints explicitly for parameters accross items and groups. Also the`PRIOR = ...`

specification was brought back and uses a similar format as the new CONSTRAIN options`plot(..., type = 'trace')`

now supports polytomous and dichotomous tracelines, and`type = 'infotrace'`

has a better y-axis rangeremoved the ‘1PL’ itemtype since the name was too ambiguous. Still possible to obtain however by applying slope constraints to the 2PL/graded response models

`plot()`

contains a which.items argument to specify which items to plot in aggregate type, such as`'infotrace'`

and`'trace'`

`fitIndicies()`

will return`CFI.M2`

and`TLI.M2`

if the argument`calcNull = TRUE`

is passed. CFI stats also normed to fall between 0 and 1data.frame returned from

`mod2values()`

and`pars = 'values'`

now contains a column indicating the internal item classuse

`ginv()`

from MASS package to improve accuracy in`fitIndices()`

calculation of M2

fix error thrown in

`PLCI.mirt`

when parameter value is equal to the boundfix the global df values, and restrict G2 statistic when tables are too sparse

`PLCI.mirt()`

function added for computing profiled likelihood standard errors. Currently only applicable to unidimensional modelsprior distributions returned in the

`pars = 'values'`

data.frame along with the input parameters, and can be edited and returned as wellfull.scores option for

`residuals()`

to compute residuals for each row in the original data`bfactor()`

can include an additional model argument for modeling two-tier structures introduced by Cai (2010), and now supports a`'group'`

input for multiple group analysesadded a general Ramsey (1975) acceleration to EM estimation by default. Can be disable with

`accelerate = FALSE`

(and is done so automatically when estimating SEM standard errors)renamed response.vector to response.pattern in

`fscores()`

, and now supports matrix input for computing factor scores on larger data sets (suggested by Felix Hansen)total.info logical added to

`iteminfo()`

to return either total item information or information from each category`mirt.model()`

supports the so-called Q-matrix input format, along with a matrix input for the covariance termsMH-RM algorithm now accessible by passing

`mirt(..., method = 'MHRM')`

, and`confmirt()`

function removed completely.`confmirt.model()`

also renamed to`mirt.model()`

support for polynomial and interaction terms in EM estimation

lognormal priors may now be passed to parprior

iterative computations in

`fscores()`

can now be run in parallel automatically following a`mirtCluster()`

definition`mirtCluster()`

function added to make utilizing parallel cores more convenient. Globally removed the cl argument for multi-core objectsupdated documentation for data sets by adding relevant examples, and added Bock1997 data set for replicating table 3 in van der Linden, W. J. & Hambleton, R. K. (1997) Handbook of modern item response theory

general speed improvements in all functions

WLE estimation fixed and now estimates extreme response patterns

exploratory starting values no longer crash in datasets with a huge number of NAs, which caused standard deviations to be zero

math fix for beta priors

support for random effect predictors now available in

`mixedmirt()`

, along with a`randef()`

function for computing MAP predictions for the random parametersEAPsum support in

`fscores()`

for mixed item typesfor consistency with current IRT software (rather than TESTFACT and POLYFACT), the scaling constant has been set to D = 1 and fixed at this value

nominal.highlow option added to specify which categories are the highest and lowest in nominal models. Often provide better numerical stability when utilized. Default is still to use the highest and lowest categories

increase number of draws in the Monte Carlo calculation of the log-likelihood from 3000 to 5000

when itemtype all equal ‘Rasch’ or ‘rsm’ models the latent variance parameter(s) are automatically freed and estimated

`mixedmirt()`

more supportive of user defined R formulas, and now includes an internal ‘items’ argument to create the item design matrix used to estimate the intercepts. More closely mirrors the results from lme4 for Rasch models as well (special thanks to Kevin Joldersma for testing and debugging)`drop.zeros`

option added to extract.item and itemplot to reduce dimensionality of factor structures that contain slopes equal to zeroEM tolerance (TOL argument) default dropped to .0001 (originally .001)

`type = 'score'`

and`type = 'infoSE'`

added to`plot()`

generic for expected total score and joint test standard error/informationcustom latent mean and covariance matrix can be passed to

`fscores()`

for EAP, MAP, and EAPsum methods. Also applies to`personfit()`

and`itemfit()`

diagnosticsscores.only option to

`fscores()`

for returning just the estimated factor scoresbfactor can include NA values in the model to omit the estimation of specific factors for the corresponding item

limiting values in z.outfit and z.infit statistics for small sample sizes (fix suggested by Mike Linacre)

missing data gradient bug fix in MH-RM for dichotomous item models

global df fix for multidimensional confirmatory models

SEM information matrix computed with more accuracy (M-step was not identical to original EM), and fixed when equality constrains are imposed

new

`'#PLNRM'`

models to fit Suh & Bolt (2010) nested logistic models`'large'`

option added to estimation functions. Useful when the datasets being analysed are very large and organizing the data becomes a computationally burdensome task that should be avoided when fitting new models. Also, overall faster handling of datasets`plot()`

,`fitted()`

, and`residuals()`

generic support added for MultipleGroup objectsCFI and X2 model statistics added, and output now includes fit stats w.r.t. both G2 and X2

z stats added for itemfit/personfit infit and outfit statistics

supplemented EM (‘SEM’) added for calculating information matrix from EM history. By default the TOL value is dropped to help make the EM iterations longer and more stable. Supports parallel computing

added return empirical reliability (

`returnER`

) option to`fscores()`

`plot()`

supports individual item information trace lines on the same graph (dichotomous items only) with the option`type = 'infotrace'`

`createItem()`

function available for defining item types that can be passed to estimation functions. This can be used to model items not available in the package (or anywhere for that matter) with the EM or MHRM. Derivatives are computed numerically by default using the numDeriv package for defining item types on the flyMstep in EM moved to quasi-Newton instead of my home grown MV Newton-Raphson approach. Gives more stability during estimation when the Hessian is ill-conditioned, and will provide an easier front-end for defining user rolled IRT models

small bias fix in Hessian and gradients in

`mirt()`

implementation causing the likelihood to not always be increasing near maximumfix input to

`itemplot()`

when object is a list of model objectsfixed implementation of infit and outfit Rasch statistics

order of nominal category intercepts were sometimes backwards. Fixed now

S_X2 collapsed cells too much and caused negative df

`response.vector`

input now supports NA inputs (reported by Neil Rubens)

S-X2 statistic computed automatically for unidimensional models via itemfit()

EAP for sum-scores added to fscores() with method = ‘EAPsum’. Works with full.scores option as well

improve speed of estimation in multipleGroup() when latent means/variances are estimated

multipleGroup(invariance = ’’) can include item names to specify which items are to be considered invariant across groups. Useful for anchoring and DIF testing

type = ‘trace’ option added to plot() to display all item trace lines on a single graph (dichotomous items only)

default estimation method in multipleGroup() switched to ‘EM’

boot.mirt() function added for computing bootstrapped standard errors with via the boot package (which supports parallel computing as well), as well as a new option SE.type = ’’ for choosing between Bock and Lieberman or MHRM type information matrix computations

indexing items in itemplot, itemfit, and extract.item can be called using either a number or the original item name

added probtrace() function for front end users to generate probability trace functions from models

plotting item tracelines with only two categories now omits the lowest category (as is more common)

parallel option passed to calcLogLik to compute Monte Carlo log-likelihood more quickly. Can also be passed down the call stack from confmirt, multipleGroup, and mixedmirt

Confidence envelopes option added to itemplot() for trace lines and information plots

lbound and ubound parameter bounds are now available to the user for restricting the parameter estimation space

mod2values() function added to convert an estimated mirt model into the appropriate data.frame used to determine parameter estimation characteristics (starting values, group names, etc)

added imputeMissing() function to impute missing values given an estimated mirt model. Useful for checking item and person fit diagnostics and obtaining overall model fit statistics

allow for Rasch itemtype in multidimensional confirmatory models

oblimin the new default exploratory rotation (suggested by Dave Flora)

more flexible calculation of M2 statistic in fitIndicies(), with user prompt option if the internal variables grow too large and cause time/RAM problems

read.mirt() fixed when objects contain standard errors (didn’t properly line up before)

mixedmirt() fix when COV argument supplied (reported by Aaron Kaat)

fix for multipleGroup when independent groups don’t contain all potential response options (reported by Scot McNary)

prevent only using ‘free_means’ and ‘free_varcov’ in multipleGroup since this would not be identified without further constraints (reported by Ken Beath)

all dichotomous, graded rating scale, (generalized) partial credit, rating scale, and nominal models have been better optimized

wald() will now support information matrices that contain constrained parameters

confmirt.model() can accept a string inputs, which may be useful for knitr/sweave documents since the scan() function tends to hang

multipleGroup() now has the logical options bfactor = TRUE to use the dimensional reduction algorithm for when the factor pattern is structured like a bifactor model

new fitIndices() function added to compute additional model fit statistics such as M2

testinfo() function added for test information

lower bound parameters under more stringent control during estimation and are bounded to never be higher than .6

infit and outfit stats in itemfit() now work for Rasch partial credit and rating scale models

Rasch rating scale models can now be estimated with potential rsm.blocks (same as grsm model). “Generalized” rating scale models can also be estimated, though this requires manipulating the starting values directly

added AICc and sample size adjusted BIC (SABIC) information statistics

new mixedmirt() function for estimating IRT models with person and item level (e.g., LLTM) covariates. Currently only supports fixed effect predictors, but random effect predictors are being developed

more structured output when using the anova() generic

item probability functions now only permit permissible values, and models may converge even when the log-likelihood decreases during estimation. In the EM if the model does not have a strictly increasing log-likelihood then a warning message will be printed

infit and outfit statistics are now only applicable to Rasch models (as they should be), and in itemfit/personfit() a ‘method’ argument has been added to specify which factor score estimates should be used

read.mirt() re-added into the package to allow for translating estimated models into a format usable by the plink package

test standard error added to plot() generic using type = ‘SE’, and expected score plot added to itemplot() using type = ‘score’

weighted likelihood estimation (WLE) factor scores now available (without standard errors)

removed the allpars option to coef() generics and only return a named list with the (possibly rotated) item and group coefficients

information functions slightly positively biased due to logistic constant adjustment, fixed for all models. Also, information functions are now available for almost all item response models (mcm items missing)

constant (D) used in estimating logistic functions can now be modified (default is still 1.702)

partcomp models recently broken, fixed now

more than one parameter can now be passed to parprior to make specifying identical priors more convenient

relative efficiency plots added to itemplot(). Works directly for multipleGroup analysis and for comparing different item types (e.g., 1PL vs 2PL) can be wrapped into a named list

infit and outfit statistics added to personfit() and itemfit()

empirical reliability printed for each dimension when fscores(…, fulldata = FALSE) called

better system to specify fixed/free parameters and starting values using pars = ‘values’. Should allow for much better simulation based work

graded model type rating scale added (Muraki, 1990) with optional estimation ‘blocks’. Use itemtype = ‘grsm’, and the grsm.block option

for multipleGroup(), optional input added to change the current freely estimated parameters to values of a previously computed model. This will save needless iterations in the EM and MHRM since these parameters should be much closer to the new ML estimates

itemplot() supports multipleGroup objects now

analytical derivatives much more stable, although some are not yet optimized

estimation bug fix in bfactor(), and slight bias fix in mirt() estimation (introduced in version 0.4.0 when multipleGroup() added)

updated documentation and beamer slide show included for some background on MIRT and some of the packages capabilities

labels added to coef() when standard errors not computed. Also allpars = TRUE is now the default

kernel estimation moved entirely to one method. Much easier to maintain and guarantees consistency across methods (i.e., no more quasi-Newton algorithms used)

Added itemfit() and personfit() functions for uni and multidimensional models. Within itemfit empirical response curves can also be plotted for unidimensional models

Wrapped itemplot() and fscores() into S3 function for better documentation. Also response curve now are all contained in individual plots

Added free.start list option for all estimation functions. Allows a quicker way to specify free and fixed parameters

Added iteminfo() and extract.item() to calculate the item information and extract desired items

Multiple group estimation available with the multipleGroup() function. Uses the EM and MHRM as the estimation engines. The MHRM seems to be faster at two factors+ though and naturally should be more accurate, therefore it is set as the default

wald() function added for testing linear constraints. Useful in situations for testing sets of parameters rather than estimating a new model for a likelihood ratio test

Methods that use the MHRM can now estimate the nominal, gpcm, mcm, and 4PL models

fscores computable for multiple group objects and in general play nicer with missing data (reported by Judith Conijn). Also, using the options full.scores = TRUE has been optimized with Rcpp

Oblique rotation bug fix for fscores and coef (reported by Pedro A. Barbetta)

Added the item probability equations in the ?mirt documentation for reference

General bug fixes as usual that were spawned from all the added features. Overall, stay frosty.

Individual classes now correspond to the type of methods: ExploratoryClass, ConfirmatoryClass, and MultipleGroupClass

plot and itemplot now works for confmirt objects

mirt can now make use of confmirt.model specified objects and hence be confirmatory as well

stochastic estimation of factor scores removed entirely, now only quadrature based methods for all objects. Also, bfactor returned objects now will estimate all the factors scores instead of just the general dimension

Standard errors for mirt now automatically calculated (borrowed from running a tweaked MHRM run)

radically changed the underlying mechanisms for the estimation functions and in doing so have decided that polymirt() was redundant and could be replaced completely by calling confmirt(data, number_of_factors). The reason for the change was to facilitate a wider range or MIRT models and to allow for easier extensions to future multiple group analysis and multilevel modelling

new univariate and MV models are available, including the 1-4 parameter logistic generalized partial credit, nominal, and multiple choice models. These are called by specifying a character vector called ‘itemtype’ of length nitems with the options ‘2PL’,‘3PL’,‘4PL’,‘graded’,‘gpcm’, ‘nominal’, or ‘mcm’; use ‘PC2PL’ and ‘PC3PL’ for partially-compensatory items. If itemtype = ‘1PL’ or ‘Rasch’, then the 1-parameter logistic/1-parameter ordinal or Rasch/partial credit models are estimated for all the data. The default assumes that items are either ‘2PL’ or ‘graded’, as before.

flexible user defined linear equality restrictions may be imposed on all estimation functions, so too can prior parameter distributions, start values, and choice of which parameters to estimate. These all follow these general 2 steps:

- Call the function as you would normally would but use, for example, mirt(data, 1, startvalues = ‘index’) to return the start values as they are indexed
- Edit them as you please (without changing the structure), then input them back into the function as mirt(data, 1, startvalues = editedstartvalues).

This is true for the parprior (MAP priors), constrain (linear equality constraints), and freepars (parameters freely estimated), each with their own little quirk. All inputs are lists with named parameters for easy identification and manipulation. Note that this means that the partial credit model and Rasch models may be calculated as well by modifying either the start values and constraints accordingly (e.g., constrain all slopes to be equal to 1/1.702 and not freely estimated for the classical Rasch model, or all equal but estimated for the 1PL model)

number of confmirt.model() options decreased due to the new way to specify item types, startvalues, prior parameter distributions, and constraints

plink package has not kept up with item information curves, so I’ll implement my own for now. Replaced plink item plots from ‘itemplots’ function with ones that I rolled

package descriptions and documentation updated

coef() now prints slightly different output, with the new option ‘allpars = TRUE’ to display all the item and group parameters, returned as a list

simdata() updated to support new item types

more accurate standard errors for MAP and ML factor scores, and specific factors in bfactorClass objects can now be estimated for all methods

dropped the ball and had lots of bug fixes this round. Future commits will avoid this problem by utilizing the testthat package to test code extensively before release

internal change in confmirt function to move MHRM engine outside the function for better maintenance

theta_angle added to mirt and polymirt plots for changing the viewing angle w.r.t theta_1

null model no longer calculated when missing data present

fixed item slope models estimated in mirt() with associated standard errors

null model computed, allowing for model statistics such as TLI

documentation changes

many back end technical details about estimation moved to technical lists

support for all GPArotation methods and options, including Target rotations

polymirt() uses confmirt() estimation engine

4PL support for mirt() and bfactor(), treating the upper bound as fixed

coef() now has a rotate option for returning rotated IRT parameters

Fixed translation bug in the C++ code from bfactor() causing illegal vector length throw

Fixed fscores() bug when using polychotomous items for mirt() and bfactor()

pass rotate=‘rotation’ from mirt and polymirt to override default ‘varimax’ rotation at estimation time (suggested by Niels Waller)

RMSEA, G^2, and p set to NaN instead of internal placeholder when there are missing data

df adjusted when missing data present

oblique rotations return invisible factor correlation matrix

degrees of freedom correctly adjusted when using noncompensatory items

confmirtClass reorganized to work with S4 methods, now work more consistently with methods.

fixed G^2 and log-likelihood in logLik() when product terms included

bugfix in drawThetas when noncompensatory items used

bugfixes for fscores, itemplot, and generic functions

read.mirt() added for creating a suitable plink object

mirt() and bfactor() can now accommodate polychotomous items using an ordinal IRT scheme

itemplot() now makes use of the handy plink package plots, giving a good deal of flexibility.

Generic plot()’s now use lattice plots extensively

Ported src code into Rcpp for future tweaking.

Added better fitted() function when missing data exist (noticed by Erin Horn)

ML estimation of factor scores for mirt and bfactor

RMSEA statistic added for all fitted models

Nonlinear polynomial estimation specification for confmirt models, now with more consistent returned labels

Provide better identification criteria for confmirt() (suggested by Hendrik Lohse)

parameter standard errors added for mirt() (1 factor only) and bfactor() models

bfactor() values that are ommited are recoded to NA in summary and coef for better viewing

‘technical’ added for confmirt function, allowing for various tweaks and varying beta prior weights

product relations added for confmirt.model(). Specified by enclosing in brackets and using an asterisk

documentation fixes with roxygenize

- allow lower bound beta priors to vary over items (suggested by James Lee)

- bias fix for mirt() function (noticed by Pedro Barbetta)