Changelog • irtQ

irtQ 1.4.0

Bug Fixes

ctt() returned a standardized alpha (alpha_std) that was too large, and could exceed 1, when a test included a constant item. The constant item is now kept in the item count as in the raw alpha, and the value is 0 when only one item varies, as is the raw alpha. An undefined standardized alpha or mean discrimination is NA instead of NaN.
ctt(), ctt_distr(), and freq_score() silently left out of their counts a score within rounding error of a whole number, such as (0.1 + 0.2) * 10. Such a score is now counted as that whole number.
freq_score() tabulated a factor by its level codes (1, 2, 3, …) instead of its labels. A factor is now converted through its labels.
ctt_distr() listed an option twice in selected-response mode when the responses wrote it in different ways, such as "1" and "01", which inflated the frequencies and percentages. The option is now listed once.
score_resp() and ctt_distr() did not treat a response with surrounding spaces, such as " 9", as missing when missing = "9". Responses are now compared with missing after trimming spaces.
score_resp() and ctt_distr() silently scored every response to an item as wrong when its key value contained a comma, such as "1,5". They now stop with an error, because every item must have exactly one correct option.
ctt() did not flag an item whose discrimination is undefined, such as an item with a constant score, so the item could pass the crit.dis check. Such an item is now flagged with "discrimination undefined".
shape_df_fipc() misplaced the parameter columns of the new items when the fixed items included a polytomous item and every new item was dichotomous, which changed a threshold of a fixed polytomous item. The new items now follow the columns of the fixed items. FIPC results built from such metadata should be rerun.
The GPCM probabilities of prm(), traceline(), and info() were distorted when an exponent exceeded 700, and the values at the other thetas and items of the same call changed with them. Each row is now rescaled by its own maximum. Results in the usual range change by less than 1e-12, and prm() no longer returns a GPCM probability of exactly 0.
info() returned NaN instead of 0 for a dichotomous item at a theta far from the item location.
traceline() returned a vector instead of a one-row matrix in prob.cats for a polytomous item when a single theta was given, which made plot() fail.
drm() checked each probability against the guessing parameter of another item, and the expected item score of traceline() included categories that the item does not have. Probabilities and expected scores could differ by about 1e-10, and item parameter estimates by about 1e-9.
traceline() and info() stopped with an error for a two-category GRM or GPCM item, such as one made by bring.mirt(), and for a 3PLM or DRM item with an NA guessing parameter, for which drm() returned NA. An NA guessing parameter is now treated as 0, so a two-category GRM or GPCM item is computed as a 2PLM item, also when the test has polytomous items of the same model.
shape_df() used a single g value for the first dichotomous item only and set the guessing parameters of the other items to 0. A single g value is now used for every dichotomous item, as in simdat(), and a g vector of any other wrong length stops with an error.
est_score() with ncore > 1 failed when a chunk of examinees had a single row, for example with 3 examinees and ncore = 2, and it did not close the cluster when a worker failed. Each chunk is now kept as a matrix, also when it has one row, and the cluster is always closed.
est_score() with method = "ML", "WL", "MLF", or "MAP" could stop at a point that was not a solution when the iterations alternated between two values, which happened mostly for low-scoring examinees on tests with 3PLM items. The estimate then depended on max.iter. When the iterations do not converge, the root of the score function is now found within the range of the visited values.
est_score() with method = "INV.TCC" returned wrong standard errors when intpol = FALSE or when the interpolation was skipped with a warning (a range.tcc that is too narrow), because the estimates were paired with the wrong sum scores of the score distribution. The standard deviation is now taken over the sum scores that have an estimate. Results with intpol = TRUE and no warning are unchanged.
est_score() with method = "MLF" gave an examinee with all responses missing an estimate near 0 and a standard error of 316.2 without a warning. The examinee now gets NA and the warning, as with the other methods.
est_score() with method = "EAP" returned NaN, with a warning about missing responses, for tests of about 1,000 items or more, because the product of the item probabilities underflowed. The posterior is now computed from log-likelihoods, and results for usual tests change by less than 1e-14.
llike_score() with method = "MLF" stopped with an error under the default fence.b = NULL. The fences are now placed at -5 and 5, the default range of est_score(). The function also merged examinees whose rows had the same row name, and an examinee without any response, who stopped the function ("ML", "MAP") or got values from the fence items alone ("MLF"), now gets NA.
lwrc() stopped with an error for a single theta when the test had polytomous items.
sx2_fit() counted some summed score groups twice in the tables of polytomous items when the test was so short that the groups merged at the two ends overlapped (for example, two items with five categories). The observed and expected frequencies and the degrees of freedom were too large. Each summed score group is now counted once.
sx2_fit() and irtfit() reported a p-value of 0 (or NaN with a warning) for an item without degrees of freedom left after collapsing, which read as a strong misfit. The critical value and p-value of such an item are now NA, with a warning that names the item.
sx2_fit() named the columns of the observed frequency and proportion tables (obs_freq and obs_prop) with an exp_freq. prefix (exp_freq.score.0, …) when the test had a single polytomous item. The columns are now named score.0, score.1, … as for tests with several polytomous items. The values are unchanged.
plot.irtfit() ignored xlab.text when type = "both" and always used theta as the x-axis title. The given title is now used, as for the other types.
plot.irtfit() drew the standardized residuals as blue crosses, instead of red circles, when every plotted residual exceeded overSR. Each kind of point now keeps its color and shape.
irtfit() stopped with an error for a logical response matrix, and irtfit() and sx2_fit() stopped with an error when the groups of an item were merged into a single row (for example, with a large min.collapse). These cases now work; an item left without degrees of freedom gets NA for the critical value and p-value of the chi-square statistic, with a warning.
sx2_fit() for an est_irt object computed the expected frequencies with a standard normal distribution even when a different latent distribution was used or estimated in the calibration (for example, with EmpHist = TRUE or FIPC). When weights, norm.prior, and nquad are all omitted, the latent distribution stored in the object is now used. The statistics change slightly for a calibration with a standard normal distribution and can change enough to alter the decision at alpha when the distribution was estimated. Set norm.prior = c(0, 1) to reproduce the earlier results.
run_mst() stopped with an obscure error for a panel whose routing module is not module 1 when ini_mod was NULL, because the starting module was drawn with sample() from a single module number. The examinees now start in the routing module.
reval_mst() took the score distribution of the first stage from module 1 even when the routing module had another number, which gave wrong results without an error. The routing module is now taken from route_map, and a first stage with more than one module stops with an error. run_mst() still runs such panels.
cac_lee() and cac_rud() used weights that did not sum to 1 as given, so the marginal indices could exceed 1. Such weights are now rescaled to sum to 1, with a warning. Weights that are missing, infinite, or negative, or that have a non-positive sum, now stop with an error.
plot.find_cut() with theta_range still stretched the x-axis to the crossing lines outside the range. The x-axis now covers the requested range.
ripd() stopped with an obscure error when an examinee had no ability estimate: a missing value in score, an examinee without any response, a focal group examinee left out by min.resp (so min.resp could not be used), or an examinee whose responses were all removed during purification. Such examinees are now excluded, and the function stops when no examinee has an ability estimate. A warning reports the examinees excluded because of score or min.resp, and one warning at the end of the purification reports the examinees excluded during it. min.resp now also applies to the first analysis when score is given, and an examinee with a missing value in score stays out of the purification. The purification stops with a warning and complete = FALSE when min.resp, which counts the items that remain, leaves a group without any ability estimate; score and the number of examinees reported as excluded then belong to the last iteration whose statistics are reported.
ripd() and pcd2() stopped with an obscure error when purification left a single item. They now work. When every item is flagged, the purification stops with a warning and returns all items as flagged with complete = FALSE.
ripd() and pcd2() silently accepted responses outside the score categories (for example, an unrecorded missing code such as -9, which gave chi-square values in the hundreds of thousands), a group of the wrong length (ripd()), and an item.skip that was not a set of item positions (an ID given to pcd2() added a spurious row and skipped nothing). They now stop with a clear message, a logical item.skip is read as item positions, also during purification, and character responses are read as numbers.
pcd2() warned about excluded items even when min.resp excluded none, and worded the warning poorly. It now warns only when items are excluded.
ripd() compared the p-values rounded to four decimals with alpha, so an item with a p-value just above alpha (for example, 0.05004 for alpha = 0.05) was flagged. The decision now uses the unrounded p-values, and the tables still show four decimals.
ripd() computed RIPD_RS from arbitrary values when the covariance matrix of RIPD_R and RIPD_S was singular, which happens when all examinees who responded to an item have the same probability of a correct response (a tiny constant was added to every entry of the matrix). The statistic is now computed with a generalized inverse and the rank of the matrix as degrees of freedom, and a warning names the items. Because the degrees of freedom can then differ between items, the purification removes the flagged item with the smallest p-value of the purify.by statistic, compared on the log scale, instead of the item with the largest statistic. Results for regular data are unchanged.
rdif() and crdif() stopped with an obscure error when an examinee had no ability estimate: a missing value in score, an examinee without any response, an examinee left out by min.resp (so min.resp could not exclude any examinee), or an examinee whose responses were all removed during purification. Such examinees are now excluded, and the functions stop when no examinee has an ability estimate. A warning reports the examinees excluded because of score or min.resp, and one warning at the end of the purification reports the examinees excluded during the purification.
rdif() and crdif() stopped with an obscure error when purification left a single item, and rdif() also stopped with “non-conformable arguments” when purification removed the item with the most score categories from a test with polytomous items. They now work. When every item is flagged, the purification stops with a warning and returns all items as flagged with complete = FALSE. The purification also stops with a warning and complete = FALSE when min.resp, which counts the items that remain, leaves a group without any ability estimate; score and the number of examinees reported as excluded then belong to the last iteration whose statistics are reported.
rdif() and crdif() silently accepted responses outside the score categories (for example, an unrecorded missing code such as -9, which flagged every item), a group of the wrong length, a focal.name that is not in group, an item.skip that was not a set of item positions (a logical item.skip skipped only the first item during purification), an alpha outside (0, 1), a max.iter that was not a positive whole number when purify = TRUE, and a group with more than two values (the values other than focal.name were pooled into the reference group). They now stop with a clear message; use grdif() to compare more than two groups. A logical item.skip is read as item positions, also during purification, and character responses are read as numbers.
rdif() compared the p-values rounded to four decimals with alpha, so an item with a p-value just above alpha (for example, 0.05004 for alpha = 0.05) was flagged, and an item with a p-value below 0.00005 was flagged even for an alpha of 1e-30. The decision now uses the unrounded p-values, and the tables still show four decimals.
rdif() computed RDIF_RS from arbitrary values, including negative chi-square values, when the covariance matrix of RDIF_R and RDIF_S was singular, which happens when all examinees who responded to an item have the same probability of a correct response (a tiny constant was added to every entry of the matrix). The statistic is now computed with a generalized inverse and the rank of the matrix as degrees of freedom, and a warning names the items. Results for regular data are unchanged.
crdif() compared RDIF_R-CR and RDIF_RS-CR of polytomous items with chi-square distributions with K and 2K degrees of freedom, although the category residuals of each examinee sum to zero, so the covariance matrices have ranks K - 1 and 2K - 1. The tests were therefore conservative (about 2% to 3% of the DIF-free items were flagged by RDIF_R-CR and RDIF_RS-CR at alpha = 0.05). The degrees of freedom are now K - 1, K, and 2K - 1, and the statistics are computed without adding a small constant to a singular covariance matrix, which changed them in the fourth decimal. Results for dichotomous items are unchanged.
The purification of crdif() removed the item with the smallest p-value, which was the item listed first when several p-values were too small to be represented (they were 0). The purification of rdif() and crdif() now removes the flagged item with the smallest p-value of the purify.by statistic, compared on the log scale, so the choice also holds for an item of rdif() whose RDIF_RS has fewer degrees of freedom because of a singular covariance matrix. Results for regular data are unchanged.
rdif() and crdif() ignored min.resp in the initial analysis when score was supplied. min.resp is now applied from the initial analysis, with or without score: the scores of the examinees with fewer than min.resp responses are set to missing and these examinees are excluded.
grdif() stopped with the error “object ‘chisq’ not found” when a group had no response to an item. The three GRDIF statistics and their p-values of such an item are now NaN, and the item is not flagged.
grdif() stopped with an obscure error when an examinee had a missing ability estimate, and catsib() left such an examinee out without a message. Both functions now exclude the examinee with a warning, and stop with a message when no examinee has an ability estimate.
grdif() put the examinees whose group is missing into the reference group, while its post-hoc analyses left them out. They are now left out of every analysis, as in the other DIF functions.
The purification of grdif() and catsib() stopped with an obscure error when one item was left, so it could not end when every item was flagged. It now stops with a warning and complete = FALSE when every item is flagged or when a group has no ability estimate left (catsib(): fewer than two within range, which happens when a single item is left and method = "ML"), and the remaining items keep the results of the last counted iteration, as in rdif() and crdif(). The flagged item with the smallest p-value is removed at each iteration, as in rdif() and crdif(); for regular data this is the item removed before. One warning reports the examinees who lost their ability estimates during the purification, the warning for an incomplete purification reads as that of rdif(), and n.iter of the with_purify results is the integer 0 when no item is flagged in the initial analysis.
grdif() and catsib() silently computed wrong statistics for some invalid inputs, such as character responses, responses outside 0 and 1, or a group, score, or se whose length does not match data, and stopped with an obscure message for others, such as a focal.name that is not found in group or method = "EAP.SUM" or "INV.TCC". They now stop with a clear message, also for an alpha outside (0, 1), a max.iter that is not a positive whole number when purify = TRUE, or an invalid min.resp. The est_item method of grdif() stops when score is passed, which it ignored before, and the est_irt and est_item methods stop when data or D is passed. catsib() also stops when min.binsize is not an integer of 2 or more (with 1, the p-values of all items were missing), when n.bin is not the maximum and the minimum number of bins in this order, and when max.del is not from 0 to less than 1.
A logical item.skip of catsib() skipped the wrong items during the purification, so a skipped item could be tested, flagged, and removed. It is now read as item positions, also during the purification.
grdif() and catsib() compared the p-values rounded to four decimals with alpha, so an item whose p-value was within rounding of alpha could be flagged wrongly, and the purification could remove it. The unrounded p-values are now compared; the tables still show four decimals.
grdif() computed GRDIF_R, GRDIF_S, and GRDIF_RS after adding small constants to the covariance matrix of an item when the matrix was singular or nearly singular, for example when all examinees of a group had the same ability estimate, and could stop with an error. The statistics now use a generalized inverse, the degrees of freedom are the rank of the matrix instead of G - 1, G - 1, and 2(G - 1), and one warning names the items. Results for regular data are unchanged.
catsib() returned a statistic of 0, a standard error of 0, and a p-value of NaN for an item when the corrected ability estimates of the two groups did not overlap, and ended the purification as complete. The statistics of such an item are now NA, one warning names the items, and the item is not flagged. The purification stops with a warning and complete = FALSE when this happens to every remaining item.
The estimation of GPCM items with large slopes in est_irt(), est_mg(), and est_item() rescaled all cumulative logits by a common factor once one exceeded 700 in absolute value, which distorted the objective function, and the gradient and the Hessian could become NaN even for logits well below 700. Each row is now shifted by its own maximum when a logit exceeds 100 in absolute value; results below that range are unchanged.
est_irt() and est_mg() computed the standard errors of GRM and GPCM items with D = 1 whatever D was. With D other than 1, such as 1.702, the standard errors of polytomous items were about 1.4 to 2 times too large, those of the dichotomous items of the same test were also affected, and so were covariance and test.2. The estimates were not affected.
With fix.g = TRUE and a g.val other than 0.2, est_irt() and est_mg() computed the standard errors of 3PLM items as if the guessing parameter were 0.2. The error grows with the distance between g.val and 0.2, for example about 9 percent with g.val = 0.25 and about 25 percent with 0.05 or 0.35 for the LSAT6 data. The estimates were not affected.
When D was not 1, the Hessian of 1PLM items with a common estimated slope (fix.a.1pl = FALSE) had a wrong cross term between the slope and the difficulties. In est_item() the standard error of the slope came out too small and the optimization could stop short of the maximum; in est_irt() and est_mg() the M-step followed a different path, which changed the number of EM cycles and the estimates within the convergence tolerance. The Hessian is now correct, and results for D = 1 are unchanged.
covirt() underestimated the standard errors of polytomous items by about 10 to 25 percent, because it computed their information with Equation A8 of Li and Lissitz (2004), which treats the category indicators of a response as independent Bernoulli variables. It now uses the information of the multinomial response, whose standard errors agreed with the empirical standard deviations of the estimates in a simulation; see Details of covirt(). The results for dichotomous items do not change.
covirt() stopped with an error for a GRM or GPCM item with two score categories. Such an item is now treated as a 2PLM item, and a GPCM item listed in pcm.loc as a 1PLM item with a fixed slope.
covirt() printed a message instead of issuing a warning when the information matrix of an item was singular, and the matrix and the vector of standard errors filled with NA had no names. It now issues a warning, and the NA values carry the parameter names.
In a test with many items, such as several hundred, the likelihood of an examinee could underflow to 0 at every quadrature point. est_irt() and est_mg() then stopped with “NA/NaN gradient evaluation”, and pcd2() returned NaN for every item. The likelihood of such an examinee is now rescaled by its largest log-likelihood, and the marginal log-likelihood adds the scale back. Results for tests where no likelihood underflows are unchanged. pcd2() also no longer returns NaN when a quadrature point has no posterior mass.
est_irt(), est_mg(), and est_item() could return the thresholds of a GRM item in a decreasing order, which is not a valid solution because the category probabilities then sum to more than 1 and the reported log-likelihood is inflated. It happened mostly in small samples or when a score category has few responses (for example, in 13 of 40 simulated data sets with five categories and 300 examinees). When the unconstrained estimates are not in increasing order, the item is now estimated again with its thresholds kept in increasing order. Results of items whose thresholds were already ordered are unchanged. Fits with a GRM item whose thresholds were reversed should be rerun.
When the second column of weights did not sum to 1, est_irt() and est_mg() reported a log-likelihood, AIC, and BIC that were off by N times the log of the sum, and a variance in group.par that was multiplied by the sum. With EmpHist = TRUE the empirical histogram was rescaled to that wrong variance, which changed the scale of the item parameters (by up to 1.8 in an example with a sum of 4). The weights are now rescaled to sum to 1 with the warning “The weights do not sum to 1 and were rescaled to sum to 1.”, as in cac_lee() and cac_rud(); weights that are negative, not finite, or sum to zero stop with an error. Weights that sum to 1 give the same results as before.
With EmpHist = TRUE and user weights whose number of quadrature points is not 49 (the default of Quadrature[1]), est_irt() and est_mg() stopped with an error such as “non-conformable arguments” when there were fewer points, and extrapolated the upper tail of the empirical histogram from the wrong point when there were more. The number of points is now taken from weights. The results for 49 points are unchanged.
est_irt() with fipc = TRUE counted a fixed item twice in the first E-step without notice when fix.loc listed it more than once, which changed the estimates of the items to calibrate (by up to 0.45 in an example with fipc.method = "OEM"). It now stops with an error.
With fipc = TRUE, all items fixed, and user weights, the change in the first EM cycle was measured from the mean and variance of the default distribution instead of those of weights, which could shift the cycle at which the iteration stopped. It is now measured from the moments of weights.
npar.est, aic, and bic of est_irt() and est_mg() did not count the weights of the empirical histogram (EmpHist = TRUE) as free parameters, and est_irt() with FIPC counted only the mean and variance of the latent distribution (2 values) even with EmpHist = TRUE. With 49 quadrature points, the AIC of a model with an empirical histogram was 92 too small (2 times the 46 weights), which made it look better than a model with a normal distribution. The count now includes the free weights: Q - 3 for a histogram rescaled to a fixed mean and variance (est_irt() without FIPC, and the reference groups of est_mg()) and Q - 1 for a histogram that is not rescaled (est_irt() with FIPC, and the free groups of est_mg()), where Q is the number of quadrature points. The values for a normal distribution do not change.
est_mg() with x = NULL stopped with an error when cats = NULL, although the help page allows it when all models are dichotomous, and when model included "DRM". Both now work, as in est_irt().
est_mg() counted a group that was listed twice in free.group twice in npar.est, which made aic and bic too large. It silently treated a group name in free.group that is not in group.name as a reference group, and it silently dropped a position in fix.loc that is outside the rows of x, so that a different set of items was fixed. A repeated group is now counted once, and the other two cases stop with an error.
est_mg() ran with a response such as 0.5, which the likelihood and the frequency tables treated differently. Responses must now be whole numbers from 0 to the number of score categories minus 1, as in est_irt().
With missing supplied, the group element of data returned by est_mg() still held the missing-value code, while overall had NA. Both now have NA.
est_item() stopped with an unclear error when score was a data frame, contained a missing value, or had a length different from the number of rows of data, and when only one item had responses. A data frame score now works, and the other cases stop with a clear message or work. The message about items that did not converge numbered the items after the items without responses had been removed; it now uses the positions in the supplied data.
est_mg() with fipc = TRUE always stopped with an error when item.id was supplied: with “do not share common items” when the IDs were those of x, and with “At least one model name is mis-specified” when they were different (present in 1.3.1). A supplied item.id now replaces the IDs in x, as the help page says, and fix.id refers to the new IDs.
FIPC with fixed items whose responses are all the same (for example, all correct) produced an error (“NA/NaN gradient evaluation”) with fipc.method = "MEM", degenerate estimates (a slope of 0.01 and a difficulty above 10) with "OEM", and NaN group parameters after 500 cycles when all items were fixed. Such fixed items carry no information to place the ability scale. est_irt() and est_mg() now stop with a message when the responses to all fixed items show no variation (est_mg() uses the responses pooled over the groups). A failed short search for starting values in the first two EM cycles is now skipped, and when the last search of an item in an M-step fails, the item keeps its previous values and is reported as not converged; both cases stopped the run before.
For est_mg() with FIPC and all items fixed, test.2 read “Solution is a possible local maximum.” although no information matrix is computed. It now reads as in est_irt(): “No item parameters are estimated, so the information matrix is not computed.”
est_item() returned the log-likelihood plus the log prior densities as loglikelihood when prior distributions were used, for example for 3PLM items with the default use.gprior = TRUE, although the help page and the printed “-2loglikelihood” call it the log-likelihood. It now returns the log-likelihood at the final estimates without the log prior densities, as est_irt() and est_mg() do. The estimates, standard errors, and covariances are unchanged, and so is loglikelihood when no prior is used.
bring.mirt() returned NA parameters, or silently wrong ones, for some mixed-format fits (present in 1.3.1). The columns of the coefficient matrix of mirt follow the model of the first item, so a fit that began with a polytomous item gave NA for its dichotomous items, and a fit that used the graded or gpcm model for every item, including two-category items, gave wrong values (the category probabilities differed by up to 0.5). The parameters of each item are now read by the column names. The intercept c of the gpcmIRT model, which was dropped, is moved into the thresholds (b - c / a); it changes the thresholds only when c is not 0. A dichotomous item of an unsupported model in a mixed-format fit, such as a 4PL item, now stops with an error instead of losing its upper asymptote. A model fitted to multiple groups, which is not supported, now stops with a message that asks for one extracted group (mirt::extract.group()).
write.flexmirt() wrapped the lines of the file at the width of the console (80 characters in Rscript), which broke the file: bring.flexmirt() could not read it, and flexMIRT would not either. This happened for the lines of items with several categories (GRM and GPCM), so the files written by the examples of the help page were affected. Each item and each group is now written as one line, and the file does not depend on the console width.
write.flexmirt() wrote a wrong first step parameter for GPCM items with a number of categories other than five (the difference was up to 0.69 for the four-category items of simCAT_MX), because the mean of the step parameters was computed with a constant 4 instead of the number of steps. With rePar = TRUE, it also left out the last scoring function value of GPCM items. Files written for GPCM items with five categories are unchanged. A GPCM item with two categories and rePar = FALSE stopped with a subscript error; it is now written as a two-parameter item. The file is also closed when the function stops with an error.
est_irt() stopped with an out-of-range error for response columns stored as character, such as "0" and "1", and est_mg() stopped with an error when only some of its groups had character columns (present in 1.3.1). Character and factor columns are now read by their values in est_irt() and est_mg(), as in est_item(). A non-numeric missing code, such as missing = ".", is recoded before the columns are read in est_irt(), as in est_mg().

Changes in Behavior

catsib() has a new argument rho.screen, TRUE by default. When the ability estimates of a few examinees have very large standard errors, the estimated reliability (rho^2) of the regression correction can be far below the reliability of the other estimates, or negative, which shrinks the corrected abilities too much and inflates the Type I error rate. With rho.screen = TRUE, the examinees whose squared standard error is equal to or greater than the variance of the ability estimates of their group are left out of the computation of the group mean, the variance, and the error variance used in the regression correction; they are still matched and tested. This changes the default results of catsib(). The warning about rho^2 falling below 0.05 is given less often, and once per call instead of at every purification iteration. rho.screen = FALSE computes rho^2 as earlier versions did.
The post-hoc pairwise analyses of grdif() take the reference group as the reference group of every pair that includes it, and, for a pair of focal groups, the group listed earlier in focal.name. Before, the group that came later in the sorted order of the group values was the focal group of a pair. When the two orders differ, group.pair is labeled in the new order, the signs of rdifr, z.rdifr, rdifs, and z.rdifs are reversed, and n.ref and n.foc are swapped. rdifrs, the p-values, and the flags do not change.
The default of range in grdif() is c(-5, 5), as in rdif(), crdif(), ripd(), catsib(), and est_score(), instead of c(-4, 4). The ability estimates of the examinees whose ML estimates reach a limit of the range (for example, all responses correct or all wrong) change, and so do the results that depend on them.
grdif() supports dichotomous items only and stops with an error when x includes a polytomous item or any item of the “GRM” or “GPCM” model, even one with two score categories, because the GRDIF framework has been evaluated for dichotomous items (Lim et al., 2024). Before, polytomous items were analyzed as net DIF, and the default post-hoc analyses of mixed-format data could stop with an error.
grdif() stops with an error when the examinees whose group is not in focal.name belong to more than one group, because they form the reference group. Before, the GRDIF statistics combined them into one reference group while the post-hoc analyses compared them separately.
grdif() and catsib() apply min.resp from the initial analysis also when score is supplied: the scores of the examinees with fewer than min.resp responses are set to missing and these examinees are excluded, and a warning reports their number.
est_irt() stops with a clear message that names the items when a response is not a whole number from 0 to the number of score categories minus 1. Before, it stopped with “subscript out of bounds”. With fipc = TRUE it also stops when x and data have different numbers of items, when fix.loc is empty or points outside the rows of x, and when an ID in fix.id is not found in the item IDs. Before, an unmatched ID in fix.id was dropped without notice.
A supplied item.id replaces the IDs in x in est_irt() also when fipc = FALSE, as it already did with fipc = TRUE. Before, it was ignored when x was supplied. It must have one value per item. The same holds for est_mg() (one vector of IDs per group, also without FIPC) and est_item().
For est_irt() with FIPC and all items fixed, test.2 reads “No item parameters are estimated, so the information matrix is not computed.”, instead of the message about a possible local maximum.
est_irt(), est_mg(), and est_item() issue one warning, with the item IDs, when an item to be estimated has a score category without responses or all its responses are the same. The maximum likelihood estimate does not exist for such an item, and the functions returned extreme or starting values with standard errors of 99999 without notice. The estimates are not changed. est_mg() judges an item on its responses pooled over the groups, and est_item() judges the items that have responses.
bring.mirt() labels a two-category item fitted with the graded, gpcm, or gpcmIRT model as "DRM", with a guessing parameter of 0, because a two-category item is a dichotomous item in irtQ. Such an item was labeled "GRM" or "GPCM", with wrong or missing parameters (see Bug Fixes).
The type argument of bring.flexmirt(), bring.bilog(), and bring.parscale() is "par" when omitted, as the usage line suggests, and its case is still ignored. Omitting it stopped with “the condition has length > 1” before. An unknown type stops with the available choices.
bring.parscale() stops with an error for the model codes 2, 4, 6, and 8 of PARSCALE when the file has polytomous items. The thresholds of such items were all NA.

Minor Improvements

est_mg() checks its inputs and stops with a clear message when group.name does not have one unique name per group, when x, model, cats, or item.id does not match the groups or the columns of data, when an item ID is repeated within a group, when items with the same ID have different models or numbers of score categories in different groups, when free.group names or numbers a group that does not exist or leaves no reference group without FIPC, when fix.loc is not a list with one element per group or has repeated positions, when an ID in fix.id is not found, and when fipc.method is not "MEM" or "OEM" (also in est_irt()).
est_mg() with FIPC warns when a fixed item has different parameter values in different groups. The values of the first group that includes the item are used, as before. Differences of at most 1e-10 are ignored.
run_flexmirt() stops with a clear message on systems other than Windows, where flexMIRT does not run, and when “C:/Program Files” holds more than one flexMIRT folder (give dir.flex then). write.flexmirt() stops with a clear message when file is NULL.
getirt() can extract the matched call of est_irt(), est_mg(), and est_item() objects with what = "call".
ctt() records the correct setting in its crit element, and the summary() report names the item-total correlation used for flagging.
ctt() and ctt_distr() stop with a clear message when crit.p, crit.dis, crit.distractor, or total is not valid, when fewer than two examinees remain, or when ctt_distr() receives numeric and non-numeric keys together.
Inputs that were silently misread now stop with a clear message. Functions that take item metadata reject parameter columns stored as text, a missing or too small cats, a dichotomous model with cats other than 2, and a polytomous item with more thresholds than cats - 1, including any value in par.3 of a two-category GRM or GPCM item. shape_df() and simdat() reject parameter vectors that do not match the items, shape_df_fipc() checks fix.loc, gen.weight() checks dist and theta, and prm() rejects an unknown pr.model.
prm() uses the GRM when pr.model is omitted, simdat() repeats a single pr.model for all polytomous items, shape_df() accepts par.drm without g, and item metadata without a par.3 column is accepted when every item is 1PLM or 2PLM.
plot.info() and plot.traceline() show item IDs as given ("item-2" was shown as item.2), and the panel titles of plot.traceline() read Score: 0 instead of Score: resp.0.
The warning for "DRM" items reads “All ‘DRM’ items are treated as ‘3PLM’ items.” and no longer mentions item parameter estimation, because it also appears in functions such as info() and simdat(). est_mg() gives it once, not once for each group.
est_score() stops with a clear message when data has a number of columns other than the number of items (it scored the wrong items before), when a response is not an integer within the categories of its item, and when method, stval.opt, tol, or max.iter is not valid (a max.iter below 1 or not a whole number removed the iteration limit). For an est_irt object, supplying data or D is now an error instead of being ignored.
est_score() now has a method for est_item objects, as its documentation already stated.
irtfit() and sx2_fit() stop with a clear message when data has a number of columns other than the number of items or a response is not an integer within the categories of its item (such a response was counted in the wrong cells before), and they read character and factor responses as numbers instead of level codes. irtfit() also stops for a score of the wrong length, scores without variation, an invalid range.score, and a loc.theta other than "average" or "middle" (ignoring case; any other value was treated as "average"), leaves out examinees with a missing ability estimate instead of failing, and works when only one item remains after items with fewer than two responses are excluded. plot.irtfit() stops for a type other than "both", "icc", or "sr" and for an item.loc that is not an evaluated item. sx2_fit() stops for a test with a single item, which has no S-X2 statistic. The warnings for excluded items and for missing responses in sx2_fit() name the items, and the error for a response outside the categories names the item and its column.
bisection() warns when the function values at lb and ub have the same sign, instead of returning a bound without notice, and it stops after exactly max.it iterations instead of max.it + 1. Its help page names the last returned element delta, as in the code.
est_score() with method = "INV.TCC" stops with a clear message when the test characteristic curve does not reach a sum score within theta from -20 to 20, for example for items with very small slopes or guessing parameters that sum to nearly the maximum score.
ctt() computes alpha with each item removed faster for large tests.
cac_lee() and cac_rud() leave out examinees with a missing ability estimate (or standard error) with a warning. Before, cac_lee() and cac_rud() stopped with an obscure error for a missing ability estimate, and cac_rud() returned wrong indices for a missing standard error. Both stop for cut scores that are not in strictly ascending order, and cac_rud() stops for a standard error that is not positive. cac_lee() no longer stops when a performance level contains no integer summed score, and it is much faster with ability estimates.
panel_info() stops with a clear message for a route map that does not define a panel (a module without transitions, a cycle, or a module outside the stages) instead of running forever, and it finds every stage when the modules are not numbered in the order of the stages. panel_info(), run_mst(), reval_mst(), and find_cut() read a logical route map as a 0/1 matrix.
reval_mst() accepts item metadata with a missing guessing parameter, such as 2PLM items from shape_df(). It checks and completes the item metadata as run_mst() does, so item.by.mod and item.by.path hold plain data frames with upper-case model names and without parameter columns that are all NA. reval_mst() and run_mst() stop with a clear message when cut_score does not match the panel or module has a number of columns other than the number of modules, they read a NULL element of cut_score for a stage with a single module as an empty vector, and they assign the cut scores to the modules of a stage in the order of the module indices. reval_mst() also stops with a clear message when a sum score has no inverse TCC estimate (with intpol = FALSE or a range.tcc that does not cover the estimates); before, it stopped with an obscure error or returned NA for every row of eval.tb.
run_mst() stops with a clear message when tol or max.iter in route_score or final_score is not valid (a max.iter of 0 or 2.5 never ended), and it warns once about examinees without any observed response to the administered items.
find_cut() no longer warns about two proper crossings when a crossing lies exactly on a grid point.
ripd() results have a print() method that prints the flagged items and the RIPD statistics of the analyses without and with purification; what selects "no_purify" or "with_purify". Before, print() listed the whole object, including the ability estimates of every examinee.
The est_irt and est_item methods of rdif() and crdif() stop with a clear message when data or D is passed, because both are taken from the object, and the est_item methods do so for score as well. Before, they were silently ignored or caused an obscure error.
The purification of rdif(), crdif(), ripd(), grdif(), and catsib() is faster: after the first iteration, only the examinees who responded to the removed item are rescored when ncore = 1 and method is a pattern scoring method. Results are unchanged.
The purification of rdif(), crdif(), ripd(), grdif(), and catsib() gives the warning of est_score() about ncore > 1 once instead of at every iteration.
rdif(), crdif(), and ripd() stop with a clear message for method = "EAP.SUM" or "INV.TCC" when they estimate the abilities (score = NULL or purify = TRUE), because these methods give no ability estimate for each response pattern. Before, rdif() and crdif() stopped with an obscure message. ripd() and pcd2() also stop with a clear message when min.resp is not a single nonnegative number, and ripd() stops when score is not numeric, when alpha is not a single number between 0 and 1 (an alpha of 1 or more, or a text value, flagged every item), or when max.iter is not a positive whole number and purify = TRUE. The est_irt and est_item methods of ripd() stop with a clear message when data or D is passed, and the est_item method when score is passed, as those of rdif() and crdif() do.
pcd2() stops with a clear message when max.iter is not a positive whole number and purify = TRUE, and when crit.val is not NULL or a single nonnegative number. Before, it accepted max.iter = 2.5, and a text crit.val flagged no item without a message.
The print() methods of grdif() and catsib() leave the significance symbol of an item with a missing p-value blank, as those of rdif() and crdif() do for an item skipped with item.skip. Before, it showed ?, which the legend does not explain.
The print() methods of rdif() and crdif() leave the significance symbol of an item skipped with item.skip blank. Before, it showed ?, which the legend does not explain.
The dif_item elements rdifr and rdifs of the no_purify results of rdif() are integer vectors like rdifrs, and n.iter of the with_purify results of rdif() and crdif() is the integer 0 when no item is flagged in the initial analysis. npar.est of est_mg() and n.iter of the with_purify results of catsib() are integers as well. The values are unchanged.
When the Hessian of an item is singular during the estimation of est_irt(), est_mg(), and est_item(), it is now repaired by adding a ridge to its diagonal instead of random numbers. The results are deterministic, and the random number stream of the user is no longer used by the estimation.
The help pages of est_irt(), est_mg(), est_item(), and getirt() now describe how the standard errors are computed: the information of the item parameter priors is added to the cross-product information matrix of est_irt() and est_mg(), and est_item() uses the observed information with the abilities treated as known. The help pages of est_irt(), est_mg(), and getirt() also state that test.2 checks whether the information matrix is singular, and those of est_irt(), est_mg(), and est_item() state that the slope prior aprior is placed on D times the slope.
est_irt() and est_mg() return a new last element, npar.detail, an integer vector with the numbers of free item parameters (item), of means and variances of the latent distributions (dist), and of weights of the empirical histograms (weight); their sum is npar.est. The summary() output shows these three numbers under “Number of free parameters”, and getirt() extracts it with what = "npar.detail". Objects created by earlier versions have no npar.detail, and their summary shows the total only.
Corrected and extended the help page of est_irt(). The returned loglikelihood is the sum over examinees of the marginal log-likelihood of the response pattern, without the prior densities (it is not a sum over items); ncase and posterior.dist refer to examinees, not response patterns; data is a matrix; and the returned call is listed. The help page also describes the response coding (0 to the number of categories minus 1), the role of x, item.id, weights, group.mean, and group.var with and without FIPC, the EM algorithm, the empirical histogram, the two FIPC methods (including that the returned distribution of fipc.method = "OEM" is updated once afterward and that the standard errors in group.par for FIPC are approximate), the counting of the free parameters, the bounds of the slopes, and the items without a maximum likelihood estimate. Etol and MaxE are described as the criterion and the maximum number of EM cycles, and the argument tables, the lists in the argument descriptions, and the examples were corrected. The same corrections apply to getirt() and to the arguments shared with other help pages.
Corrected and extended the help pages of est_mg(), est_item(), and getirt() in the same way. The help page of est_mg() now explains free.group, the roles of weights, group.mean, group.var, and EmpHist for groups in and not in free.group, the MG-FIPC algorithm (Kim & Kolen, 2016), and the structure of the returned loglikelihood, group.par, posterior.dist, data, ncase, and nitem. The help page of est_item() states that the estimates are Bayes modal when priors are used, that the items are estimated separately, and what group.par, data, deleted.item, and n.response contain. The README and the reference of Kim and Kolen (2016) were corrected.
The help pages of est_irt() and est_mg() state that the EM cycles stop on the largest absolute change in the estimated item parameters (with all items fixed in FIPC, in the means and variances of the latent distributions). The estimated means and variances of the latent distributions and the weights of empirical histograms are not part of this criterion, so they can be less converged than the item parameters, and a smaller Etol gives more precise values; the default Etol of est_mg() (1e-3) is larger than that of est_irt() (1e-4). The help pages also state that, with EmpHist = TRUE, group.par reports the fixed mean and variance of a rescaled histogram, which can differ slightly from the mean and variance of the returned weights, and that the standard errors of the item parameters treat the latent distributions as known.
Corrected the help pages of bring.flexmirt() and the other import functions (the returned elements, the arguments type, rePar, and rePar.gpc, the model labels, and the item types of mirt that are supported), write.flexmirt() (norm.pop holds the mean and the variance, metadata from irtQ needs rePar = FALSE, and the scaling constant), run_flexmirt() (Windows only), summary() (the returned object), sx2_fit() (missing responses are replaced with 0), and the data sets LSAT6, simCAT_DC, simCAT_MX, simIPD, simMG, and simMST. The package overview gives D = 1 as the default and lists the functions for item parameter drift, MST panels, and the import and export of files. The help pages of rdif(), crdif(), grdif(), catsib(), est_irt(), est_mg(), and est_item() state that the digits argument of their print methods is not used.
The print methods are now documented on the help pages of the functions that create the objects (rdif(), crdif(), grdif(), catsib(), irtfit(), run_mst(), and find_cut()), and those of the est_irt(), est_mg(), and est_item() objects and their summaries on the help page of summary(), including the use of their digits argument.

irtQ 1.3.1

CRAN release: 2026-10-08

Bug Fixes

Fixed the routing in run_mst(). The ability estimate used to choose the next module was computed from the responses to the current module only. It is now computed from the responses to all modules administered so far, as intended. This applies to every routing method ("bmat", "mfi", and cut scores) and every route_score method, so simulated paths and results can differ from earlier versions, in particular from stage 3 onward.
Fixed cut-score routing in run_mst() for route maps in which a module reaches only some modules of the next stage. The examinee could be sent to a module that did not match the cut scores. Only the cut scores that separate the reachable modules are now used, which is the rule applied by reval_mst(). Results are unchanged when every module of a stage can be reached.
Fixed the final estimate of run_mst() for responses with missing values (response argument). The ML, WL, MLF, MAP, and EAP estimates used the parameters of other items instead of those of the observed items.
score_resp() and ctt_distr() now match a data frame key to the item columns by item number when its item column is character or factor. Before, keys for tests with 10 or more items could be assigned to the wrong items.
shape_df() with default.par = TRUE now repeats a single value of cats or model for all items. Before, a single cats value gave item IDs of “V1” for every item and a guessing parameter of 0 instead of 0.2 for 3PLM items.
irtfit() now computes the observed category proportions directly from the frequencies. Before, they came from janitor::adorn_percentages(), which can include the total column in the denominator in some environments and halve the proportions, the residuals, and overSR.prop. Results are unchanged when the proportions were computed correctly.
catsib() no longer overwrites score with se when se is given as a matrix or data frame.
cac_rud() no longer stops with a dimnames error when a performance level has no examinees, and the label of the total row of marginal is now “marginal”, as in cac_lee().
simdat() treats NA values in g.drm as zeros, as the item metadata input does. Before, the responses to those items were all NA.
The Wald confidence intervals in plot.irtfit() now use the two-sided critical value, qnorm(1 - alpha / 2). Before, they used qnorm(1 - alpha) and were 90% intervals at the default alpha = 0.05, while the Wilson intervals were 95% intervals.

Minor Improvements

reval_mst() now stops with an informative message when the modules in a stage differ in maximum sum score.
In run_mst() and reval_mst(), an ability estimate equal to a cut score is now routed to the higher module, as in the classification rule of cac_lee() and cac_rud(). Results change only when an estimate equals a cut score exactly.
ctt(), ctt_distr() (scored-category mode), and the CTT helper functions now stop with an informative error when an item score is not a whole number between 0 and cats - 1. When cats is inferred, every item has at least two categories, so an item that every examinee scores 0 on gets a difficulty of 0 and is flagged.
ctt() reports the listwise deletion of incomplete rows once, and the item table has default row names.
plot.find_cut() no longer passes an unused inherit.aes argument to geom_vline(), which caused warnings with some ggplot2 versions.
catsib() now stops with an informative message when score is supplied without se. Before, it failed with a “missing value where TRUE/FALSE needed” error.

Documentation

Stated in ?reval_mst that the recursion is based on inverse TCC estimates, that only inverse TCC scoring with cut-score routing is supported, and that modules in a stage must have the same maximum sum score; corrected the documented default of theta and the description of the returned list.
In ?run_mst, described the cumulative routing estimate and the relation to reval_mst(), and stated that "EAP.SUM" and "INV.TCC" count a missing response as 0 in the final sum score.
Corrected the interpretation of alpha with the item removed in ?ctt. The ctt() examples and the CTT article now use simulated IRT data.
Corrected the description of the expected frequencies in pcd2() and stated that crit.val = NULL flags no items.
Reworded the motivation of ripd().
In ?simMST, noted that the item parameters are on the D = 1.702 scale. In ?find_cut, clarified that the cut scores in simMST were obtained with find_cut() and softened the statement about path reversals.
Corrected the probability matrix of the first example in ?lwrc, the GPCM formula note in ?irtQ, and the class of prob.cats in ?traceline. Corrected statements in the README, the vignette overview, and the articles (shrinkage of MAP and EAP, range.score in irtfit(), fix.id in est_mg(), the effect of EmpHist in FIPC, the fixed-slope 1PLM, fixed guessing, the CATSIB regression correction, the purification procedure, and the usage notes of the utility functions). The DIF article now simulates item difficulties in a narrower range so that the pooled calibrations converge.

irtQ 1.3.0

CRAN release: 2026-10-05

New Features

New classical test theory (CTT) functions: ctt() for item- and test-level statistics, with print() and summary() methods; freq_score() for a total-score frequency table; ctt_distr() for option or score-category distributions with point-biserial correlations; and score_resp() for scoring raw selected-response data with an answer key. Item-total and option-total correlations are reported both with and without the item.
Added a vignette, “Introduction to irtQ” (vignette("irtQ")), that walks through calibration, scoring, and model-fit evaluation.

Bug Fixes

Fixed the EM convergence check in est_irt() and est_mg(). It used the largest signed change instead of the largest absolute change, so the EM could stop before the Etol criterion was met, most often when some parameters were held constant (for example, the guessing parameter of 1PLM and 2PLM items, or slopes fixed by fix.a.1pl or fix.a.gpcm). Refitting a model can change the estimates and take more EM cycles. A fit from an earlier version that reported maxpar.diff equal to 0 most likely stopped early.
est_irt() and est_mg() no longer warn that the convergence criteria are not satisfied when fipc.method = "OEM" or when the EM converges at the MaxE-th cycle. In these cases, test.2 can now report a possible local maximum.
est_item() now uses Beta(5, 16) as the default prior for the guessing parameter, as in est_irt() and est_mg().
getirt() now returns posterior.dist and scale.D for objects from est_irt() and est_mg(), as documented.

Minor Improvements

find_cut() now uses D = 1 by default, like the other functions in the package (previously 1.702). Supply D = 1.702 to reproduce earlier results.
shape_df_fipc() now accepts a single value of cats or model for all new items.
The mirt package moved from Imports to Suggests. Only bring.mirt() uses it, and it now stops with an informative message when mirt is not installed.

Documentation

Added examples to print.ctt(), summary.ctt(), print.summary.ctt(), and the data sets.
Corrected the descriptions of Etol, test.1, test.2, and maxpar.diff, the return class of est_mg(), dif_item in the purified results of rdif(), crdif(), and grdif(), and the component names of simCAT_DC and simCAT_MX.
Corrected bibliographic details, added DOIs, cited the polytomous RDIF extensions in rdif() and crdif(), and listed every reference cited in the help pages on the package help page (?irtQ).

irtQ 1.2.0

CRAN release: 2026-07-27

New Features

Added a new function, find_cut(), which identifies TIF-crossing routing cut scores for MST panels. For each adjacent module pair within a stage, the function locates the theta where the two modules’ test information functions (TIFs) intersect. A warning is issued when the mean difficulty order of modules within a stage differs from their input index order.
Added a new S3 method, plot.find_cut(), which visualizes TIF curves and routing cut scores stage by stage using ggplot2 facets. Proper, anomalous, and unselected cut scores are distinguished by line type. A layout argument ("vertical" / "horizontal") controls the facet orientation.
Added a new function, run_mst(), which simulates MST administrations for a given panel structure and returns response data along with ability and routing information for each simulated examinee.
Added a new exported dataset, simIPD, which contains simulated CAT response data for illustrating IPD detection with ripd() and pcd2(). The dataset represents one replication of a CAT simulation (N = 3,000; test length = 30; 360-item 3PLM pool) in which 5% of items (18 items) had both $a$ and $b$ parameters decreased by 0.5.

Minor Improvements

est_irt(), est_item(), and est_mg() now accept a partial control list. Users can specify only the arguments they wish to override (e.g., control = list(iter.max = 500)); unspecified arguments fall back to their defaults via modifyList().
Reorganized the pkgdown reference page: ripd() and pcd2() are now grouped under a new Item Parameter Drift (IPD) section, and reval_mst(), panel_info(), find_cut(), plot.find_cut(), and run_mst() are grouped under a new Multistage-Adaptive Test (MST) section.

Documentation

Expanded the documentation for ripd() by adding a @details section that covers the theoretical background of the RIPD framework, asymptotic distributions of the three RIPD statistics, a drift-type diagnostic guide, the CAT-specific three-step workflow, and the purification procedure. Also added a \donttest{} example demonstrating a complete CAT-based IPD detection workflow using the simIPD dataset. Updated @references to include Lim & Choe (2023) and replaced the previous conference paper citation with the journal reference (Lim & Han, 2026).
Added a \donttest{} example to pcd2() demonstrating CAT-based IPD detection using the simIPD dataset, including the bootstrap critical value procedure described in Lim & Han (2026).
Updated the MST Panel Evaluation and Simulation article (vignettes/articles/mst-panel-evaluation.Rmd) to introduce run_mst() and extend the existing reval_mst() content with routing and scoring examples, a find_cut()-based principled cut score derivation, a side-by-side routing method comparison, and a Monte Carlo-vs-analytical validation example (Example 6).

Bug Fixes

Rebuilt the simMST dataset: the previous version had 9 items duplicated across non-adjacent modules because the original assembly only enforced no-overlap within a single routing pathway. The new version enforces a global no-overlap constraint across all 7 modules (56 unique items total) and adds a mean(b) per-module band constraint; cut scores were regenerated via find_cut() on the rebuilt modules.

irtQ 1.1.0

CRAN release: 2026-06-07

Major Improvements

Improved the speed and reduced memory usage of item parameter estimation and standard error computation in est_irt(), est_item(), and est_mg().
Improved the computational speed of est_score() by up to 52% for dichotomous items (N = 10,000) and up to 27% for mixed-format tests, through a series of optimizations.
Improved the computational speed of sx2_fit() substantially by replacing the O(J^2) Lord-Wingersky recursion with a forward-backward pass (up to 11x faster for mixed-format tests with J = 55 items) and vectorizing internal helper functions expFreq(), obsFreq(), and the PRM category-collapsing routine.

New Features

Added a unit test suite using the testthat 3rd edition (testthat >= 3.0.0). Tests cover core functions including drm(), prm(), est_irt(), est_score(), est_mg(), rdif(), crdif(), and catsib(), with the relevant tests across dichotomous, polytomous, and mixed-format item scenarios (355 tests total).
Added a new function, ripd(), which implements the Residual-based Item Parameter Drift (RIPD) detection framework. The function computes three RIPD statistics: $RIPD_R$ , $RIPD_S$ , and $RIPD_{RS}$ (one for each item). $RIPD_R$ captures uniform item parameter drift (IPD) via differences in mean raw residuals between groups, $RIPD_S$ captures nonuniform IPD via differences in mean squared residuals, and $RIPD_{RS}$ is a combined chi-square-based statistic sensitive to both types of drift. An optional purification procedure is also supported.

New Articles

Launched the irtQ documentation website at https://hwangQ.github.io/irtQ/, built with pkgdown. The site includes a full function reference index and the following vignettes covering the complete irtQ workflow:
- Getting Started with irtQ: an end-to-end overview of the package workflow.
- Item Parameter Estimation: detailed guidance on est_irt(), est_item(), and est_mg().
- Ability Estimation: scoring methods available in est_score().
- Model-Data Fit Evaluation: using irtfit() and sx2_fit() to assess model fit.
- DIF Detection: applying rdif(), grdif(), and catsib() to detect item bias.
- Classification Accuracy and Consistency: computing indices via cac_lee() and cac_rud().
- Utility Functions: usage of info(), traceline(), lwrc(), simdat(), and related helpers.
- Evaluating MST Panels with reval_mst(): measurement precision and bias evaluation for multistage adaptive tests.

Bug Fixes

Fixed a minor bug in sx2_fit() that caused incorrect cell collapsing between two adjacent score categories for polytomous items when computing the S- $X^2$ item fit statistic.
Fixed minor bugs in est_score() and info().
Resolved an issue in catsib() where the function failed when all responses were missing (NA) in either the reference or focal group.
Updated cac_rud() to include the x argument, allowing users to pass item metadata data frames directly.
Revised default control parameters in est_irt(), est_item(), and est_mg(), and updated the documentation accordingly.
Fixed a minor bug in est_score() function in terms of Newton-Raphson method.
Fixed multiple stability issues in catsib():
- The final bin exclusion step in catsib_item() used a hardcoded threshold of 3 instead of the user-supplied min.binsize argument, causing inconsistent bin filtering behavior.
- The reliability estimate rho2 in catsib_one() was not clamped to $[0, 1]$ , so when errvar > sigma2 (e.g., very few items or purification cascade), a negative rho2 reversed the regression correction direction, inflating the Type I error rate.
- When errvar >= sigma2 during purification, rho2 collapsed to 0, causing all corrected scores to converge to the group mean. With group mean differences (impact), this produced empty bin data frames and an invalid purification result. A minimum floor of 0.05 is now enforced for rho2 to preserve score spread.
Fixed a critical bug in covirt() where the guessing parameter (par[,3]) was incorrectly passed as the difficulty parameter (par[,2]) to the integrand() function for DRM items. This caused the gradient computation to receive c = b, which zeroed out the $\partial P/\partial a$ and $\partial P/\partial b$ gradient components and produced a singular Fisher information matrix. Additionally, NA values in par.3 for 1PLM and 2PLM items are now substituted with 0 prior to gradient evaluation to prevent NA propagation.

irtQ 1.0.0

CRAN release: 2025-07-17

The documentation for the irtQ package has been revised to reflect updates to function behavior, fix typos, and provide more relevant and detailed information for existing functions.
A new function, crdif(), has been added. This function computes three statistics from the residual-based DIF detection framework using categorical residuals (RDIF-CR). It allows for the detection of global DIF, particularly in polytomously scored items.
A new function, shape_df_fipc(), has been introduced. This function merges fixed-item metadata with automatically generated metadata for new items and produces a single data frame ordered by test position. It is designed to support fixed item parameter calibration (FIPC) via the est_irt() function.
The plot() method has been enhanced to support the display of all item characteristic curves for a given item in a single panel.
The pcd2() function has been updated to include a purification procedure.
The rdif() and catsib() functions now include an item.skip argument. This allows users to specify a numeric vector of item indices to exclude from the DIF analysis.

irtQ 0.2.1

CRAN release: 2024-08-25

Enhanced functionality of the bind.fill() function by adding a new argument fill. The value in the argument is used to fill in missing data when aligning datasets.
Fixed a bug within the est_irt() function that was previously unable to implement the fixed item parameter calibration (FIPC) when only freely estimating a single item given that all other items are fixed.
Added a new function, reval_mst(), which evaluates the measurement precision and bias in Multistage-Adaptive Test (MST) panels using a recursion-based evaluation method introduced by Lim et al. (2020).
Added a new function, pcd2(), which computes the Pseudo-count $D^{2}$ statistics (Cappaert et al., 2018; Stone, 2000) to detect item parameter drift.

irtQ 0.2.0

CRAN release: 2023-07-06

Introduced Warm’s (1989) Weighted Likelihood (WL) estimation method to the est_score() function. This WL scoring method can now be utilized by setting method = "WL".
Enhanced the speed of ability parameter estimation in the est_score() function when using the ML, MLF, or MAP methods for the method argument. The updated version performs approximately three times faster than its predecessor.
Addressed a bug within the est_score() function that was previously unable to accurately compute scores when only a single item data was provided. This issue was occurring with the EAP.SUM and INV.TCC estimation methods.
Added two new functions for computing classification accuracy and consistency: cac_rud() and cac_lee().
- cac_rud: This function implements Rudner’s (2001, 2005) method for computing classification accuracy and consistency. It takes cut scores, ability estimates, standard errors, and optional weights as inputs and returns a list containing a confusion matrix, marginal and conditional classification accuracy and consistency indices, the probability of being assigned to each level category, and the cut scores used in the analysis.
- cac_lee: This function implements Lee’s (2010) method for computing classification accuracy and consistency. It takes a data frame containing item metadata, cut scores, optional ability estimates, optional weights, a scaling factor, and a logical value indicating the cut score metric as inputs. It returns a list similar to cac_rud.
Added a new function, llike_score(), which computes the loglikelihood of ability parameters given the item parameters and response data.
Enhanced functionality of the rdif() and grdif() functions: Both now support the graded response model (GRM) and generalized partial credit model (GPCM).
Fixed an issue in the grdif() function that inaccurately calculated the GRDIF statistics when group membership was specified in a non-standard way. Specifically, the problem arose when 0 wasn’t used as the reference group and consecutive numbers (e.g., 1, 2, 3) weren’t used to represent focal groups in the group argument.

irtQ 0.1.1

CRAN release: 2023-05-08

Resolved the misalignment issue of standard errors in the output of the est_irt() function when fix.a.1pl = TRUE is specified and the items are calibrated using the 1PLM.
Added a new function, grdif(), to perform differential item functioning (DIF) analysis across multiple groups. This function calculates three generalized IRT residual DIF (GRDIF) statistics. For more information about the function and its usage, please refer to the accompanying documentation.
Fixed several typos in the manual documentation

irtQ 0.1.0

CRAN release: 2023-01-10

Initial release on CRAN
The irtQ package is a successor of the irtplay package which was retracted from R CRAN due to the intellectual property (IP) violation. All issues of the IP violation have been clearly resolved in the irtQ package.
Most of the functions the irtQ package are identical in appearance and functionality to those of irtplay package except a few functions (e.g., shape_df(), est_score()). However, the computing speed of several functions (e.g., est_irt(), est_score(), lwrc()) in the irtQ package are faster than the previous ones in the irtplay package. Read the documentation carefully prior to using the functions.