o Included a new function of ‘post_den’ to compute updated prior (a.k.a. posterior) densities of the latent ability distribution given a prior ability distribution, item parameters, and item response data.

o Updated ‘est_score’ function so that the standard errors of ability estimates can be computed using eigther the observed item information function or the expected item information (a.k.a. Fisher information) function when MLE, MLE with fence (MLEF), or MAP scoring method is used.

o Updated ‘est_irt’ function to compute the loglikelihood-based fit statistics of Akaike information criterion (AIC) and Bayesian information criterion (BIC).

o Updated ‘est_irt’ function to tally the number of freely estimated parameters taking the mean and variance parameters of the latent ability distribution into consideration when ‘fipc = TRUE’.

o Updated ‘est_irt’ function to suppress printing the observed data log-likelihood after each EM cycle using the argument of ‘verbose’.

o Fixed an error of the ‘est_irt’ function when only dichotomous items are used with ‘fipc = TRUE’. In that condition, an error message of “subscript out of bounds” was returned in the previous version. No error message is shown in the updated version. (thanks to Ahmet GUVEN)

o Fixed the ‘lwrc’ function so that it can return the probability results even when only a single theta value is used.

The package has been updated significantly in this verstion. In this version, I have:

o Updated ‘est_score’ function to estimate ability parameters much faster than the previous version of the function.

o Updated ‘est_irt’ and ‘est_item’ functions to estimate item parameters much faster than the previous version of the functions.

o Updated ‘test.info’ function to compute items infomation and test information much faster than the previous version of the function.

o Added an option to use a prior distribution of the item difficulty (or threshold) parameters in ‘est_irt’, ‘est_item’, and ‘llike_item’ functions.

o Solved unstable item parameter estimation of ‘est_irt’ and ‘est_item’ functions which occured when the scaling factor of ‘D’ is other than 1.0 and ‘use.aprior = TRUE’.

o Fixed an error which occured in the function ‘est_irt’ when the data set contains missing values and ‘fix.a.1pl = FALSE’.

o Included ‘summary’ method to summarize the IRT calibration results from ‘est_irt’ or ‘est_item’ objects.

o Included a new function of ‘getirt’ to extract various estimates results from ‘est_irt’ or ‘est_item’ objects.

o Fixed an error which happens when “DRM” is specified in the model name in the function ‘est_irt’.

o Included total computation time in the function ‘est_irt’.

o Changed the title of ‘irtplay’ package to “Unidimensional Item Response Theory Modeling”.

o Included a new function of ‘est_irt’ to fit unidimensional IRT models to mixture of dichotomous and polytomous item data using the marginal maximum likelihood estimation with expectation-maximization (MMLE-EM; Bock & Aitkin, 1981) algorithm.

o Included the fixed item parameter calibration (FIPC; Kim, 2006) approach, which is one of useful online calibration methods, in the function ‘est_irt’.

o Updated the documentation to explain how to implement the new function ‘est_irt’.

o Included well-known LSAT6 dichotomous response data set from Thissen (1982).

o Fixed a problem of inaccurate item parameter estimation in the function ‘est_item’ when a prior distribution of the slope parameter is used with a scaling factor other than D = 1.

o Updated the function ‘bring.flexmirt’ to read the item parameters of the generalized partial credit model when the number of score categories are two.

o Updated the function ‘est_score’ to find a smart starting value when MLE is used. More specifically, the smart starting value is a theta value where the log-likelihood is the maximum at the highest peak.

o Included the function ‘run_flexmirt’ to implement flexMIRT software (Cai, 2017) through R.

o Applied a prior distribution to the slope parameters of the IRT 1PL model when the slope parameters are constrained to be equal in the function of ‘est_item’.

o Fixed a problem of using staring values to estimate item parameters in the function of ‘est_item’.

o Fixed a non-convergence problem of the maximum likelihood estimation with fences (MLEF) in the function of ‘est_score’.

o Updated the description and introduction of the package.

o Updated the documentation to explain how to implement the function “est_item” in more detail.

o Updated the README.md file to explain how to implement the function “est_item” in more detail.

o Included the function ‘llike_score’ to compute the loglikelihood function of ability for an examinee.

o Updated the function ‘est_item’ to find better starting values for item parameter calibration.

o Updated the function ‘est_item’ to exclude items that contains no item response data during the item parameter estimation.

o Updated the function ‘est_item’ to count the number of item responses for each item used to estimate the item parameters.

o Updated the function ‘est_score’ to find better starting values when MLE is used.

o Updated the function ‘est_score’ to address NaNs of gradient values and NaNs of hessian values when MLE, MLEF, or MAP is used.

o Fixed a problem of the function ‘est_score’, which returned an error message when a vector of an examinee’s response data was used in the argument of ‘x’.

o Fixed a problem of the function ‘est_score’, which returned an error message when only one dichotomous item or one polytomous item was included in the item meta data set.

o Fixed a problem of the function ‘est_item’, which returned an error message when the inverse of hessian matrix is not obtainable.

o Included the ‘maximum likelihood estimation with fences scoring method (Han, 2016) in the function ’est_score’.

o Included the ‘inverse test characteristic curve (TCC)’ scoring method (e.g., Stocking, 1996) in the function ‘est_score’.

o Included the function ‘llike_item’ to compute the loglikelihood values of items.

o For the function ‘est_item’, default parameters of a-parameter prior distribution were revised

o Updated the function ‘est_item’ to find better starting values for item parameter calibration.

o Updated the function ‘est_score’ to estimate an ability in a brute force way when MLE or MAP fails to find the solution.

o Updated the function ‘irtfit’ to compute the likelihood ratio chi-square fit statistic (G2; Mckinley & Mills, 1985).

o initial release on CRAN