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