Fit unidimensional item response theory (IRT) models to a mixture 
    of dichotomous and polytomous data, calibrate online item parameters 
    (i.e., pretest and operational items), estimate examinees' abilities, 
    and examine the IRT model-data fit on item-level in different ways 
    as well as provide useful functions related to unidimensional IRT models. 
    For the item parameter estimation, the marginal maximum likelihood estimation 
    via the expectation-maximization (MMLE-EM) algorithm 
    (Bock & Aitkin (1981) <doi:10.1007/BF02294168>) is used. 
    For the online calibration, the fixed item parameter calibration method 
    (Kim (2006) <doi:10.1111/j.1745-3984.2006.00021.x>) and 
    the fixed ability parameter calibration method
    (Ban, Hanson, Wang, Yi, & Harris (2011) <doi:10.1111/j.1745-3984.2001.tb01123.x>) 
    are provided. For the ability estimation, several popular scoring methods 
    (e.g., MLE, EAP, and MAP) are implemented. In terms of assessing the IRT 
    model-data fit, one of distinguished features of this package is that it 
    gives not only well-known item fit statistics (e.g., chi-square (X2), 
    likelihood ratio chi-square (G2), infit and oufit statistics, and 
    S-X2 statistic (Ames & Penfield (2015) <doi:10.1111/emip.12067>)) 
    but also graphical displays to look at residuals between the observed 
    data and model-based predictions 
    (Hambleton, Swaminathan, & Rogers (1991, ISBN:9780803936478)). 
    In addition, there are many useful functions such as analyzing differential item 
    functioning, computing asymptotic variance-covariance matrices of item parameter 
    estimates (Li & Lissitz (2004) <doi:10.1111/j.1745-3984.2004.tb01109.x>), 
    importing item and/or ability parameters from popular IRT software, 
    running 'flexMIRT' (Cai, 2017) through R, generating simulated data, 
    computing the conditional distribution of observed scores using 
    the Lord-Wingersky recursion formula (Lord & Wingersky (1984) 
    <doi:10.1177/014662168400800409>), computing the loglikelihood of individual 
    items, computing the loglikelihood of abilities, computing item and test 
    information functions, computing item and test characteristic curve functions, 
    and plotting item and test characteristic curves and item and test information 
    functions. See Lim and Wells (2022) <doi:10.18637/jss.v103.i12> for more details.