Implementation of analytical and sampling-based power analyses for
    the Wald, likelihood ratio (LR), score, and gradient tests. Can be applied
    to item response theory (IRT) models that are fitted using marginal maximum
    likelihood estimation. The methods are described in our paper
    (Zimmer et al. (2022) <doi:10.1007/s11336-022-09883-5>).

Fit unidimensional item response theory (IRT) models to test
    data, which includes both dichotomous and polytomous items, calibrate 
    pretest item parameters, estimate examinees' abilities, and examine 
    the IRT model-data fit on item-level in different ways as well as provide 
    useful functions related to IRT analyses such as IRT model-data fit 
    evaluation and differential item functioning analysis. 
    The bring.flexmirt() and write.flexmirt() functions were written by modifying 
    the read.flexmirt() function (Pritikin & Falk (2022) <doi:10.1177/0146621620929431>).
    The bring.bilog() and bring.parscale() functions were written by modifying the read.bilog() 
    and read.parscale() functions, respectively (Weeks (2010) <doi:10.18637/jss.v035.i12>).
    The bisection() function was written by modifying the bisection() function 
    (Howard (2017, ISBN:9780367657918)). The code of the inverse test characteristic curve 
    scoring in the est_score() function was written by modifying the irt.eq.tse() function 
    (González (2014) <doi:10.18637/jss.v059.i07>). In est_score() function, the code of weighted 
    likelihood estimation method was written by referring to the Pi(), Ji(), and Ii() functions
    of the catR package (Magis & Barrada (2017) <doi:10.18637/jss.v076.c01>).

Helper functions and example data sets to facilitate the estimation of IRTree models from data with different shape and using different software.

Estimation of reliability coefficients for ability estimates and sum scores from item response theory models as defined in Cheng, Y., Yuan, K.-H. and Liu, C. (2012) <doi:10.1177/0013164411407315> and Kim, S. and Feldt, L. S. (2010) <doi:10.1007/s12564-009-9062-8>. The package supports the 3-PL and generalized partial credit models and includes estimates of the standard errors of the reliability coefficient estimators, derived in Andersson, B. and Xin, T. (2018) <doi:10.1177/0013164417713570>.