Implementation of the methodologies described in 1) Alexander Petersen, Xi Liu and Afshin A. Divani (2021) <doi:10.1214/20-aos1971>, including global F tests, partial F tests, intrinsic Wasserstein-infinity bands and Wasserstein density bands, and 2) Chao Zhang, Piotr Kokoszka and Alexander Petersen (2022) <doi:10.1111/jtsa.12590>, including estimation, prediction, and inference of the Wasserstein autoregressive models.

Provides functions, tutorials, and examples to preprocess, 
    analyze, and visualize data from whole room indirect calorimeters (WRIC) by 
    Maastricht Instruments, using the 'OmniCal' software. Some functions may also 
    work with WRICs from other manufacturers, though full functionality has only 
    been validated for Maastricht Instruments devices.

A powerful yet simple graphical tool available in the field of psychometrics is the Wright Map (also known as item maps or item-person maps), which presents the location of both respondents and items on the same scale. Wright Maps are commonly used to present the results of dichotomous or polytomous item response models. The 'WrightMap' package provides functions to create these plots from item parameters and person estimates stored as R objects. Although the package can be used in conjunction with any software used to estimate the IRT model (e.g. 'TAM', 'mirt', 'eRm' or 'IRToys' in 'R', or 'Stata', 'Mplus', etc.),  'WrightMap' features special integration with 'ConQuest' to facilitate reading and plotting its output directly.The 'wrightMap' function creates Wright Maps based on person estimates and item parameters produced by an item response analysis. The 'CQmodel' function reads output files created using 'ConQuest' software and creates a set of data frames for easy data manipulation, bundled in a 'CQmodel' object. The 'wrightMap' function can take a 'CQmodel' object as input or it can be used to create Wright Maps directly from data frames of person and item parameters.

Imports variables from 'ReaderBench' (Dascalu et al.,
    2018)<doi:10.1007/978-3-319-66610-5_48>, 'Coh-Metrix' (McNamara et
    al., 2014)<doi:10.1017/CBO9780511894664>, and/or 'GAMET' (Crossley et
    al., 2019) <doi:10.17239/jowr-2019.11.02.01> output files; downloads
    predictive scoring models described in Mercer & Cannon
    (2022)<doi:10.31244/jero.2022.01.03> and Mercer et
    al.(2021)<doi:10.1177/0829573520987753>; and generates predicted
    writing quality and curriculum-based measurement (McMaster & Espin,
    2007)<doi:10.1177/00224669070410020301> scores.