Provides tools for working with observational health data in the 
  Observational Medical Outcomes Partnership (OMOP) Common Data Model format with a pipe friendly syntax.
  Common data model database table references are stored in a single compound object along with metadata.

Provides useful tools for cognitive diagnosis modeling (CDM). The package includes functions for empirical Q-matrix estimation and validation, such as the Hull method (Nájera, Sorrel, de la Torre, & Abad, 2021, <doi:10.1111/bmsp.12228>) and the discrete factor loading method (Wang, Song, & Ding, 2018, <doi:10.1007/978-3-319-77249-3_29>). It also contains dimensionality assessment procedures for CDM, including parallel analysis and automated fit comparison as explored in Nájera, Abad, and Sorrel (2021, <doi:10.3389/fpsyg.2021.614470>). Other relevant methods and features for CDM applications, such as the restricted DINA model (Nájera et al., 2023; <doi:10.3102/10769986231158829>), the general nonparametric classification method (Chiu et al., 2018; <doi:10.1007/s11336-017-9595-4>), and corrected estimation of the classification accuracy via multiple imputation (Kreitchmann et al., 2022; <doi:10.3758/s13428-022-01967-5>) are also available. Lastly, the package provides some useful functions for CDM simulation studies, such as random Q-matrix generation and detection of complete/identified Q-matrices.

Wrapper functions to model and extract various quantitative information from absorption spectra of chromophoric dissolved organic matter (CDOM).

Parallel coordinate plotting with resolutions for large data sets
 and missing values.

A wrapper for the 'CDRC' 'API' that returns data frames or 'sf' of 'CDRC' data. The 'API' web reference is:<https://api.cdrc.ac.uk/swagger/index.html>.

This is an implementation of constrained dual scaling for
    detecting response styles in categorical data, including utility functions. The
    procedure involves adding additional columns to the data matrix representing the
    boundaries between the rating categories. The resulting matrix is then doubled
    and analyzed by dual scaling. One-dimensional solutions are sought which provide
    optimal scores for the rating categories. These optimal scores are constrained
    to follow monotone quadratic splines. Clusters are introduced within which the
    response styles can vary. The type of response style present in a cluster can
    be diagnosed from the optimal scores for said cluster, and this can be used to
    construct an imputed version of the data set which adjusts for response styles.