Thematic quality indices are provided to facilitate the evaluation
    and quality control of geospatial data products (e.g. thematic maps, remote
    sensing classifications, etc.). The indices offered are based on the
    so-called confusion matrix. This matrix is constructed by comparing the
    assigned classes or attributes of a set of pairs of positions or objects
    in the product and the ground truth. In this package it is considered that
    the classes of the ground truth correspond to the columns and that the
    classes of the product to be valued correspond to the rows. The package
    offers two object classes with their methods: 'ConfMatrix' (Confusion
    matrix) and 'QCCS' (Quality Control Columns Set). The 'ConfMatrix' class of
    objects offers more than 20 methods based on the confusion matrix. The
    'QCCS' class of objects offers a different perspective in which the ground
    truth is considered to allow the values of the column marginals to be fixed,
    see Ariza López et al. (2019) <doi:10.3390/app9204240> and Canran Liu et al.
    (2007) <doi:10.1016/j.rse.2006.10.010> for more details. The package was
    created with 'R6'.

Provides tools for the combination of individual study results in
    meta-analyses using 'p-value' functions. Implements various combination methods 
    including those by Fisher, Stouffer, Tippett, Edgington along with 
    weighted generalizations. Contains functionality for the visualization 
    and calculation of confidence curves and drapery plots to summarize 
    evidence across studies.

Fits emulators, also known as surrogates or response surfaces, using conformal inference with random forests. The conformal calibration is performed using out-of-bag samples from the forest, eliminating the need for a separate hold-out set. The method is based on Johansson et al. (2014 <doi:10.1007/s10994-014-5453-0>).

Provides functions to construct finite-sample calibrated predictive 
    intervals for Bayesian models, following the approach in Barber et al. 
    (2021) <doi:10.1214/20-AOS1965>. These intervals are calculated efficiently
    using importance sampling for the leave-one-out residuals. By default,
    the intervals will also reflect the relative uncertainty in the Bayesian 
    model, using the locally-weighted conformal methods of Lei et al. (2018)
    <doi:10.1080/01621459.2017.1307116>.