Implements a probabilistic approach to time series forecasting combining XGBoost regression with conformal inference methods. The package provides functionality for generating predictive distributions, evaluating uncertainty, and optimizing hyperparameters using Bayesian, coarse-to-fine, or random search strategies.

Helps systematize and ease the process of 
    building unit tests with the 'testthat' package by providing 
    tools for generating expectations.

Allows to provide live interpretations and explanations of statistical 
    functions in R. These interpretations and explanations are shown when the explained function 
    is called by the user. They can interact with the values of the explained function's actual 
    results to offer relevant, meaningful insights. The 'xplain' interpretations and explanations 
    are based on an easy-to-use XML format that allows to include R code to interact with the 
    returns of the explained function.

Provides a consistent interface for common feature importance
    methods as described in Ewald et al. (2024)
    <doi:10.1007/978-3-031-63797-1_22>, including permutation feature
    importance (PFI), conditional and relative feature importance
    (CFI, RFI), leave one covariate out (LOCO), and Shapley
    additive global importance (SAGE), as well as feature sampling
    mechanisms to support conditional importance methods.

Tools for interactive data exploration built using 'shiny'. Includes apps for descriptive 
    statistics, visualizing probability distributions, inferential statistics, linear regression, 
    logistic regression and RFM analysis.

Provides a set of functions devoted to multivariate exploratory statistics on textual data. Classical methods such as correspondence analysis and agglomerative hierarchical clustering are available. Chronologically constrained agglomerative hierarchical clustering enriched with labelled-by-words trees is offered. Given a division of the corpus into parts, their characteristic words and documents are identified. Further, accessing to 'FactoMineR' functions is very easy. Two of them are relevant in textual domain. MFA() addresses multiple lexical table allowing applications such as dealing with multilingual corpora as well as simultaneously analyzing both open-ended and closed questions in surveys. See <http://xplortext.unileon.es> for examples.