Methods for the computation of surface/image texture indices using a geostatistical based approach (Trevisani et al. (2023) <doi:10.1016/j.catena.2023.106927> and Trevisani and Guth (2025) <doi:10.3390/rs17233864>). It provides various functions for the computation of surface texture indices (e.g., omnidirectional roughness and roughness anisotropy), including the ones based on the robust MAD estimator. The kernels included in the software permit also to calculate the surface/image texture indices directly from the input surface  (i.e., without de-trending) using increments of order 2 and of order 4. It also provides the new radial roughness index (RRI), representing the improvement of the popular topographic roughness index (TRI). The framework can be easily extended with ad-hoc surface/image texture indices.

Implements Surprisal analysis for gene expression data such as RNA-seq or microarray experiments. Surprisal analysis is an information-theoretic method that decomposes gene expression data into a baseline state and constraint-associated deviations, capturing coordinated gene expression patterns under different biological conditions. References: Kravchenko-Balasha N. et al. (2014) <doi:10.1371/journal.pone.0108549>. Zadran S. et al. (2014) <doi:10.1073/pnas.1414714111>. Su Y. et al. (2019) <doi:10.1371/journal.pcbi.1007034>. Bogaert K. A. et al. (2018) <doi:10.1371/journal.pone.0195142>.

Implements the "Residual (Sur)Realism" algorithm described by
    Stefanski (2007) <doi:10.1198/000313007X190079> to generate datasets 
    that reveal hidden images or messages in their residual plots. It offers
    both predefined datasets and tools to embed custom text or images
    into residual structures. Allowing users to create intriguing visual
    demonstrations for teaching model diagnostics.

In a clinical trial, it frequently occurs that the most credible 
  outcome to evaluate the effectiveness of a new therapy (the true endpoint) is 
  difficult to measure. In such a situation, it can be an effective strategy to 
  replace the true endpoint by a (bio)marker that is easier to measure and that 
  allows for a prediction of the treatment effect on the true endpoint (a 
  surrogate endpoint). The package 'Surrogate' allows for an evaluation of the 
  appropriateness of a candidate surrogate endpoint based on the meta-analytic, 
  information-theoretic, and causal-inference frameworks. Part of this software
  has been developed using funding provided from the European Union's Seventh 
  Framework Programme for research, technological development and demonstration 
  (Grant Agreement no 602552), the Special Research Fund (BOF) of Hasselt 
  University (BOF-number: BOF2OCPO3), GlaxoSmithKline Biologicals, Baekeland 
  Mandaat (HBC.2022.0145), and Johnson & Johnson Innovative Medicine.