Let us consider a sample of patients who can suffer from several diseases simultaneously, in a given set of diseases. The goal of the implemented algorithm is to estimate the individual average cost of each disease, starting from the global health costs available for each patient.

Improved predictive models by indirect classification and
  bagging for classification, regression and survival problems 
  as well as resampling based estimators of prediction error.

Provides methods to perform and analyse I-prior regression models.
    Estimation is done either via direct optimisation of the log-likelihood or 
    an EM algorithm.

Immunotherapy has revolutionized cancer treatment, but predicting patient
    response remains challenging. Here, we presented Intelligent Predicting
    Response to cancer Immunotherapy through Systematic Modeling (iPRISM), a
    novel network-based model that integrates multiple data types to predict
    immunotherapy outcomes. It incorporates gene expression, biological
    functional network, tumor microenvironment characteristics, immune-related
    pathways, and clinical data to provide a comprehensive view of factors
    influencing immunotherapy efficacy. By identifying key genetic and
    immunological factors, it provides an insight for more personalized
    treatment strategies and combination therapies to overcome resistance
    mechanisms.

Provides tools for estimating uncertainty in individual 
    polygenic risk scores (PRSs) using both sampling-based and analytical 
    methods, as well as the Best Linear Unbiased Estimator (BLUE). 
    These methods quantify variability in PRS estimates for both binary 
    and quantitative traits. 
    See Henderson (1975) <doi:10.2307/2529430> for more details.

Functions that wrap popular phylogenetic software for sequence
    alignment, masking of sequence alignments, and estimation of phylogenies and
    ancestral character states.

Estimates the density of a spatially distributed animal population 
  sampled with an array of passive detectors, such as traps. Models incorporating 
  distance-dependent detection are fitted by simulation and inverse prediction 
  as proposed by Efford (2004) <doi:10.1111/j.0030-1299.2004.13043.x>.