Computes intervention in prediction measure for assessing variable importance for random forests. See details at I. Epifanio (2017) <DOI:10.1186/s12859-017-1650-8>.

Calculation of key bacterial growth curve parameters using fourth degree polynomial functions. 
    Six growth curve parameters are provided including peak growth rate, doubling time, lag time, maximum growth, and etc.
    'ipolygrowth' takes time series data from individual biological samples (with technical replicates) or multiple samples.

Generates random numbers corresponding to the events on a Poisson point process with changing event rates. This includes the possibility to incorporate additional information such as the number of events occurring or the location of an already known event. It can also generate the probability density functions of specific events in the cases where additional information is available. Based on Hohmann (2019) <arXiv:1901.10754>.

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.