Methods for assessing the performance of a prediction model with respect to identifying patient-level treatment benefit. All methods are applicable for continuous and binary outcomes, and for any type of statistical or machine-learning prediction model as long as it uses baseline covariates to predict outcomes under treatment and control.

An implementation of prediction intervals for overdispersed count data,
            for overdispersed binomial data and for linear random effects models.

Recent years have seen an increased interest in novel methods
    for analyzing quantitative data from experimental psychology. Currently, however, they lack an
    established and accessible software framework. Many existing implementations provide no guidelines,
    consisting of small code snippets, or sets of packages. In addition, the use of existing packages
    often requires advanced programming experience. 'PredPsych' is a user-friendly toolbox based on
    machine learning predictive algorithms. It comprises of multiple functionalities for multivariate
    analyses of quantitative behavioral data based on machine learning models.

An implementation of reliability estimation methods described in the paper (Bosnic, Z., & Kononenko, I. (2008) <doi:10.1007/s10489-007-0084-9>), which allows you to test the reliability of a single predicted instance made
             by your model and prediction function. It also allows you to make a correlation test to estimate which reliability estimate is the most 
             accurate for your model.

Evaluate the predictive performance of an existing (i.e. previously 
    developed) prediction/ prognostic model given relevant information about the 
    existing prediction model (e.g. coefficients) and a new dataset. Provides a 
    range of model updating methods that help tailor the existing model to the 
    new dataset; see Su et al. (2018) <doi:10.1177/0962280215626466>. Techniques 
    to aggregate multiple existing prediction models on the new data are also 
    provided; see Debray et al. (2014) <doi:10.1002/sim.6080> and Martin et al. 
    (2018) <doi:10.1002/sim.7586>).