A tool to calculate the performance of a time series in a specific date or period. It is more intended for data analysis in the fields of finance, banking, telecommunications or operational marketing.

Provides methods to evaluate predictive performance of models that 
    estimate risks under hypothetical intervention scenarios 
    (interventional/causal/counterfactual predictions) with observational data 
    subject to treatment-outcome confounding. Inverse probability of treatment 
    weighting (IPTW) is used to construct a pseudopopulation in which all 
    individuals receive a specified intervention, enabling assessment of 
    agreement between predicted risks under the intervention and observed 
    outcomes in the pseudo-population corresponding to that intervention. 
    Package supports binary and time-to-event outcomes under binary 
    interventions made at a single time point. Performance measures supported 
    are AUC (Area Under the receiving operating characteristic Curve), Brier 
    score, observed-expected ratio, and calibration plots. Methods implemented 
    in this package are based on work by Keogh and Van Geloven (2024) 
    <DOI:10.1097/EDE.0000000000001713>.

Fast raking for survey weighting. The computational core
    is written in Rust for speed. Supports multiple raking variables,
    automatic variable selection, weight bounding, and comprehensive
    diagnostics.

The core of the package is cvr2.ipflasso(), an extension of glmnet to be used when the (large) set of available predictors is partitioned into several modalities which potentially differ with respect to their information content in terms of prediction. For example, in biomedical applications patient outcome such as survival time or response to therapy may have to be predicted based on, say, mRNA data, miRNA data, methylation data, CNV data, clinical data, etc. The clinical predictors are on average often much more important for outcome prediction than the mRNA data. The ipflasso method takes this problem into account by using different penalty parameters for predictors from different modalities. The ratio between the different penalty parameters can be chosen from a set of optional candidates by cross-validation or alternatively generated from the input data.