Set of forecasting tools to predict ICU beds using a Vector Error Correction model with a single cointegrating vector. Method described in  Berta, P. Lovaglio, P.G. Paruolo, P. Verzillo, S., 2020. "Real Time Forecasting of Covid-19 Intensive Care Units demand" Health, Econometrics and Data Group (HEDG) Working Papers 20/16, HEDG, Department of Economics, University of York, <https://www.york.ac.uk/media/economics/documents/hedg/workingpapers/2020/2016.pdf>.

Computes confidence intervals for the positive predictive value (PPV) and negative predictive value (NPV) based on varied scenarios. In situations where the proportion of diseased subjects does not correspond to the disease prevalence (e.g. case-control studies), this package provides two types of solutions: 1) five methods for estimating confidence intervals for PPV and NPV via ratio of two binomial proportions including Gart & Nam (1988), Walter (1975), MOVER-J (Laud, 2017), Fieller (1954), and Bootstrap (Efron, 1979); 2) three direct methods that compute the confidence intervals including Pepe (2003), Zhou (2007), and Delta. In prospective studies where the proportion of diseased subjects is an unbiased estimate of the disease prevalence, this package provides several methods for calculating the confidence intervals for PPV and NPV including Clopper-Pearson, Wald, Wilson, Agresti-Coull, and Beta. See the Details and References sections in the corresponding functions.

Provides a framework for specifying and running flexible
    linear-time reachability-based algorithms for graphical causal inference. Rule
    tables are used to encode and customize the reachability algorithm to typical
    causal and probabilistic reasoning tasks such as finding d-connected nodes or
    more advanced applications. For more information, see Wienöbst, Weichwald and
    Henckel (2025) <doi:10.48550/arXiv.2506.15758>.

A publication-ready toolkit for modern survival and competing risks
    analysis with a minimal, formula-based interface. Both nonparametric
    estimation and direct polytomous regression of cumulative incidence
    functions (CIFs) are supported. The main functions 'cifcurve()', 'cifplot()',
    and 'cifpanel()' estimate survival and CIF curves and produce high-quality
    graphics with risk tables, censoring and competing-risk marks, and
    multi-panel or inset layouts built on 'ggplot2' and 'ggsurvfit'. The modeling
    function 'polyreg()' performs direct polytomous regression for coherent joint
    modeling of all cause-specific CIFs to estimate risk ratios, odds ratios, or
    subdistribution hazard ratios at user-specified time points. All core
    functions adopt a formula-and-data syntax and return tidy and extensible
    outputs that integrate smoothly with 'modelsummary', 'broom', and the broader
    'tidyverse' ecosystem. Key numerical routines are implemented in C++ via
    'Rcpp'.