Extends the Cox model to events with more than one causes. Also supports random and fixed effects, tied events, and time-varying variables. Model details are provided in Peng et al. (2018) <doi:10.1509/jmr.14.0643>.

Calculate the R-squared, aka explained randomness, based on the partial likelihood ratio statistic under the Cox Proportional Hazard model [J O'Quigley, R Xu, J Stare (2005) <doi:10.1002/sim.1946>].

An implementation of robust estimation in Cox model. Functionality includes fitting efficiently and robustly Cox proportional hazards regression model in its basic form, where explanatory variables are time independent with one event per subject.  Method is based on a smooth modification of the partial likelihood.

Fits Cox regression based on retrospectively ascertained times-to-event. The method uses Inverse-Probability-Weighting estimating equations.

Fit a CoxSEI (Cox type Self-Exciting Intensity) model to right-censored counting process data.

Functions for calculating the conditional power
             for different models in survival time analysis
             within randomized clinical trials
             with two different treatments to be compared
             and survival as an endpoint.

Functions to check whether a vector of p-values respects the assumptions of FDR (false discovery rate) control procedures and to compute adjusted p-values.

Functions to test and compare causal models using Confirmatory Path Analysis.

Provides a comprehensive framework for time series omics analysis,
  integrating changepoint detection, smooth and shape-constrained trends,
  and uncertainty quantification. It supports gene- and transcript-level inferences,
  p-value aggregation for improved power, and both case-only and case-control designs.
  It includes an interactive 'shiny' interface. The methods are described in Yates et al. (2024) <doi:10.1101/2024.12.22.630003>.

A Bayesian meta-analysis method for studying cross-phenotype
    genetic associations. It uses summary-level data across multiple phenotypes to
    simultaneously measure the evidence of aggregate-level pleiotropic association
    and estimate an optimal subset of traits associated with the risk locus. CPBayes
    is based on a spike and slab prior. The methodology is available from: A Majumdar, T Haldar, S Bhattacharya, JS Witte (2018) <doi:10.1371/journal.pgen.1007139>.