A high performance package estimating Cox Model when an even has more than one causes. It also supports random and fixed effects, tied events, and time-varying variables.

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>.

Implements several statistical tests for structural change, specifically the tests featured in Horváth, Rice and Miller (in press): CUSUM (with weighted/trimmed variants), Darling-Erdös, Hidalgo-Seo, Andrews, and the new Rényi-type test.