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>.

Implements cluster-polarization coefficient for measuring distributional
	polarization in single or multiple dimensions, as well as associated functions.
	Contains support for hierarchical clustering, k-means, partitioning around medoids,
	density-based spatial clustering with noise, and manually imposed cluster membership.
	Mehlhaff (2024) <doi:10.1017/S0003055423001041>.

P-values and no/lowest observed (adverse) effect concentration values derived from the closure principle computational approach test (Lehmann, R. et al. (2015) <doi:10.1007/s00477-015-1079-4>) are provided. The package contains functions to generate intersection hypotheses according to the closure principle (Bretz, F., Hothorn, T., Westfall, P. (2010) <doi:10.1201/9781420010909>), an implementation of the computational approach test (Ching-Hui, C., Nabendu, P., Jyh-Jiuan, L. (2010) <doi:10.1080/03610918.2010.508860>) and the combination of both, that is, the closure principle computational approach test.