Provides the Augmented Dickey-Fuller test and its variations to check the existence of bubbles (explosive behavior) for time series, based on the article by Peter C. B. Phillips, Shuping Shi and Jun Yu (2015a) <doi:10.1111/iere.12131>. Some functions may take a while depending on the size of the data used, or the number of Monte Carlo replications applied.

Build CPMs (cumulative probability models, also known as cumulative link models) to account for detection limits (both single and multiple detection limits) in response variables. Conditional quantiles and conditional CDFs can be calculated based on fitted models. The package implements methods described in Tian, Y., Li, C., Tu, S., James, N. T., Harrell, F. E., & Shepherd, B. E. (2022). "Addressing Detection Limits with Semiparametric Cumulative Probability Models". <arXiv:2207.02815>.

Provides tools to perform interrupted-time series through a generalised least squares (GLS) framework on linear outcomes. Allows for multiple interventions and a control with ARMA (autoregressive and moving-average) correction. For more details see Lopez Bernal, Cummins, and Gasparrini (2017) <doi:10.1093/ije/dyw098>.

Fit Cox proportional hazard models with a weighted 
  partial likelihood. It handles one or multiple endpoints, additional matching 
  and makes it possible to reuse controls for other endpoints 
  Stoer NC and Samuelsen SO (2016) <doi:10.32614/rj-2016-030>.

Fits generalized linear models, Cox proportional-hazards
    models, log-rank tests, generalized estimating equations, mixed models
    with repeated measures, Kaplan-Meier curves, quantile differences, and
    hierarchical net-benefit (win-difference) and log win-ratio statistics
    jointly across multiple endpoints, and returns the full asymptotic
    covariance matrix linking them. Implements PATED (Prognostic Assisted
    Treatment Effect Detection), a randomized-trial method that exploits
    balanced prognostic covariates to tighten standard errors and increase
    statistical power without introducing bias.