A collection of functions dedicated to simulating staggered entry platform 
    trials whereby the treatment under investigation is a combination of two 
    active compounds. In order to obtain approval for this combination therapy,
    superiority of the combination over the two active compounds and superiority
    of the two active compounds over placebo need to be demonstrated. 
    A more detailed description of the design can be found in 
    Meyer et al. <DOI:10.1002/pst.2194> and a manual in Meyer et al. <arXiv:2202.02182>.

Functions to simplify the process of preparing event and transaction for cohort analysis.

Estimate survival using data mapped to the Observational Medical Outcomes Partnership common data model. Survival can be estimated based on user-defined study cohorts.

Calculating crude sequence ratio, adjusted sequence ratio and 
    confidence intervals using data mapped to the Observational Medical 
    Outcomes Partnership Common Data Model.

Functions to make lifetables and to calculate hazard function estimate using Poisson regression model with splines. Includes function to draw simple flowchart of cohort study. Function boxesLx() makes boxes of transition rates between states. It utilizes 'Epi' package 'Lexis' data.

Copula-based imputation methods: parametric and nonparametric algorithms for missing multivariate data through conditional copulas.

Conditional inference procedures for the general independence
             problem including two-sample, K-sample (non-parametric ANOVA),
             correlation, censored, ordered and multivariate problems described
             in <doi:10.18637/jss.v028.i08>.

Extract and monitor price and market cap of 'Cryptocurrencies' from 'Coin Market Cap' <https://coinmarketcap.com/api/>.

Several authors have proposed methods for constructing simultaneous confidence intervals for multinomial proportions. The package implements seven classical approaches—Wilson, Quesenberry and Hurst, Goodman, Wald (with and without continuity correction), Fitzpatrick and Scott, and Sison and Glaz—along with Bayesian methods based on Dirichlet models. Both equal and unequal Dirichlet priors are supported, providing a broad framework for inference, data analysis, and sensitivity evaluation.