Provides tools to assist planning and monitoring of time-to-event trials under complicated censoring assumptions and/or non-proportional hazards. There are three main components: The first is analytic calculation of predicted time-to-event trial properties, providing estimates of expected hazard ratio, event numbers and power under different analysis methods. The second is simulation, allowing stochastic estimation of these same properties. Thirdly, it provides parametric event prediction using blinded trial data, including creation of prediction intervals. Methods are based upon numerical integration and a flexible object-orientated structure for defining event, censoring and recruitment distributions (Curves).

Provides a series of general purpose tools to perform g-estimation using the methods described in Sjolander and Vansteelandt (2016) <doi:10.1515/em-2015-0005> and Dukes and Vansteelandt <doi:10.1093/aje/kwx347>. The package allows for g-estimation in a wide variety of circumstances, including an end of study or time-varying outcome, and an exposure that is a binary, continuous, or a categorical variable with three or more categories. The package also supports g-estimation with time-varying causal effects and effect modification by a confounding variable.

Implementation of global envelopes for a set of general d-dimensional vectors T
    in various applications. A 100(1-alpha)% global envelope is a band bounded by two
    vectors such that the probability that T falls outside this envelope in any of the d
    points is equal to alpha. Global means that the probability is controlled simultaneously
    for all the d elements of the vectors. The global envelopes can be used for graphical
    Monte Carlo and permutation tests where the test statistic is a multivariate vector or
    function (e.g. goodness-of-fit testing for point patterns and random sets, functional
    analysis of variance, functional general linear model, n-sample test of correspondence
    of distribution functions), for central regions of functional or multivariate data (e.g.
    outlier detection, functional boxplot) and for global confidence and prediction bands
    (e.g. confidence band in polynomial regression, Bayesian posterior prediction). See
    Myllymäki and Mrkvička (2023) <doi:10.48550/arXiv.1911.06583>,
    Myllymäki et al. (2017) <doi:10.1111/rssb.12172>,
    Mrkvička and Myllymäki (2023) <doi:10.1007/s11222-023-10275-7>,
    Mrkvička et al. (2016) <doi:10.1016/j.spasta.2016.04.005>,
    Mrkvička et al. (2017) <doi:10.1007/s11222-016-9683-9>,
    Mrkvička et al. (2020) <doi:10.14736/kyb-2020-3-0432>,
    Mrkvička et al. (2021) <doi:10.1007/s11009-019-09756-y>,
    Myllymäki et al. (2021) <doi:10.1016/j.spasta.2020.100436>,
    Mrkvička et al. (2022) <doi:10.1002/sim.9236>,
    Dai et al. (2022) <doi:10.5772/intechopen.100124>,
    Dvořák and Mrkvička (2022) <doi:10.1007/s00180-021-01134-y>, and
    Mrkvička et al. (2023) <doi:10.48550/arXiv.2309.04746>.