Cumulative descriptive statistics for (arithmetic, geometric, harmonic) mean, median, mode, variance, skewness and kurtosis.

Tools for visualization of, and inference on, the calibration of prediction models on the cumulative domain. This provides a method for evaluating calibration of risk prediction models without having to group the data or use tuning parameters (e.g., loess bandwidth). This package implements the methodology described in Sadatsafavi and Patkau (2024) <doi:10.1002/sim.10138>. The core of the package is cumulcalib(), which takes in vectors of binary responses and predicted risks. The plot() and summary() methods are implemented for the results returned by cumulcalib().

Access the 'Cumulocity' API and retrieve data on devices, measurements, and events. Documentation for the API can be found at <https://www.cumulocity.com/guides/reference/rest-implementation/>.

Calculates and visualises cumulative percent 'decay' curves,
    which are typically calculated from metagenomic taxonomic profiles.
    These can be used to estimate the level of expected 'endogenous' taxa
    at different abundance levels retrieved from metagenomic samples, when
    comparing to samples of known sampling site or source. Method
    described in Fellows Yates, J. A. et. al. (2021) Proceedings of the
    National Academy of Sciences USA <doi:10.1073/pnas.2021655118>.

Contains functions for estimating generalized parametric mixture and non-mixture cure models <doi:10.1016/j.cmpb.2022.107125>, loss of lifetime, mean residual lifetime, and crude event probabilities.

Estimate mixture cure models with subgroup survival probabilities as auxiliary information. A reference of the underlying methods is Jie Ding, Jialiang Li and Xiaoguang Wang (2024) <doi:10.1093/jrsssc/qlad106>.

Cure dependent censoring regression models for long-term survival multivariate data. These models are based on extensions of the frailty models, capable to accommodating the cure fraction and the dependence between failure and censoring times, with Weibull and piecewise exponential marginal distributions. Theoretical details regarding the models implemented in the package can be found in Schneider et al. (2022) <doi:10.1007/s10651-022-00549-0>.