A small package containing functions to perform a joint calibration of totals and quantiles. The calibration for totals is based on Deville and Särndal (1992) <doi:10.1080/01621459.1992.10475217>, the calibration for quantiles is based on Harms and Duchesne (2006) <https://www150.statcan.gc.ca/n1/en/catalogue/12-001-X20060019255>. The package uses standard calibration via the 'survey', 'sampling' or 'laeken' packages. In addition, entropy balancing via the 'ebal' package and empirical likelihood based on codes from Wu (2005) <https://www150.statcan.gc.ca/n1/pub/12-001-x/2005002/article/9051-eng.pdf> can be used. See the paper by Beręsewicz and Szymkowiak (2023) for details <arXiv:2308.13281>.

Tools for competing risks trials that allow simultaneous inference on
    recovery and mortality endpoints. Provides data preparation helpers, standard
    cumulative incidence estimators (restricted mean time gained/lost), and severity
    weighted extensions that integrate longitudinal ordinal outcomes to summarise
    treatment benefit. Methods follow Wen, Hu, and Wang (2023) Biometrics 79(3):1635-1645
    <doi:10.1111/biom.13752>.

Fit survival data and perform dynamic prediction under joint frailty-copula models for tumour progression and death.
 Likelihood-based methods are employed for estimating model parameters, where the baseline hazard functions are modeled by the cubic M-spline or the Weibull model.
 The methods are applicable for meta-analytic data containing individual-patient information from several studies.
 Survival outcomes need information on both terminal event time (e.g., time-to-death) and non-terminal event time (e.g., time-to-tumour progression).
 Methodologies were published in
 Emura et al. (2017) <doi:10.1177/0962280215604510>, Emura et al. (2018) <doi:10.1177/0962280216688032>,
 Emura et al. (2020) <doi:10.1177/0962280219892295>, Shinohara et al. (2020) <doi:10.1080/03610918.2020.1855449>,
 Wu et al. (2020) <doi:10.1007/s00180-020-00977-1>, and Emura et al. (2021) <doi:10.1177/09622802211046390>.
 See also the book of Emura et al. (2019) <doi:10.1007/978-981-13-3516-7>.
 Survival data from ovarian cancer patients are also available.