Flexible parametric models for time-to-event data,
    including the Royston-Parmar spline model, generalized gamma and
    generalized F distributions.  Any user-defined parametric
    distribution can be fitted, given at least an R function defining
    the probability density or hazard. There are also tools for
    fitting and predicting from fully parametric multi-state models,
    based on either cause-specific hazards or mixture models.

Flexible parametric mixture and non-mixture cure models for time-to-event data.

Use a grammar for creating and customizing pretty tables.
    The following formats are supported: 'HTML', 'PDF', 'Typst', 'RTF',
    'Microsoft Word', 'Microsoft PowerPoint', R 'Grid Graphics' and
    'patchwork'.  'R Markdown', 'Quarto' and the package 'officer' can be
    used to produce the result files. The syntax is the same for the user
    regardless of the type of output to be produced. A set of functions
    allows the creation, definition of cell arrangement, addition of
    headers or footers, formatting and definition of cell content with
    text and or images. The package also offers a set of high-level
    functions that allow tabular reporting of statistical models and the
    creation of complex cross tabulations.

Enables the construction of flexible urban delineations that
    can be tailored to specific applications or research questions, see Van
    Migerode et al. (2024) <DOI:10.1177/23998083241262545> and Van Migerode
    et al. (2025) <DOI:10.5281/zenodo.15173220>.
    Originally developed to flexibly reconstruct the Degree of
    Urbanisation classification of cities,
    towns and rural areas developed by Dijkstra et al. 
    (2021) <DOI:10.1016/j.jue.2020.103312>. Now it also support a broader 
    range of delineation approaches, using multiple datasets – including
    population, built-up area, and night-time light grids – and different
    thresholding methods.

Estimation of mixed models including a subject-specific variance which can be time and covariate dependent. In the joint model framework, the package handles left truncation and allows a flexible dependence structure between the competing events and the longitudinal marker. The estimation is performed under the frequentist framework, using the Marquardt-Levenberg algorithm. (Courcoul, Tzourio, Woodward, Barbieri, Jacqmin-Gadda (2023) <arXiv:2306.16785>).