Estimation of functional spaces based on traits of organisms.
    The package includes functions to impute missing trait values (with or
    without considering phylogenetic information), and to create,
    represent and analyse two dimensional functional spaces based on
    principal components analysis, other ordination methods, or raw
    traits. It also allows for mapping a third variable onto the
    functional space.  See 'Carmona et al. (2021)' 
    <doi:10.1038/s41586-021-03871-y>, 'Puglielli et al.  (2021)'
    <doi:10.1111/nph.16952>, 'Carmona et al. (2021)'
    <doi:10.1126/sciadv.abf2675>, 'Carmona et al. (2019)'
    <doi:10.1002/ecy.2876> for more information.

Helpers for parsing out the R functions
  and packages used in R scripts and notebooks.

Implementation of two sample comparison procedures based on
    median-based statistical tests for functional data, introduced in
    Smida et al (2022) <doi:10.1080/10485252.2022.2064997>.  Other
    competitive state-of-the-art approaches proposed by Chakraborty and
    Chaudhuri (2015) <doi:10.1093/biomet/asu072>, Horvath et al (2013)
    <doi:10.1111/j.1467-9868.2012.01032.x> or Cuevas et al (2004)
    <doi:10.1016/j.csda.2003.10.021> are also included in the package, as
    well as procedures to run test result comparisons and power analysis
    using simulations.

A collection of methods for modeling time-to-event data using both functional and scalar predictors. It implements functional data analysis techniques for estimation and inference, allowing researchers to assess the impact of functional covariates on survival outcomes, including time-to-single event and recurrent event outcomes.

Computes the functional tangential angle pseudo-depth and its robustified version from the paper by Kuhnt and Rehage (2016). See Kuhnt, S.; Rehage, A. (2016): An angle-based multivariate functional pseudo-depth for shape outlier detection, JMVA 146, 325-340, <doi:10.1016/j.jmva.2015.10.016> for details.

Nonparametric estimators and tests for time series analysis. The functions use bootstrap techniques and robust nonparametric difference-based estimators to test for the presence of possibly non-monotonic trends and for synchronicity of trends in multiple time series.