Provides easy and consistent time conversion for public health purposes. The time conversion functions provided here are between date, ISO week, ISO yearweek, ISO year, calendar month/year, season, season week.

Exploits dynamical seasonal forecasts in order to provide
    information relevant to stakeholders at the seasonal timescale. The package
    contains process-based methods for forecast calibration, bias correction,
    statistical and stochastic downscaling, optimal forecast combination and
    multivariate verification, as well as basic and advanced tools to obtain
    tailored products. This package was developed in the context of the ERA4CS 
    project MEDSCOPE and the H2020 S2S4E project and includes contributions from 
    ArticXchange project founded by EU-PolarNet 2. Implements methods described in
    Pérez-Zanón et al. (2022) <doi:10.5194/gmd-15-6115-2022>,
    Doblas-Reyes et al. (2005) <doi:10.1111/j.1600-0870.2005.00104.x>,
    Mishra et al. (2018) <doi:10.1007/s00382-018-4404-z>,
    Sanchez-Garcia et al. (2019) <doi:10.5194/asr-16-165-2019>,
    Straus et al. (2007) <doi:10.1175/JCLI4070.1>,
    Terzago et al. (2018) <doi:10.5194/nhess-18-2825-2018>,
    Torralba et al. (2017) <doi:10.1175/JAMC-D-16-0204.1>,
    D'Onofrio et al. (2014) <doi:10.1175/JHM-D-13-096.1>,
    Verfaillie et al. (2017) <doi:10.5194/gmd-10-4257-2017>,
    Van Schaeybroeck et al. (2019) <doi:10.1016/B978-0-12-812372-0.00010-8>,
    Yiou et al. (2013) <doi:10.1007/s00382-012-1626-3>.

Domain mean estimation with monotonicity or block monotone constraints. See Xu X, Meyer MC and Opsomer JD (2021)<doi:10.1016/j.jspi.2021.02.004> for more details.

Utility functions that help with common base-R problems relating to lists.
    Lists in base-R are very flexible. This package provides functions to quickly and easily
    characterize types of lists. That is, to identify if all elements in a list
    are null, data.frames, lists, or fully named lists. Other functionality is provided
    for the handling of lists, such as the easy splitting of lists into equally sized
    groups, and the unnesting of data.frames within fully named lists.