A collection of tools for estimating a network from a random sample of cognitive social structure (CSS) slices. Also contains functions for evaluating a CSS in terms of various error types observed in each slice.

Selection of the number of clusters in cluster analysis using
    stability methods.

A uniform  statistical inferential tool in making individualized treatment decisions, which implements the methods of Ma et al. (2017)<DOI:10.1177/0962280214541724> 
    and Guo et al. (2021)<DOI:10.1080/01621459.2020.1865167>. It uses a flexible semiparametric modeling strategy for heterogeneous treatment effect estimation in high-dimensional settings and can gave valid confidence bands. Based on it, one can find the subgroups of patients that benefit from each treatment, thereby making individualized treatment selection.

Helpful functions for the cleaning and manipulation of surveillance data, especially with regards to the creation and validation of panel data from individual level surveillance data.

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>.