Contains the Correlates of State Policy Project dataset (+ codebook) assembled by Marty P. Jordan and Matt Grossmann (2020) <http://ippsr.msu.edu/public-policy/correlates-state-policy> used by the 'cspp' package. The Correlates data contains over 3000 variables across more than 100 years that pertain to state politics and policy in the United States.

Encode and decode c-squares, from and to simple feature (sf)
    or spatiotemporal arrays (stars) objects. Use c-squares codes to quickly
    join or query spatial data.

Account for uncertainty when working with ranks. 
    Estimate standard errors consistently in linear regression with ranked variables.
    Construct confidence sets of various kinds for positions of populations in a ranking 
    based on values of a certain feature and their estimation errors. 
    Theory based on Mogstad, Romano, Shaikh, and Wilhelm (2023)<doi:10.1093/restud/rdad006> and
    Chetverikov and Wilhelm (2023) <doi:10.48550/arXiv.2310.15512>.

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.

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'.
    '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>'.