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.

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.

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.