Calculate the predictive discrete Fourier transform, complete discrete Fourier transform, complete periodogram, and tapered complete periodogram. This algorithm is based on the preprint "Spectral methods for small sample time series: A complete periodogram approach" (2020) by Sourav Das, Suhasini Subba Rao, and Junho Yang.

A tool that imports, subsets, visualizes, and exports the Correlates of State Policy Project dataset assembled by Marty P. Jordan and Matt Grossmann (2020) <http://ippsr.msu.edu/public-policy/correlates-state-policy>. The Correlates data contains over 2000 variables across more than 100 years that pertain to state politics and policy in the United States. Users with only a basic understanding of R can subset this data across multiple dimensions, export their search results, create map visualizations, export the citations associated with their searches, and more.

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.

Functionality for segmenting individual trees from a forest stand scanned with a close-range (e.g., terrestrial or mobile) laser scanner. The complete workflow from a raw point cloud to a complete tabular forest inventory is provided. The package contains several algorithms for detecting tree bases and a graph-based algorithm to attach all remaining points to these tree bases. It builds heavily on the 'lidR' package. A description of the segmentation algorithm can be found in Larysch et al. (2025) <doi:10.1007/s10342-025-01796-z>.

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>.