Depends: R(>= 2.10.0), sp, RANN, PBSmapping, RgoogleMaps
Author: Markus Loecher <markus.loecher@gmail.com> and Madhav Kumar <madhavkumar2005@gmail.com>
Description: Facilitates efficient polygon search using kd trees.
Coordinate level spatial data can be aggregated to higher geographical
identities like census blocks, ZIP codes or police district boundaries.
This process requires mapping each point in the given data set to a
particular identity of the desired geographical hierarchy. Unless efficient
data structures are used, this can be a daunting task. The operation
point.in.polygon() from the package sp is computationally expensive.
Here, we exploit kd-trees as efficient nearest neighbor search algorithm
to dramatically reduce the effective number of polygons being searched.

Title: A Fast and Light Package to Compute Polygenic Risk Scores
person("Guillermo","Reales", role = c("aut", "cre"), email = "gr440@cam.ac.uk",  comment = c(ORCID = "0000-0001-9993-3916")),
person("Chris", "Wallace", role = "aut", email = "cew54@cam.ac.uk", comment = c(ORCID = "0000-0001-9755-1703")),
person("Olly", "Burren", role = "ctb", email = "ob219@cam.ac.uk", comment = c(ORCID = "0000-0002-3388-5760")))
Description: Quickly computes polygenic scores from GWAS summary statistics of either case-control or quantitative traits, without LD matrix computation or parameter tuning. Reales,G., Kelemen,M., Wallace,C. (2020) <doi:10.1101/2020.07.24.220392> "RápidoPGS: A rapid polygenic score calculator for summary GWAS data without validation dataset".
Depends: R (>= 3.6.0), data.table, RCurl, curl
Author: Guillermo Reales [aut, cre] (<https://orcid.org/0000-0001-9993-3916>),

Description: Quickly computes polygenic scores from GWAS summary statistics of either case-control or quantitative traits, without LD matrix computation or parameter tuning. Reales,G., Vigorito, E., Kelemen,M., Wallace,C. (2021) <doi:10.1101/2020.07.24.220392> "RápidoPGS: A rapid polygenic score calculator for summary GWAS data without a test dataset".
Depends: R (>= 4.0), data.table, RCurl, curl
Imports: dplyr (>= 1.0.6), GenomicRanges (>= 1.42.0), IRanges (>=