Segment-Based Ordinary Kriging and Segment-Based Regression
Kriging for Spatial Prediction
Segment-based Kriging methods, including segment-based ordinary
Kriging (SOK) and segment-based regression Kriging (SRK) for
spatial prediction of line segment spatial data as described in
Yongze Song (2018) <doi:10.1109/TITS.2018.2805817>. Includes the
spatial prediction and spatial visualisation. The descriptions of
the methods and case datasets refer to the citation information
below.
stats, graphics, rgeos, RColorBrewer, sp, GD, rtop, FitAR,
MASS

SNP-set (Sequence) Kernel Association Test
Seunggeun Lee, Larisa Miropolsky and Micheal Wu

Seunggeun (Shawn) Lee, with contributions from Larisa Miropolsky and Michael Wu
SKAT is a kernel regression based association test between a set of single nucleotide polymorphism (SNP) and dichotomous or quantitative phenotypes. SKAT aggregates individual score test statistics of SNPs in a SNP set and efficiently computes SNP-set level p-values, while adjusting for covariates, such as principal components to account for population stratification.

SNP-Set (Sequence) Kernel Association Test
Functions for kernel regression based association tests including Burden test, SKAT and SKAT-O. These methods aggregate individual SNP score statistics in a SNP set and efficiently computes SNP-set level p-values, while adjusting for covariates.

Functions for kernel-regression-based association tests including Burden test, SKAT and SKAT-O. These methods aggregate individual SNP score statistics in a SNP set and efficiently compute SNP-set level p-values.

Seunggeun (Shawn) Lee and Zhangchen Zhao, with contributions from Larisa Miropolsky and Michael Wu

c(person("Seunggeun (Shawn)", "Lee", role = c("aut", "cre", "cph"), email = "lee7801@snu.ac.kr"),
person("Zhangchen", "Zhao", role = c("aut")),
person("Larisa", "Miropolsky", role = c("aut")),
person("Michael", "Wu", role = c("aut")))
Functions for kernel-regression-based association tests including Burden test, SKAT and SKAT-O. These methods aggregate individual SNP score statistics in a SNP set and efficiently compute SNP-set level p-values. Methods  available in this package include SKAT described in Micheal Wu, Seunggeun Lee et al. (2011) <doi:10.1016/j.ajhg.2011.05.029>, SKAT-O in Seunggeun Lee et al. (2012) <doi:10.1093/biostatistics/kxs014>, combined test of rare and common variants in Iuliana Ionita-Laza and Seunggeun Lee et al. (2013) <doi:10.1016/j.ajhg.2013.04.015>, efficient resampling for binary traits in Seunggeun Lee et al. (2016) <doi:10.1093/biostatistics/kxv033>, and robust test for binary traits in Zhangchen Zhao et al. (2020) <doi:10.1016/j.ajhg.2019.11.012>.
R (>= 2.13.0), Matrix, SPAtest, RSpectra
Seunggeun (Shawn) Lee [aut, cre, cph],
Zhangchen Zhao [aut],
Larisa Miropolsky [aut],
Michael Wu [aut]