Depends: R (>= 2.10), network, latentnet, gtools

Title: Dengue Cases and Climate Variables in Colombo Sri Lanka
Description: Weekly notified dengue cases and climate variables in Colombo district Sri Lanka from 2008/ week-52 to 2014/ week-21.

Title: Colocalisation tests of two genetic traits
Description: Performs the colocalisation tests described in Plagnol et al (2009) and Wallace et al (in preparation).

Description: Performs the colocalisation tests described in Plagnol et
al (2009) and Wallace et al (in preparation).

Collate: 'AllClasses.R' 'bma.R' 'coloc-package.R' 'coloc.combine.R'

Authors@R: c(person("Chris", "Wallace", role=c("aut","cre"), email =
Author: Chris Wallace, with contributions from Claudia Giambartolemei
al (2009), Wallace et al (2013) and Giambartolomei et al

Author: Chris Wallace and Claudia Giambartolemei with contributions

Description: Performs the colocalisation tests described in Plagnol et al
(2009), Wallace et al (2013) and Giambartolomei et al (2013).

Imports: ggplot2, snpStats, BMA, reshape, methods, flashClust, speedglm
Title: Colocalisation Tests of Two Genetic Traits
Description: Performs the colocalisation tests described in
Plagnol et al (2009) <doi:10.1093/biostatistics/kxn039>,
Giambartolomei et al (2013) <doi:10.1371/journal.pgen.1004383>.

Imports: data.table, ggplot2, snpStats, BMA, reshape, methods
Suggests: knitr, testthat, bindata, rmarkdown

Imports: data.table, ggplot2, methods, viridis, stats, grDevices,
Suggests: knitr, testthat, snpStats, mvtnorm, magrittr, rmarkdown
Giambartolomei et al (2013) <doi:10.1371/journal.pgen.1004383>,
Collate: 'check.R' 'claudia.R' 'coloc-package.R' 'plot.R' 'private.R'
'sensitivity.R' 'split.R' 'susie.R' 'testdata.R' 'zzz.R'

Title: Normalized Spatial Intensity Correlation
Author: Jiahui Xu, Xueyan Liu, Cheng Cheng, Hui Zhang
Description: Calculate the colocalization index, NSInC, in two different ways as described in the paper (Liu et al., 2018. Manuscript submitted for publication.) for multiple-species spatial data which contain the precise locations and membership of each spatial point. The two main functions are nsinc.d() and nsinc.z(). They provide the Pearson’s correlation coefficients of signal proportions in different memberships within a concerned proximity of every signal (or every base signal if single direction colocalization is considered) across all (base) signals using two different ways of normalization. The proximity sizes could be an individual value or a range of values, where the default ranges of values are different for the two functions.