data retrieved, e.g., from Elsevier's SciVerse Scopus,
computing bibliometric impact of individuals,
Depends: R (>= 3.2.0), agop, RSQLite, RGtk2
Imports: hash, stringi, DBI, grDevices, graphics, stats, utils

or modelling phenomena encountered in the social sciences.
This package is deprecated, see 'agop' instead.
Author: Marek Gagolewski [aut, cre] (<https://orcid.org/0000-0003-0637-6028>)

Imports: stringi, DBI, grDevices, graphics, stats, utils

Title: Functions to Plot Confidence Interval
Description: Plot confidence interval from the objects of statistical tests such as
t.test(), var.test(), cor.test(), prop.test() and fisher.test() ('htest' class),
Tukey test [TukeyHSD()], Dunnett test [glht() in 'multcomp' package],
logistic regression [glm()], and Tukey or Games-Howell test [posthocTGH() in
Users are able to set the styles of lines and points.
This package contains the function to calculate odds ratios and their confidence
intervals from the result of logistic regression.

Title: Copula-Based Joint Analysis of Multiple Phenotypes
Authors@R: c( person("Stefan", "Konigorski", email = "stefan.konigorski@gmail.com", role = c("aut", "cre")), person("Yildiz E.", "Yilmaz", email = "yyilmaz@mun.ca", role = c("ctb", "ths")))
Description: We provide a computationally efficient and robust implementation of the recently proposed C-JAMP (Copula-based Joint Analysis of Multiple Phenotypes) method (Konigorski et al., 2018, submitted). C-JAMP allows estimating and testing the association of one or multiple predictors on multiple outcomes in a joint model, and is implemented here with a focus on large-scale genome-wide association studies with two phenotypes. The use of copula functions allows modeling a wide range of multivariate dependencies between the phenotypes, and previous results are supporting that C-JAMP can increase the power of association studies to identify associated genetic variants in comparison to existing methods (Konigorski, Yilmaz, Pischon, 2016, <DOI:10.1186/s12919-016-0045-6>; Konigorski, Yilmaz, Bull, 2014, <DOI:10.1186/1753-6561-8-S1-S72>). In addition to the C-JAMP functions, functions are available to generate genetic and phenotypic data, to compute the minor allele frequency (MAF) of genetic markers, and to estimate the phenotypic variance explained by genetic markers.