Regularized Categorial Effects/Categorial Effect
Modifiers/Continuous/Smooth effects in GLMs
The function fits generalized structured regression models
with regularized categorical effects, categorical effect
modifiers, continuous effects and smooth effects.

The function fits generalized structured regression models with regularized categorical effects, categorical effect modifiers, continuous effects and smooth effects.

Regularized Categorical Effects/Categorical Effect
Modifiers/Continuous/Smooth effects in GLMs
Generalized structured regression models with regularized categorical effects, categorical effect modifiers, continuous effects and smooth effects.

Regularized Categorical Effects/Categorical Effect
Modifiers/Continuous/Smooth Effects in GLMs

R (>= 2.10.0), Matrix, MASS, splines, mgcv

Global Validation of Linear Models Assumptions
Edsel A. Pena <pena@stat.sc.edu> and Elizabeth H. Slate
<slateeh@musc.edu>
Methods from the paper: Pena, EA and Slate, EH, "Global
Validation of Linear Model Assumptions," J. American
Statistical Association, 101(473):341-354, 2006.

Edsel A. Pena <pena@stat.sc.edu> and Elizabeth H. Slate <slateeh@musc.edu>
Methods from the paper: Pena, EA and Slate, EH, "Global Validation of Linear Model Assumptions," J. American Statistical Association, 101(473):341-354, 2006.

Edsel A. Pena <pena@stat.sc.edu> and Elizabeth H. Slate <eslate@fsu.edu>

Manhattan, Q-Q, and PCA Plots using 'ggplot2'
person(given = "Lindokuhle",
family = "Nkambule",
role = c("aut", "cre"),
email = "lindonkambule116@gmail.com",
comment = c(ORCID = "0000-0002-5682-2834"))
Generate Manhattan, Q-Q, and PCA plots from GWAS and PCA results using 'ggplot2'.
RColorBrewer (>= 1.1.2), ggplot2, ggrepel, dplyr, tidyr,
data.table, scales, stats, grDevices
Lindokuhle Nkambule [aut, cre]
(<https://orcid.org/0000-0002-5682-2834>)

c(
person("Yili", "Xu", email = "yilixu@usc.edu", role = c("aut", "cre")),
person("Anqi", "Wang", email = "wanganqi@usc.edu", role = c("aut")))
Extract and reform data from GWAS (genome-wide association study) results, and then make a single integrated forest plot containing multiple windows of which each shows the result of individual SNPs (or other items of interest).
colorspace, data.table, dplyr, ggplot2, ggrepel, glue, utils