Analyze Multivariate Phenotypes by Combining Univariate Results
Shuo Li <skyli@bu.edu>, Xuan Liu <liuxuan@bu.edu> and Qiong Yang <qyang@bu.edu>
Combining Univariate Association Test Results of Multiple Phenotypes for Detecting Pleiotropy.

Compute Decision Interval and Average Run Length for CUSUM
Charts
Douglas M. Hawkins, David H. Olwell, Boxiang Wang
Computation of decision intervals (H) and average run lengths (ARL) for CUSUM charts.

Douglas M. Hawkins, David H. Olwell, and Boxiang Wang

Computation of decision intervals (H) and average run lengths (ARL) for CUSUM charts. Details of the method are seen in Hawkins and Olwell (2012): Cumulative sum charts and charting for quality improvement, Springer Science & Business Media.

c(person(given = "José", family = "Gama", role = c("aut","cre","trl"),email = "jgama@abo.fi"),
person(given = "Maria", family = "Nyberg", role = "aut"),
person(given = "Göran", family = "Högnäs", role = "ths"),
person(given = "Brian", family = "Foutch", role = "ctb"),
person(given = "Mark", family = "Grundland", role = "ctb"),
person(given = "Neil", family = "Dodgson", role = "ctb"))
Methods for color vision deficiencies (CVD), to help understanding and mitigating issues with CVDs and to generate tests for diagnosis and interpretation.
José Gama [aut, cre, trl],
Maria Nyberg [aut],
Göran Högnäs [ths],
Brian Foutch [ctb],
Mark Grundland [ctb],
Neil Dodgson [ctb]

c(person(given = "Jose", family = "Gama", role = c("aut","cre","trl"),email = "rxprtgama@gmail.com"),
person(given = "Brian", family = "Foutch", role = "ctb"),
person(given = "Mark", family = "Grundland", role = "ctb"),
person(given = "Neil", family = "Dodgson", role = "ctb"))
Jose Gama [aut, cre, trl],
Brian Foutch [ctb],
Mark Grundland [ctb],
Neil Dodgson [ctb]

c(person("Wenying", "Deng", role = c("aut", "cre"),
email = "wdeng@g.harvard.edu"),
person("Jeremiah Zhe", "Liu", role = c("ctb")))
Implementation of Cross-Validated Kernel Ensemble (CVEK),
a flexible modeling framework for robust nonlinear regression and
hypothesis testing based on ensemble learning with kernel-ridge estimators
(Jeremiah et al. (2017) <arXiv:1710.01406> and
Wenying et al. (2018) <arXiv:1811.11025>). It allows user to conduct
nonlinear regression with minimal assumption on the function form by
aggregating nonlinear models generated from a diverse collection of kernel
families. It also provides utilities to test for the estimated nonlinear
effect under this ensemble estimator, using either the asymptotic or
the bootstrap version of a generalized score test.
testthat, knitr, rmarkdown, ggplot2, ggrepel
Wenying Deng [aut, cre],
Jeremiah Zhe Liu [ctb]