Identification of Cancer Dysfunctional Subpathway with Omics
Data
c(
person("Junwei", "Han", email = "hanjunwei1981@163.com", role = "cre"),
person("Baotong", "Zheng", email = "btzheng1116@163.com", role = "aut"),
person("Siyao", "Liu", email = "liusiyao29@163.com", role = "ctb"))
Junwei Han [cre],
Baotong Zheng [aut],
Siyao Liu [ctb]

Kernel Estimators for Interval-Censored Data

c(person(given = "Sam",
family = "Parsons",
email = "sam.parsons@radboudumc.nl",
role = c("aut", "cre"),
comment = c(ORCID = "0000-0002-7048-4093")),
person(given = "Rogier",
family = "Kievit",
role = c("aut"),
comment = c(ORCID = "0000-0003-0700-4568")),
person(given = "Andreas",
family = "Brandmaier",
role = c("aut"),
comment = c(ORCID = "0000-0001-8765-6982")))
Estimate test-retest reliability for complex sampling strategies
and extract variances using IntraClass Effect Decomposition. Developed by
Brandmaier et al. (2018) "Assessing reliability in neuroimaging research through intra-class effect decomposition (ICED)" <doi:10.7554/eLife.35718>
Also includes functions to simulate data based on sampling strategy.
Unofficial version release name: "Good work squirrels".

Individual Conditional Expectation Plot Toolbox
Alex Goldstein, Adam Kapelner, Justin Bleich
This package implements Individual Conditional Expectation (ICE) plots, a tool for visualizing the model estimated by any supervised learning algorithm. ICE plots refine Friedman's partial dependence plot by graphing the functional relationship between the predicted response and a covariate of interest for individual observations. Specifically, ICE plots highlight the variation in the fitted values across the range of a covariate of interest, suggesting where and to what extent they may exist.

Implements Individual Conditional Expectation (ICE) plots, a tool for visualizing the model estimated by any supervised learning algorithm. ICE plots refine Friedman's partial dependence plot by graphing the functional relationship between the predicted response and a covariate of interest for individual observations. Specifically, ICE plots highlight the variation in the fitted values across the range of a covariate of interest, suggesting where and to what extent they may exist.