Title: Identification of Cancer Dysfunctional Subpathway with Omics
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"))

Description: Kernel Estimators for Interval-Censored Data
Packaged: Wed Oct 12 12:05:27 2005; braun

Packaged: Thu Nov 23 00:25:52 2006; braun

Depends: R (>= 2.0.1), KernSmooth, stats

Description: 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".
Imports: boot, knitr, lavaan, MASS, stringr
Suggests: rmarkdown, testthat (>= 3.0.0)
Author: Sam Parsons [aut, cre] (<https://orcid.org/0000-0002-7048-4093>),

Title: Individual Conditional Expectation Plot Toolbox
Author: Alex Goldstein, Adam Kapelner, Justin Bleich
Description: 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.

Description: 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.