Shilin Zhao, Linlin Yin, Yan Guo, Quanhu Sheng, Yu Shyr

Heatmap Fit Statistic For Binary Dependent Variable Models
c(person("Justin", "Esarey", role = c("aut", "cre"), email =
"Justin@justinesarey.com"), person("Andrew", "Pierce", role
="aut", email = "awpierc@emory.edu"))
Generates a fit plot for diagnosing misspecification in
GLM models, and calculates the related heatmap fit statistic
(Esarey and Pierce, 2012).

Fit statistic for binary dependent variable models
c(person("Justin", "Esarey", role = c("aut", "cre"), email =
"justin@justinesarey.com"), person("Andrew", "Pierce", role = "aut", email
= "awpierc@emory.edu"), person("Jericho", "Du", role= "aut", email =
"jericho.du@gmail.com"))
Generates a fit plot for diagnosing misspecification in models of
binary dependent variables, and calculates the related heatmap fit
statistic (Esarey and Pierce, 2012).
Justin Esarey [aut, cre],
Andrew Pierce [aut],
Jericho Du [aut]

Fit Statistic for Binary Dependent Variable Models
Generates a fit plot for diagnosing misspecification in models of
binary dependent variables, and calculates the related heatmap fit
statistic described in Esarey and Pierce (2012) <DOI:10.1093/pan/mps026>.
R (>= 3.1.1), stats, graphics, grDevices

A set of tools supporting more flexible heatmaps. The graphics is grid-like using
the old graphics system. The main function is heatmap.n2(), which is a wrapper around the various functions
constructing individual parts of the heatmap, like sidebars, picket plots, legends etc. The function supports zooming
and splitting, i.e., having (unlimited) small heatmaps underneath each other in one plot deriving from the same data set,
e.g., clustered and ordered by a supervised clustering method.
Biobase, Heatplus, grDevices, stats, graphics

Interactive Cluster Heat Maps Using 'plotly'
c(person("Tal", "Galili", role = c("aut", "cre", "cph"), email =
"tal.galili@gmail.com", comment = "https://www.r-statistics.com"),
person("Jonathan", "Sidi", email = "yonis@metrumrg.com", comment = "https://github.com/yonicd",role = "ctb"),
person("Alan", "O'Callaghan", comment = "https://github.com/Alanocallaghan",role = "ctb"),
person("Yoav", "Benjamini", email = "ybenja@tau.ac.il",role = "ths"))
Create interactive cluster 'heatmaps' that can be saved as a stand-
alone HTML file, embedded in 'R Markdown' documents or in a 'Shiny' app, and
available in the 'RStudio' viewer pane. Hover the mouse pointer over a cell to
show details or drag a rectangle to zoom. A 'heatmap' is a popular graphical
method for visualizing high-dimensional data, in which a table of numbers
are encoded as a grid of colored cells. The rows and columns of the matrix
are ordered to highlight patterns and are often accompanied by 'dendrograms'.
'Heatmaps' are used in many fields for visualizing observations, correlations,
missing values patterns, and more. Interactive 'heatmaps' allow the inspection
of specific value by hovering the mouse over a cell, as well as zooming into
a region of the 'heatmap' by dragging a rectangle around the relevant area.
This work is based on the 'ggplot2' and 'plotly.js' engine. It produces
similar 'heatmaps' as 'heatmap.2' or 'd3heatmap', with the advantage of speed
('plotly.js' is able to handle larger size matrix), the ability to zoom from
the 'dendrogram' panes, and the placing of factor variables in the sides of the
'heatmap'.
ggplot2 (>= 2.2.0), dendextend (>= 1.4.0), magrittr (>=
1.0.1), reshape2, scales, seriation, utils, stats, grDevices,
methods, colorspace, RColorBrewer, htmlwidgets, gplots,
assertthat
Tal Galili [aut, cre, cph] (https://www.r-statistics.com),
Jonathan Sidi [ctb] (https://github.com/yonicd),
Alan O'Callaghan [ctb] (https://github.com/Alanocallaghan),
Yoav Benjamini [ths]