Collate: 'AMAPVox.R' 'Butterfly.R' 'Canopy.R' 'Classes.R' 'ComputeG.R'
'Crop.R' 'FillNA.R' 'Generics.R' 'Getters.R' 'Ground.R'

Title: A Multiple Comparison Perspective
Authors@R: c(person(given="Scott", family="Maxwell", role = "aut"), person(given="Harold", family="Delaney", role = "aut"), person("Ken", "Kelley", role = c("aut", "cre"), email = "kkelley@nd.edu"))
Description: Accompanies "Designing Experiments and analyzing data: A model comparison perspective"" (3rd ed.) Maxwell, Delaney, & Kelley (forthcoming). This package contains all of the data sets in the book. Information about the book is available here <http://www.DesigningExperiments.com>.

Description: Accompanies "Designing experiments and analyzing data: A model comparison perspective"" (3rd ed.) Maxwell, Delaney, & Kelley (forthcoming). This package contains all of the data sets in the book. Information about the book is available here <http://www.DesigningExperiments.com>.

Description: Accompanies "Designing experiments and analyzing data: A model comparison perspective" (3rd ed.) by Maxwell, Delaney, & Kelley (forthcoming from Routledge). This package contains all of the data sets in the book. Information about the book is available at <http://www.DesigningExperiments.com>.

Description: Accompanies "Designing experiments and
analyzing data: A model comparison perspective" (3rd ed.) by
Maxwell, Delaney, & Kelley (forthcoming from Routledge).
Contains all of the data sets in the book's chapters and
end-of-chapter exercises. Information about the book is available at

Maxwell, Delaney, & Kelley (2018; Routledge).

Title: Generate LaTeX Code for Auto-Multiple-Choice (AMC)
Description: Generate code for use with the Optical Mark Recognition free software Auto Multiple Choice (AMC). More specifically, this package provides functions that use as input the question and answer texts, and output the LaTeX code for AMC.
Packaged: 2017-03-28 23:14:39 UTC; nmyszkowski

Packaged: 2018-11-10 18:40:46 UTC; nmyszkowski

Description: AMGET allows to simply and rapidly creates highly informative diagnostic plots for ADAPT 5 models. Features include data analysis prior any modeling form either NONMEM or ADAPT shaped dataset, goodness-of-fit plots (GOF), posthoc-fits plots (PHF), parameters distribution plots (PRM) and visual predictive check plots (VPC) based on ADAPT output.