Title: A Companion to the Multi-CAST Collection
Authors@R: person("Nils Norman", "Schiborr", email = "nils-norman.schiborr@uni-bamberg.de", role=c("aut", "cre"))
Description: Provides a basic interface for accessing annotation data from
the Multi-CAST collection, a database of spoken natural language texts
edited by Geoffrey Haig and Stefan Schnell. The collection draws from a
diverse set of languages and has been annotated across multiple levels.
Annotation data is downloaded on request from the servers of the
Language Archive Cologne. See the Multi-CAST website
<https://lac.uni-koeln.de/multicast/> for more information and a list of
Maintainer: Nils Norman Schiborr <nils-norman.schiborr@uni-bamberg.de>

Authors@R: person("Nils Norman", "Schiborr", email = "nils-norman.schiborr@uni-bamberg.de", role = c("aut", "cre"))

University of Bamberg. See the Multi-CAST website
<https://multicast.aspra.uni-bamberg.de/> for more information and a list

Title: A Generic Convex-Hull-Based Model Selection Method
Depends: igraph, graphics, plotly, shiny, shinythemes, stats
Description: Given a set of models for which a measure of model (mis)fit and model complexity is provided, CHull(), developed by Ceulemans and Kiers (2006) <doi:10.1348/000711005X64817>, determines the models that are located on the boundary of the convex hull and selects an optimal model by means of the scree test values.
Author: Marlies Vervloet [aut, cre, trl],

Title: Build MultiClass Pair-Based Classifiers using TSPs or RF
Description: A toolbox to train a single sample classifier that uses in-sample feature relationships. The relationships are represented as feature1 < feature2 (e.g. gene1 < gene2). We provide two options to go with. First is based on 'switchBox' package which uses Top-score pairs algorithm. Second is a novel implementation based on random forest algorithm. For simple problems we recommend to use one-vs-rest using TSP option due to its simplicity and for being easy to interpret.  For complex problems RF performs better.  Both lines filter the features first then combine the filtered features to make the list of all the possible rules (i.e. rule1: feature1 < feature2, rule2: feature1 < feature3, etc...).  Then the list of rules will be filtered and the most important and informative rules will be kept. The informative rules will be assembled in an one-vs-rest model or in an RF model.  We provide a detailed description with each function in this package to explain the filtration and training methodology in each line.
Imports: methods, utils, stats, graphics, grDevices, ranger, Boruta,