Package: modes
Title: Find the Modes and Assess the Modality of Complex and Mixture
Distributions, Especially with Big Datasets
Version: 0.6.1
Date: 2016-02-05
Author: Sathish Deevi [aut, cre],
4D Strategies [aut,own]
Maintainer: Sathish Deevi <SathishCDeevi@gmail.com>
Copyright: 4D Strategies
Description: Designed with a dual purpose of
accurately estimating the mode (or modes) as well as characterizing
the modality of data. The specific application area includes complex
or mixture distributions particularly in a big data environment.
The heterogeneous nature of (big) data may require deep introspective
statistical and machine learning techniques, but these statistical tools
often fail when applied without first understanding the data. In small
datasets, this often isn't a big issue, but when dealing with large scale
data analysis or big data thoroughly inspecting each dimension
typically yields an O(n^n-1) problem. As such, dealing with big data
require an alternative toolkit. This package not only identifies the
mode or modes for various data types, it also provides a programmatic
way of understanding the modality (i.e. unimodal, bimodal, etc.) of
a dataset (whether it's big data or not). See
<http://www.sdeevi.com/modes_package> for examples and discussion.
Depends: R (>= 3.2.2)
License: CC BY-NC-SA 4.0
Collate: 'Utility_functions.R' 'Nonparametric_functions.R'
'Parametric_functions.R'
URL: http://www.sdeevi.com/modes_package
NeedsCompilation: no
Packaged: 2016-02-06 00:15:40 UTC; Novus PC
Repository: CRAN
Date/Publication: 2016-02-06 11:07:55

Title: Model-Based Dose Escalation Shiny Tools
Authors@R: c(person("Philip", "Pallmann", role=c("aut", "cre"), email="p.pallmann@lancaster.ac.uk"), person("Fang", "Wan", role="aut"))
Description: User-friendly Shiny apps for designing and evaluating phase I cancer clinical trials, with the aim to estimate the maximum tolerated dose (MTD) of a novel drug, using a Bayesian logistic regression method.

Authors@R: c(person("Philip", "Pallmann", role=c("aut", "cre"), email="pallmannp@cardiff.ac.uk"), person("Fang", "Wan", role="aut"))
Description: User-friendly Shiny apps for designing and evaluating phase I cancer clinical trials, with the aim to estimate the maximum tolerated dose (MTD) of a novel drug, using a Bayesian decision procedure based on logistic regression.
Imports: knitr, rhandsontable, shiny, shinyBS