Package: molaR
Title: Dental Surface Complexity Measurement Tools
Version: 4.4
Authors@R: c(person("James D.", "Pampush", role = c("aut", "cre", "cph"), email = "jdpampush@gmail.com"),
person("Julia M.", "Winchester", role = c("aut", "cph")),
person("Paul E.", "Morse", role = c("aut", "cph")),
person("Alexander Q.", "Vining", role = c("aut", "cph")))
Description: Surface topography calculations of Dirichlet's normal energy,
relief index, surface slope, and orientation patch count for teeth using scans of
enamel caps.
Importantly, for the relief index and orientation patch count calculations to
work, the scanned tooth files must be oriented with the occlusal plane parallel
to the x and y axes, and perpendicular to the z axis. The files should also be
simplified, and smoothed in some other software prior to uploading into R.
Depends: R (>= 2.10), alphahull, rgl, Rvcg
License: ACM
LazyData: true
Suggests: knitr, rmarkdown, rglwidget
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2018-09-18 16:57:10 UTC; jamespampush
Author: James D. Pampush [aut, cre, cph],
Julia M. Winchester [aut, cph],
Paul E. Morse [aut, cph],
Alexander Q. Vining [aut, cph]
Maintainer: James D. Pampush <jdpampush@gmail.com>
RoxygenNote: 6.1.0
Repository: CRAN
Date/Publication: 2018-09-18 17:20:02 UTC

person("Alexander Q.", "Vining", role = c("aut", "cph")),
Packaged: 2020-03-03 12:27:07 UTC; jamespampush

Depends: R (>= 2.10), alphahull, rgl, Rvcg, pracma
Packaged: 2022-05-12 22:04:43 UTC; jamespampush

Packaged: 2022-08-10 13:27:48 UTC; jamespampush

Title: Multivariate Outlier Detection in Contingency Tables
Description: Outlier detection in, possibly high-dimensional, categorical data following
Imports: Rcpp, doParallel, foreach, ggplot2, ggridges, ess
Suggests: knitr, rmarkdown, pander, dplyr, testthat, igraph

Title: Predicting Differential Drug Response using Multi-Omics Networks
Description: Networks provide a means to incorporate molecular interactions into
reasoning, but on the omics-level, they are currently mainly used to combine
genomic and proteomic information. We here present a novel network analysis
pipeline that enables integrative analysis of multi-omics data including
metabolomics. It allows for comparative conclusions between two different
conditions, such as tumor subgroups, healthy vs. disease, or generally control
Our approach focuses on interactions and their strength instead of on node
properties and includes molecules with low abundance and unknown function. We
use correlation-induced networks that are reduced and combined to form
heterogeneous, multi-omics molecular networks. Prior information such as
metabolite-protein interactions are incorporated. A semi-local, path-based
integration step denoises the network and ensures integrative conclusions. As
case studies, we investigate differential drug response in breast cancer tumor
datasets providing proteomics, transcriptomics, phospho-proteomics and
metabolomics data and contrasting patients with different estrogen receptor
Packaged: 2021-08-05 15:40:14 UTC; julianhugo