Description: Pulls together a collection of datasets from Miguel de Carvalho research articles. Including, for example:
Packaged: 2022-04-28 06:38:47 UTC; mdecarvalho

Title: Deconvolution Analysis Tool for Dynamic Contrast Enhanced MRI
Maintainer: Gregory Z. Ferl <ferl.gregory@gene.com>
Depends: R (>= 2.11.0), xtable, akima, R.oo, R.methodsS3, matlab,
lattice, locfit, graphics, grDevices, grid
Description: This package performs voxel-wise deconvolution analysis of
DCE-MRI contrast agent concentration versus time data and
generates the Impulse Response Function, which can be used to
approximate commonly utilized kinetic parameters such as
K^trans and ve.  An interactive advanced voxel diagnosis tool
(AVDT) is also provided to facilitate easy navigation of

approximate commonly utilized kinetic parameters such as Ktrans
and ve.  An interactive advanced voxel diagnosis tool (AVDT) is
also provided to facilitate easy navigation of voxel-wise data.

Title: Differential Analysis with Random Reference Genes
Description: Differential Analysis of short RNA transcripts that can be modeled by either Poisson or Negative binomial distribution.  The statistical methodology implemented in this package is based on the random selection of references genes (Desaulle et al. (2021) <arXiv:2103.09872>).
Author: Dorota Desaulle [aut, cre] (<https://orcid.org/0000-0002-3419-9447>),

Title: Detecting Breakpoints and Estimating Segments in Trend (DBEST)
Description: An algorithm for analysing vegetation time series with two main application domains - generalising vegetation trends and characterizing vegetation trend changes. The algorithm simplifies the trend into linear segments using one of three parameters: a generalisation threshold parameter, the largest changes, or a threshold for the magnitude of changes. The outputs are the simplified trend, the change type (abrupt or non-abrupt), and the time and magnitude of the change.

Title: Detecting Breakpoints and Estimating Segments in Trend
Description: A program for analyzing vegetation time series, with two algorithms: 1) change detection algorithm that detects trend changes, determines their type (abrupt or non-abrupt), and estimates their timing, magnitude, number, and direction; 2) generalization algorithm that simplifies the temporal trend into main features. The user can set the number of major breakpoints or magnitude of greatest changes of interest for detection, and can control the generalization process by setting an additional parameter of generalization-percentage.