Title: Exploratory Data Analysis and Data Preparation Tool-Box
lazyeval, utils, RColorBrewer, moments, entropy, cli, stringr
Packaged: 2019-09-08 16:49:32 UTC; oblaphouses

Packaged: 2019-10-09 01:08:22 UTC; oblaphouses

Packaged: 2019-08-26 14:21:12 UTC; oblaphouses

Title: Fungal Species Identification using DNA Barcode with Random
Author: P K Meher <meherprabin@yahoo.com>
Maintainer: P K Meher <meherprabin@yahoo.com>
Imports: bold, randomForest, Biostrings, BioSeqClass
Description: A machine learning based approach for fungal species identification using barcode sequence data. The multi-class random forest model has been used for prediction purpose, where the gap-pair compositional feature was used to encode the barcode sequence data. The encoded dataset was used as input for prediction purpose. Though this approach has been developed for fungal species identification in particular, can be used for other species identification as well.

Title: Clean and Visualize Over Expression Results from
Authors@R: c(person(given = "Raghvendra",family = "Mall",role = c("aut","cre"), email = "rmall@hbku.edu.qa"))
Description: Provides functions to have visualization and clean-up of enriched gene ontologies (GO) terms,
protein complexes and pathways (obtained from multiple databases) using 'ConsensusPathDB'
from gene set over-expression analysis. Performs clustering of pathway based on similarity
of over-expressed gene sets and visualizations similar to Ingenuity Pathway Analysis (IPA)
when up and down regulated genes are known. The methods are described in a paper currently
submitted by Orecchioni et al, 2020 in Nanoscale.
Depends: ggplot2, igraph, devtools, ggrepel, grDevices, R (>= 3.6)
Packaged: 2020-08-03 17:45:30 UTC; raghvendra

Authors@R: c(person(given = "Raghvendra",family = "Mall",role = c("aut","cre"), email = "raghvendra5688@gmail.com"))
Packaged: 2022-07-22 21:02:48 UTC; administrator

person("Fabio", "Centofanti", role = c("aut")),
Description: Provides functional control charts
for statistical process monitoring of functional data,
using the methods of Capezza et al. (2020) <doi:10.1002/asmb.2507> and
Depends: fda, dplyr, ggplot2, R (>= 4.0.0)
Imports: rlang, parallel, tidyr, patchwork
Packaged: 2021-03-12 14:07:18 UTC; christian