Package: fdrtool
Version: 1.2.7
Date: 2011-03-19
Title: Estimation and Control of (Local) False Discovery Rates
Author: Korbinian Strimmer.
Maintainer: Korbinian Strimmer <strimmer@uni-leipzig.de>
Depends: R (>= 2.7.0)
Suggests:
Description: This package allows to estimate both tail area-based false
discovery rates (Fdr) as well as local false discovery rates
(fdr) for a variety of null models (p-values, z-scores,
correlation coefficients, t-scores).  The proportion of null
values and the parameters of the null distribution are
adaptively estimated from the data.  In addition, the package
contains functions for non-parametric density estimation
(Grenander estimator), for monotone regression (isotonic
regression and antitonic regression with weights), for
computing the greatest convex minorant (GCM) and the least
concave majorant (LCM), and for the half-normal and correlation
distributions.
License: GPL (>= 3)
URL: http://strimmerlab.org/software/fdrtool/
Packaged: Sat Mar 19 01:28:04 2011; strimmer
Repository: CRAN
Date/Publication: 2011-03-19 06:20:20

Maintainer: Han Lin Shang <HanLin.Shang@buseco.monash.edu.au>

Packaged: 2013-08-22 09:09:23 UTC; hanlinshang

Packaged: 2018-10-31 18:55:55 UTC; hanlinshang

Title: Frequency Distribution Tables, Histograms and Poligons
Author: Jose Claudio Faria & Enio Jelihovschi
Depends: R (>= 2.6.0), base, grDevices, graphics
Description: Perform frequency distribution tables, associated
histograms and poligons from vector, data.frame and matrix

Author: Jos� Cl�udio Faria & Enio Jelihovschi
Description: Perform frequency distribution tables, associated histograms
and poligons from vector, data.frame and matrix objects.

Title: Frequency Distribution Tables, Histograms and Polygons
Author: Jos� Cl�udio Faria, Enio G. Jelihovschi and Ivan B. Allaman
Depends: R (>= 2.6.0), stats, grDevices, graphics
and polygons from vector, data.frame and matrix objects for

Title: Classification and Feature Selection for microRNA/mRNA
Description: Comprises a pipeline for predicting microRNA/mRNA interactions, as detailed in Williams, Calinescu, Mohorianu (2020) <doi:10.1101/2020.12.23.424130>. Its input consists of [a] a messenger RNA (mRNA) dataset (either in fasta format, focused on 3' UTRs or in gtf format; for the latter, the sequences of the 3’ UTRs are generated using the genomic coordinates), [b] a microRNA dataset (in fasta format, retrieved from miRBase,  <http://www.mirbase.org/>) and [c] an interaction dataset (in csv format, from miRTarBase <http://mirtarbase.cuhk.edu.cn/php/index.php>). To characterise and predict microRNA/mRNA interactions, we use [a] statistical analyses based on Chi-squared and Fisher exact tests and [b] Machine Learning classifiers (decision trees, random forests and support vector machines). To enhance the accuracy of the classifiers we also employ feature selection approaches used in on conjunction with the classifiers. The feature selection approaches include a voting scheme for decision trees, a measure based on Gini index for random forests, forward feature selection and Genetic Algorithms on SVMs. The pipeline also includes a novel approach based on embryonic Genetic Algorithms which combines and optimises the forward feature selection and Genetic Algorithms. All analyses, including the classification and feature selection, are applicable on the microRNA seed features (default), on the full microRNA features and/or flanking features on the mRNA. The sets of features can be combined.
Imports: stringr, randomForest, rpart, rpart.plot, GA, e1071, ggplot2,
Config/reticulate: list( packages = list( list(package = "os"),
list(package = "argparse"), list(package = "gzip"),
list(package = "pandas"), list(package = "numpy"), list(package
= "math"), list(package = "scipy.stats"), list(package =
"matplotlib.pyplot"), list(package = "seaborn"), list(package =
"statistics"), list(package = "logging"), list(package = "Bio")
SystemRequirements: Python (>=3.6) sreformat patman