Imports: sampling(>= 2.8), rmarkdown(>= 1.9), SmartEDA, data.table(>=
Packaged: 2021-03-07 17:26:53 UTC; dubrangala

Imports: sampling(>= 2.8), rmarkdown, SmartEDA, data.table(>=
Packaged: 2021-06-12 17:25:55 UTC; dubrangala

Packaged: 2021-10-18 10:47:50 UTC; dubrangala

Title: Implementation of a Machine Learning Framework for Predicting
Maintainer: Giovanna Maria Dimitri <gmd43@cam.ac.uk>
Description: An implementation of a Machine Learning Framework for prediction of new drugs Side Effects.
Firstly drugs are clustered with respect to their features description and secondly predictions are made, according to Bayesian scores.
Moreover it can perform protein enrichment considering the proteins clustered together in the first step of the algorithm.
This last tool is of extreme interest for biologist and drug discovery purposes, given the fact that it can be used either as a validation of the clusters obtained, as well as for the possible discovery of new interactions between certain side effects and non targeted pathways.
Clustering of the drugs in the feature space can be done using K-Means, PAM or K-Seeds (a novel clustering algorithm proposed by the author).
Imports: ROCR, MESS, cclust, cluster, e1071, utils, base

Packaged: 2016-04-23 07:26:09 UTC; GiovannaMariaDimitri

Title: Predict Drug Functional Similarity to Drug Repurposing
person(given = "Ji", family = "Li", role  = "aut"),
Description: A systematic biology tool was developed to repurpose drugs via a drug-drug functional similarity network. 'DrugSim2DR' first predict drug-drug functional similarity  in the context of specific disease, and then using the similarity constructed a weighted drug similarity network. Finally, it used a network propagation algorithm on the network to identify drugs with significant target abnormalities as candidate drugs.
Imports: igraph, stats, pheatmap, ChemmineR, rvest, base, sp, tidyr,

Title: Analyze Two Choice Reaction Time Data with the D*M Method
Author: Don van den Bergh, Stijn Verdonck, Francis Tuerlinckx
Maintainer: Don van den Bergh <donvdbergh@hotmail.com>
Description: A collection of functions to estimate parameters of a diffusion model via a D*M analysis. Build in models are: the Ratcliff diffusion model, the RWiener diffusion model, and Linear Ballistic Accumulator models. Custom models functions can be specified as long as they have a density function.
Imports: DEoptim, RWiener, rtdists, methods, stats, ggplot2