c(person("Dipro", "Sinha", role = c("aut", "cre"),email = "diprosinha@gmail.com"),
person("Sayanti", "Guha Majumdar", role = "aut", email = "sayanti23gm@gmail.com"),
person("Anu", "Sharma", role = "aut", email = "anu9.dixit@gmail.com"),
person(as.person("Dwijesh Chandra Mishra"), role = "aut", email = "dwij.mishra@gmail.com")
)
Clustering in metagenomics is the process of grouping of microbial contigs in species specific bins. This package contains functions that extract genomic features from metagenome data, find the number of clusters for that given data and find the best clustering algorithm for binning.
factoextra, cluster, dbscan, dplyr, seqinr, Biostrings
Dipro Sinha [aut, cre],
Sayanti Guha Majumdar [aut],
Anu Sharma [aut],
Dwijesh Chandra Mishra [aut]

Extract and Summarise Data from Published Figures
c(person("Joel", "Pick", email = "joel.l.pick@gmail.com", role = c("aut")), person("Shinichi", "Nakagawa", email = "s.nakagawa@unsw.edu.au", role = c("aut")), person("Daniel", "Noble", email = "daniel.wa.noble@gmail.com", role = c("aut", "cre")))
High-throughput, flexible and reproducible extraction of data from figures in primary research papers. metaDigitise() can extract data and / or automatically calculate summary statistics for users from box plots, bar plots (e.g., mean and errors), scatter plots and histograms.
Joel Pick [aut],
Shinichi Nakagawa [aut],
Daniel Noble [aut, cre]

Automated Intuitive Package for Meta-Ensemble Learning
Extends the base classes and methods of 'caret' package for integration of base learners. The user can input the number of different base learners, and specify the final learner, along with the train-validation-test data partition split ratio. The predictions on the unseen new data is the resultant of the ensemble meta-learning <https://machinelearningmastery.com/stacking-ensemble-machine-learning-with-python/> of the heterogeneous learners aimed to reduce the generalization error in the predictive models. It significantly lowers the barrier for the practitioners to apply heterogeneous ensemble learning techniques in an amateur fashion to their everyday predictive problems.
caret, ggplot2, graphics, e1071, gbm, randomForest