Title: Enrichment Analysis and Intersecting Sankey Diagram
Authors@R: c(person("Zhi", "Zhao", email = "zhi.zhao@medisin.uio.no", role = c("aut","cre")), person("Manuela", "Zucknick", email = "manuela.zucknick@medisin.uio.no", role = c("aut")), person("Tero", "Aittokallio", email = "t.a.aittokallio@medisin.uio.no", role = c("ctb")))
Description: A flexible tool for enrichment analysis based on user-defined sets is provided. It allows users to perform over-representation analysis of the custom sets among any specified ranked feature list, hence making enrichment analysis applicable to various types of data from different scientific fields. 'EnrichIntersect' also enables an interactive means to visualize identified associations based on, for example, the mix-lasso model (Zhao et al. (2022) <doi:10.1016/j.isci.2022.104767>) or similar methods.
Imports: ggplot2, dplyr, networkD3, jsonlite, htmlwidgets, webshot2,

Description: A flexible tool for enrichment analysis based on user-defined sets. It allows users to perform over-representation analysis of the custom sets among any specified ranked feature list, hence making enrichment analysis applicable to various types of data from different scientific fields. 'EnrichIntersect' also enables an interactive means to visualize identified associations based on, for example, the mix-lasso model (Zhao et al. (2022) <doi:10.1016/j.isci.2022.104767>) or similar methods.

Author: Saeid Amiri, Bertrand Clarke and Jennifer Clarke.
Depends: stats, utils, graphics, dendextend, ggplot2, ggdendro, seqinr,
Description: An implementation of the clustering methods of categorical data
discussed in Amiri, S., Clarke, B., and Clarke, J. (2015). Clustering categorical
data via ensembling dissimilarity matrices. Preprint arXiv:1506.07930 <https://arxiv.org/abs/1506.07930>.
Packaged: 2017-01-18 13:50:58 UTC; saeidamiri

data via ensembling dissimilarity matrices.  Preprint <arXiv:1506.07930>.
Packaged: 2017-02-01 00:37:12 UTC; saeidamiri

Title: Extensible Package for Parallel, Batch Training of Base Learners
Description: Extensible S4 classes and methods for batch training of regression and classification algorithms such as Random Forest, Gradient Boosting Machine, Neural Network, Support Vector Machines, and K-Nearest Neighbors. These algorithms constitute a set of 'base learners', which can subsequently be combined together to form ensemble predictions. This package provides cross-validation wrappers to allow for downstream application of ensemble integration techniques, including best-error selection. All base learner estimation objects are retained, allowing for repeated prediction calls without the need for re-training. For large problems, an option is provided to save estimation objects to disk, along with prediction methods that utilize these objects. This allows users to train and predict with large ensembles of base learners without being constrained by system RAM.