Packaged: 2022-08-16 10:43:20 UTC; ellessenne

Title: Interactive Document for Working with KNN Analysis
Description: An interactive document on  the topic of K-nearest neighbour (KNN) using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://kartikeyabolar.shinyapps.io/KNNShiny/>.
Packaged: 2019-05-08 19:40:39 UTC; KARTIKEYA

Description: A novel strategy for conducting variable selection without prior model topology
knowledge using the knockoff method (Barber and Candes (2015) <doi:10.1214/15-AOS1337>)
with extreme boosted tree models (Chen and Guestrin (2016) <doi:10.1145/2939672.2939785>).
This method is inspired by the original knockoff method, where the differences between
original and knockoff variables are used for variable selection with false discovery rate
control. In addition to the original knockoff generating methods, two new sampling methods
are available to be implemented, namely the sparse covariance and principal component
knockoff methods. As results, the indices of selected variables are returned.
Imports: glmnet (>= 2.0-18), knockoff, spcov, xgboost, Rdpack (>=

Author: Stefano Cacciatore, Claudio Luchinat, Leonardo Tenori
Title: Knowledge discovery by accuracy maximization
Description: KODAMA (KnOwledge Discovery by Accuracy MAximization) is an unsupervised and semisupervised learning algorithm that performs feature extraction from noisy and high-dimensional data.

Author: Stefano Cacciatore, Leonardo Tenori, Claudio Luchinat, Phillip R. Bennett, and David A. MacIntyre
Title: Knowledge Discovery by Accuracy Maximization
Description: An unsupervised and semi-supervised learning algorithm that performs feature extraction from noisy and high-dimensional data.

Description: KODAMA algorithm is an unsupervised and semi-supervised learning algorithm that performs feature extraction from noisy and high-dimensional data. It facilitates identification of patterns representing underlying groups on all samples in a data set. The algorithm was published by Cacciatore et al. 2014 <DOI:10.1073/pnas.1220873111>. Addition functions was introduced by Cacciatore et al. 2017 <DOI:10.1093/bioinformatics/btw705> to facilitate the identification of key features associated with the generated output and are easily interpretable for the user. Cross-validated techniques are also included in this package.