Title: OGA+HDIC+Trim and High-Dimensional Linear Regression Models
Author: Hai-Tang Chiou, Ching-Kang Ing, Tze Leung Lai
Description: Ing and Lai (2011) <doi:10.5705/ss.2010.081> proposed a high-dimensional model selection procedure that comprises three steps: orthogonal greedy algorithm (OGA), high-dimensional information criterion (HDIC), and Trim. The first two steps, OGA and HDIC, are used to sequentially select input variables and determine stopping rules, respectively. The third step, Trim, is used to delete irrelevant variables remaining in the second step. This package aims at fitting a high-dimensional linear regression model via OGA+HDIC+Trim.

Title: R Client for Mobilize/Andwellness server
Description: R Client for Mobilize and Andwellness server. Implements
basic R functions to call and process data.
Depends: R (>= 2.14), stats, methods, reshape, plyr, RColorBrewer

Description: R Client for Mobilize and Andwellness server. Implements basic R

Description: R Client for Ohmage 2 server. Implements basic R

Title: Multivariate Methods Based on the Oja Median and Related
Author: Daniel Fischer, Karl Mosler, Jyrki Möttönen, Klaus Nordhausen, Oleksii Pokotylo, Daniel Vogel
Description: Functions to calculate the Oja median, Oja signs and ranks and methods based upon them.

Author: Daniel Fischer, Jyrki M�tt�nen, Klaus Nordhausen, Daniel Vogel
Description: The package provides functions for the Oja median, Oja
signs and ranks and methods based upon them.

Description: Functions to calculate the Oja median, Oja signs and ranks and methods based upon them. For details, see the the corresponding publication in the Journal of Statistical Software <doi:10.18637/jss.v092.i08>.

Title: A k-Nearest Neighbours Ensemble via Optimal Model Selection for
Description: Optimal k Nearest Neighbours Ensemble is an ensemble of base k nearest neighbour models each constructed on a bootstrap sample with a random subset of features. k closest observations are identified for a test point "x" (say), in each base k nearest neighbour model to fit a stepwise regression to predict the output value of "x". The final predicted value of "x" is the mean of estimates given by all the models. The implemented model takes training and test datasets and trains the model on training data to predict the test data. Ali, A., Hamraz, M., Kumam, P., Khan, D.M., Khalil, U., Sulaiman, M. and Khan, Z. (2020) <DOI:10.1109/ACCESS.2020.3010099>.