Packaged: 2017-01-19 11:15:58 UTC; boessenkool

Imports: OpenStreetMap, berryFunctions (>= 1.15.0), sp
Packaged: 2017-04-12 12:57:19 UTC; boessenkool

Title: Orthogonal Sparse Non-Negative Matrix Tri-Factorization
Description: A novel method to implement cancer subtyping and subtype specific drug targets identification via non-negative matrix tri-factorization.
To improve the interpretability, we introduce orthogonal constraint to the row coefficient matrix and column coefficient matrix. To meet the prior knowledge
that each subtype should be strongly associated with few gene sets, we introduce sparsity constraint to the association sub-matrix. The average residue was
introduced to evaluate the row and column cluster numbers. This is part of the work "Liver Cancer Analysis via Orthogonal Sparse Non-Negative Matrix Tri-
Factorization" which will be submitted to BBRC.

Authors@R: c(person("Naz","Gul", role = c("aut", "cre"),
Description: Function for growing survival trees ensemble ('Naz Gul', 'Nosheen Faiz', 'Dan Brawn', 'Rafal Kulakowski', 'Zardad Khan', and 'Berthold Lausen' (2020) <arXiv:2005.09043>) is given.
The trees are grown by the method of random survival forest ('Marvin Wright', 'Andreas Ziegler' (2017) <doi:10.18637/jss.v077.i01>).
The survival trees grown are assessed for both individual and collective performances.
The ensemble can give promising results on fewer survival trees selected in the final ensemble.
Imports: ranger, pec, stats, survival, prodlim

Title: Over Sampling for Time Series Classification
Description: Oversampling of imbalanced univariate time series classification data
using integrated ESPO and ADASYN methods. Enhanced Structure Preserving Oversampling
(ESPO) is used to generate a large percentage of the synthetic minority samples
from univariate labeled time series under the modeling assumption that the predictors
are Gaussian. ESPO estimates the covariance structure of the minority-class samples
and applies a spectral filer to reduce noise. Adaptive Synthetic (ADASYN) sampling
approach is a nearest neighbor interpolation approach which is subsequently applied
to the ESPO samples. This code is ported from a 'MATLAB' implementation by Cao et al.
<doi:10.1109/TKDE.2013.37> and adapted for use with Recurrent Neural Networks
Imports: fields, MASS, stats, utils, parallel, doParallel, doSNOW,
Suggests: knitr, rmarkdown, keras, dummies, rlist, pROC, devtools,