Imports: cluster, mclust, flexclust, fpc, plyr, dplyr, doParallel,
foreach, igraph, compiler, stats, parallel, grid, ggplot2,
gridExtra, intergraph, GGally, network, sna

Title: Simulation and Comparison of Dissolution Profiles
Description: Compare dissolution profiles with confidence interval of similarity
factor f2 using bootstrap methodology as described in the literature, such as
Efron and Tibshirani (1993, ISBN:9780412042317), Davison and Hinkley (1997,
The package can also be used to simulate dissolution profiles based on
mathematical modelling and multivariate normal distribution.
Imports: ggplot2, minpack.lm, MASS, readxl, stats

Title: Use multiple feature selection algorithms to derive robust
feature sets for two class classification problems.
Description: To select a highly discriminative set of features
classifying two groups of samples, multiple classification and
feature selection algorithms are utilised. By combining the
results of all methods and selecting features in a
bootstrapping approach, a robust set of discriminative features
Depends: lhs, tgp, mlegp, penalizedSVM, Boruta, pamr, gplots,

Description: To select a set of features for classification of two
groups of samples, multiple classification and feature
selection algorithms are utilised. By combining the results of
all methods and applying a bootstrapping approach a robust set
of features with high power to distinguish the two groups is

Description: To select a set of features for classification of two groups of samples, multiple classification and feature selection algorithms are utilised. By combining the results of all methods and applying a bootstrapping approach a robust set of features with high power to distinguish the two groups is selected.
Imports: igraph, ROCR, gbm, pROC, colorRamps, gplots, gtools, pamr,
randomForest, Boruta, caret, tgp, mlegp, penalizedSVM

feature sets for two or multiclass classification problems.
Description: To select a set of features used for successful classification of two or more groups of samples, multiple classification and feature selection algorithms are utilised. By combining the results of all methods and applying a bootstrapping approach a robust set of features with high power to distinguish the sample groups is selected.
Imports: igraph, ROCR, gbm, colorRamps, gplots, gtools, pamr,