CLUE is an R package for identifying optimal number of clusters in a given time-course dataset clustered by cmeans or kmeans algorithms.

CLUster Evaluation (CLUE) is a computational method for identifying optimal number of clusters in a given time-course dataset clustered by cmeans or kmeans algorithms and subsequently identify key kinases or pathways from each cluster. Its implementation in R is called ClueR. See Readme on <https://github.com/PengyiYang/ClueR> for more details.

CLUster Evaluation (CLUE) is a computational method for identifying
optimal number of clusters in a given time-course dataset clustered by
cmeans or kmeans algorithms and subsequently identify key kinases or
pathways from each cluster. Its implementation in R is called ClueR.
See README on <https://github.com/PengyiYang/ClueR> for more details.
P Yang et al. <10.1371/journal.pcbi.1004403>.

CLUster Evaluation (CLUE) is a computational method for identifying
optimal number of clusters in a given time-course dataset clustered by
cmeans or kmeans algorithms and subsequently identify key kinases or
pathways from each cluster. Its implementation in R is called ClueR.
See README on <https://github.com/PengyiYang/ClueR> for more details.
P Yang et al. (2015) <doi:10.1371/journal.pcbi.1004403>.

Clustering algorithms are designed to cluster objects into a number of clusters. Any clustering algorithm provides the 'best'
grouping of objects according to some criterion.
This does not guarantee a 'good' clustering solution in the sense that some allocations were not simply the result of chance.
This package allows the user to apply any clustering algorithm to a data set. The cluster allocations are subjected to a
bootstrap analysis
to determine the extent to which the clustering structure is stable and fundamental to the data set. For more information
see <https://slubbe.wixsite.com/academic-cv/conference-presentations>.

Simultaneous Semi-Parametric Estimation of Clustering and
Regression
c(person("Matthieu", "Marbac", role = c("aut", "cre", "cph"), email = "matthieu.marbac-lourdelle@ensai.fr"),
person("Mohammed", "Sedki", role = "aut"),
person("Christophe", "Biernacki", role = "aut"),
person("Vincent", "Vandewalle", role = "aut"))
Parameter estimation of regression models with fixed group effects, when the group variable is missing while group-related variables are available. Parametric and semi-parametric approaches described in Marbac et al. (2020) <arXiv:2012.14159> are implemented.
Rcpp, parallel, ALDqr, ald, quantreg, VGAM
Matthieu Marbac [aut, cre, cph],
Mohammed Sedki [aut],
Christophe Biernacki [aut],
Vincent Vandewalle [aut]
'cluspred.R' 'RcppExports.R' 'Singleblock_algo_NP.R'
'Singleblock_algo_Param.R' 'Singleblock_prediction.R' 'tool.R'
'TwoSteps_algo.R' 'Twosteps_computelogPDFwithZ.R'
'TwoSteps_Mstep.R'