Imports: utils, glmx, grDevices, graphics, gtools, stats, MASS, Matrix,

Description: Quantitative Analysis of Complex Networks. This package
offers a set of topological network measures to analyze complex
Depends: graph, RBGL, igraph, combinat, Rgraphviz

Title: This package offers a set of topological network measures to

Depends: graph, RBGL, igraph (>= 0.5.5-1), combinat, Rgraphviz

Depends: graph, RBGL, igraph (>= 0.5.5-1), combinat, Rgraphviz, Rmpfr

Depends: graph, RBGL, igraph (>= 0.5.5-1), combinat, Rmpfr

Title: QuACN: Quantitative Analysis of Complex Networks
Author: Laurin Mueller, Michael Schutte, Karl Kugler, Matthias Dehmer

Depends: graph, RBGL, igraph0, combinat, Rmpfr

Author: Karl G. Kugler, Matthias Wieser,  Matthias Dehmer, Michael Schutte, Laurin A.J. Mueller
Maintainer: Karl G. Kugler <kg.kugler@gmail.com>
Description: Quantitative Analysis of Complex Networks. This package offers a set of topological network measures to analyze complex Networks structurally.
Packaged: 2014-11-19 13:47:45 UTC; karl.kugler

Author: Christian Hennig, Cinzia Viroli and Laura Anderlucci
Description: Various quantile-based clustering algorithms: algorithm CU (Common theta and Unscaled variables), algorithm CS (Common theta and Scaled variables through lambda_j), algorithm VU (Variable-wise theta_j and Unscaled variables) and algorithm VW (Variable-wise theta_j and Scaled variables through lambda_j). Hennig, Viroli, Anderlucci (2018) <arXiv:1806.10403v1>.

Description: Various quantile-based clustering algorithms: algorithm CU (Common theta and Unscaled variables), algorithm CS (Common theta and Scaled variables through lambda_j), algorithm VU (Variable-wise theta_j and Unscaled variables) and algorithm VW (Variable-wise theta_j and Scaled variables through lambda_j). Hennig, C., Viroli, C., Anderlucci, L. (2019) "Quantile-based clustering." Electronic Journal of Statistics. 13 (2) 4849 - 4883  <doi:10.1214/19-EJS1640>.

Title: Constructing Quantitative Environment Sensor using
Description: A method for prediction of environmental conditions based on
transcriptome data linked with the environmental gradients. This package
provides functions to overview gene-environment relationships, to construct
the prediction model, and to predict environmental conditions where the
transcriptomes were generated. This package can quest for candidate genes
for the model construction even in non-model organisms' transcriptomes