Suggests: knitr, rmarkdown, testthat, broom

comment = "adapted parts of the code to use dplyr 0.8.0"),
comment = "simplified parts of the code")
Romain Francois [ctb] (adapted parts of the code to use dplyr 0.8.0),
Lionel Henry [ctb] (simplified parts of the code)

comment = "simplified parts of the code"),
comment = "sent us different suggestions and bug reports")
Lionel Henry [ctb] (simplified parts of the code),
Hrisyana Doytchinova [rev] (sent us different suggestions and bug

comment = "improved the double demeaning function"),
Sarah Johnson [ctb] (improved the double demeaning function),

Title: Gene Registration from Expression and Time-Courses in R
Description: A tool for registering (aligning) gene expression profiles
between two species (reference data and data to transform).
Imports: cli, cowplot, data.table, dplyr, ggplot2, ggpubr, ggrepel,
magrittr, rlang, Rtsne, scales, stats, stringr, viridis

Imports: cli, data.table, dplyr, ggplot2, optimization, magrittr,

Title: Classification of Spatial Patterns from Environmental Data
Authors@R: c(person("Wencheng", "Lau-Medrano", role = c("aut", "cre"), email = "luis.laum@gmail.com"))
Description: Provides algorithms for detection of spatial patterns from oceanographic data using image processing methods based on Gradient Recognition.
Packaged: 2017-07-09 17:52:26 UTC; NvTomus InDemonic

Depends: R (>= 3.2.0), imagine (>= 1.3.1), raster
Packaged: 2017-07-29 04:30:43 UTC; NvTomus InDemonic

Title: GRadient-Based RECognition of Spatial Patterns in Environmental
Packaged: 2017-08-13 21:27:45 UTC; NvTomus InDemonic

Depends: R (>= 3.2.0), imagine (>= 1.5.2), raster

Title: Gradient-Based Recognition of Spatial Patterns in Environmental

Title: Clustering and Model Selection with the Integrated
Description: An ensemble of algorithms that enable the clustering of networks and data matrix such as document/term matrix with different type of generative models. Model selection and clustering is performed in combination by optimizing the Integrated Classification Likelihood (which is equivalent to minimizing the description length). Several models are available such as: Stochastic Block Model, degree corrected Stochastic Block Model, Mixtures of Multinomial, Latent Block Model. The optimization is performed thanks to a combination of greedy local search and a genetic algorithm (see <arXiv:2002:11577> for more details).
Imports: Rcpp (>= 1.0.0), Matrix, future, listenv, ggplot2, graphics,
Suggests: testthat, knitr, rmarkdown, igraph, dplyr, tibble, tidyr,
Collate: 'RcppExports.R' 'misc.R' 'models_classes.R' 'fit_classes.R'
'dcsbm.R' 'coclust_dcsbm.R' 'data.R' 'generator.R' 'mvmreg.R'
'gmm.R' 'greed.R' 'sbm.R' 'misssbm.R' 'mm.R' 'multistart_alg.R'