A tool for Hierarchical Climate Regionalization applicable to any correlation-based clustering.
It adds several features and a new clustering method (called, 'regional' linkage) to hierarchical
clustering in R ('hclust' function in 'stats' library): data regridding, coarsening spatial resolution,
geographic masking, contiguity-constrained clustering, data filtering by mean and/or variance
thresholds, data preprocessing (detrending, standardization, and PCA), faster correlation function
with preliminary big data support, different clustering methods, hybrid hierarchical clustering,
multivariate clustering (MVC), cluster validation, visualization of regionalization results, and
exporting region map and mean timeseries into NetCDF-4 file.
The technical details are described in Badr et al. (2015) <doi:10.1007/s12145-015-0221-7>.
covr, devtools, knitr, rmarkdown, roxygen2, spelling,
testthat

This package allows you to detect domains in HiC data by rephrasing this problem as a two-dimensional segmentation issue.

Antonio Pedro Duarte Silva <psilva@porto.ucp.pt>
Performs Linear Discriminant Analysis in High Dimensional
problems based on covariance estimators derived from low
dimensional factor models. Includes routines for classifier
training, prediction, cross-validation and variable selection.

Performs Linear Discriminant Analysis in High Dimensional
problems based on reliable covariance estimators for problems
with(many) more variables than observations. Includes routines
for classifier training, prediction, cross-validation and
variable selection.

Performs Linear Discriminant Analysis in High Dimensional
problems based on reliable covariance estimators for problems
with (many) more variables than observations. Includes routines
for classifier training, prediction, cross-validation and
variable selection.

Performs linear discriminant analysis in high dimensional
problems based on reliable covariance estimators for problems
with (many) more variables than observations. Includes routines
for classifier training, prediction, cross-validation and
variable selection.