The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

HiClimR 2.2.1

• Updated package website
• Updated package DESCRIPTION and README
• Updated package dependencies and WORDLIST
• Style and format Fortran code

HiClimR 2.2.0

• Fixed NOTE: Found (possibly) invalid URLs

HiClimR 2.1.9

• Updated citation in package DESCRIPTION
• Updated NAMESPACE and documentation
• Fixed spelling errors
• Updated lifecycle URL in the README

HiClimR 2.1.8

• Code cleanup and formatting
• Removed HISTORY comments from source code
• Replaced 1:n expressions with seq_len(n)
• Updated citation, manual, and user information
• Updated documents after code formatting
• Updated package DESCRIPTION and added reference DOI
• Updated package URL: https://hsbadr.github.io/HiClimR/
• README: Updated README.md and added NEWS.md

HiClimR 2.1.7

• Updated package DESCRIPTION and author information
• Updated copyright year to 2021
• README: Added Markdown badges
• README: Added Digital Object Identifier (DOI) badge
• README: Linked version and download badges to CRAN
• README: Updated URLs

HiClimR 2.1.6

• README: Added CRAN downloads badge
• R: Fix non-informative failure for unsupported input of a vector

HiClimR 2.1.5

• R: Use inherits() to check class inheritance

HiClimR 2.1.4

• Added vignette for HiClimR Bug Reporting
• HiClimR2nc: Updated documentation and examples
• man: Use \code{} instead of \bold{} for classes

HiClimR 2.1.3

• Fixed spelling errors and allowed custom words
• HiClimR2nc: Fixed timeseries variable definition
• README: Link HiClimR to CRAN package page

HiClimR 2.1.2

• Fixed example ERROR in CRAN checks
• Added example to export NetCDF-4 file
• Updated dependencies and suggested packages

HiClimR 2.1.1

• fastCor: Fixed row/col names of the correlation matrix
• fastCor: Cleaned up zero-variance data check
• Examples: Minor comment update

HiClimR 2.1.0

• Supported contiguity constraint based on geographic distance
• Exporting region map and mean timeseries into NetCDF-4 file
• Replaced multi-variate with multivariate
• Renamed weightedVar to weightMVC
• Updated citation information
• Updated and cleaned up package DESCRIPTION
• Updated and cleaned up README

HiClimR 2.0.0

• Fixed NOTE: Registering native routines
• fastCor: Removed zero-variance data
• fastCor: Introduced optBLAS
• fastCor: Code cleanup
• Reformatted R source code
• Updated and fixed the examples
• Updated CRU TS dataset citation
• Updated README and all URLs

HiClimR 1.2.3

• Fixed geogMask confusing country codes/names
• Fixed geogMask filtering InDispute areas
• Corrected data construction in the user manual
  • x should be created using as.vector(t(x0))
  • x0 is the n by m original data matrix
  • n = length(unique(lon)) and m = length(unique(lat))
• coarseR now returns the original row numbers
• Minor README corrections and updates

HiClimR 1.2.2

• Changes for Undefined global functions
• Checking geographic masking output
• Minor README corrections and updates

HiClimR 1.2.1

• Updating variance for multivariate clustering
• More plotting options (pch and cex)
• geogMask supports ungridded data
• Updated user manual with the following notes:
  • longitudes takes values from -180 to 180 (not 0 to 360)
  • for gridded data, the rows of input data matrix for each variable is ordered by longitudes
    • check rownames(TestCase$x) for example!
      • each row represents a station (grid point)
      • row name is in the form of longitude,latitude
• Minor verbose fixes and updates
• Minor README corrections and updates
• Citation updated: technical paper has been published

HiClimR 1.2.0

• Multivariate clustering (MVC)
  • the input matrix x can now be a list of matrices (one matrix for each variable)
    • length(x) = nvars where nvars is the number of variables
    • number of rows N = number of objects (e.g., stations) to be clustered
    • number of columns M may vary for each variables
      • e.g., different temporal periods or record lengths
  • Each variable is separately preprocessed to allow for all possible options
    • preprocessing is specified by lists with length of nvars (number of variables)
      • length(meanThresh) = length(x) = nvars
      • length(varThresh) = length(x) = nvars
      • length(detrend) = length(x) = nvars
      • length(standardize) = length(x) = nvars
      • length(weightMVC) = length(x) = nvars
    • filtering with meanThresh and varThresh thresholds
    • detrending with detrend option, if requested
    • standardization with standardize option, if requested
      • strongly recommended since variables may have different magnitudes
        
    • weighting by the new weightMVC option (default is 1)
    • combining variables by column (for each object: spatial points or stations)
    • applying PCA (if requested) and computing the correlation/dissimilarity matrix
• Preliminary big data support
  • function fastCor can now split the data matrix into nSplit splits
  • adds a logical parameter upperTri to fastCor function
    • computes only the upper-triangular half of the correlation/dissimilarity matrix
    • it includes all required information since the correlation/dissimilarity matrix is symmetric
    • this almost halves memory use, which can be very important for big data.
  • fixes “integer overflow” for very large number of objects to be clustered
• Adds a logical parameter verbose for printing processing information
• Adds a logical parameter dendrogram for plotting dendrogram
• Uses \dontrun{} to skip time-consuming examples
  • for more examples: https://github.com/hsbadr/HiClimR#examples
• Backward compatibility with previous versions
• The user manual is updated and revised

HiClimR 1.1.6

• Setting minimum k = 2, for objective tree cutting
  • this addresses an issue caused by undefined k = NULL in validClimR function
  • when all inter-cluster correlations are significant at the user-specified significance level
• Code reformatting using formatR
• Package description and URLs have been revised
• Source code is now maintained on GitHub by authors

HiClimR 1.1.5

• Updating description, URL, and citation info

HiClimR 1.1.4

• Addresses an issue for zero-length mask vector: Error in -mask : invalid argument to unary operator
  • this error was introduced in v1.1.2+ after fixing the data-mean bug

HiClimR 1.1.3

• The user manual is revised
• lonSkip and latSkip renamed to lonStep and latStep, respectively
• Minor bug fixes

HiClimR 1.1.2

• A bug has been fixed where data mean is added to centered data if standardize = FALSE
  • objective tree cut and data component are now corrected
    • to match input parameters especially when clustering of raw data
    • centered data was used in previous versions

HiClimR 1.1.1

• Minor bug fixes and memory optimizations especially for the geographic masking function geogMask
• The limit for data size has been removed (use with caution)
• A logical parameter InDispute is added to geogMask function to optionally consider areas in dispute for geographic masking by country

HiClimR 1.1.0

• Code cleanup and bug fixes
• An issue with fastCor function that degrades its performance on 32-bit machines has been fixed
  • A significant performance improvement can only be achieved when building R on 64-bit machines with an optimized BLAS library, such as ATLAS, OpenBLAS, or the commercial Intel MKL
• The citation info has been updated to reflect the current status of the technical paper

HiClimR 1.0.9

• Minor changes and fixes for CRAN
• For memory considerations,
  • smaller test case with 1 degree resolution instead of 0.5 degree
  • the resolution option (res parameter) in geographic masking is removed
  • Mask data is only available in 0.1 degree (~10 km) resolution
• LazyLoad and LazyData are enabled in the description file
• The worldMask and TestCase data are converted to lists to avoid conflicts of variable names (lon, lat, info, and mask) with lazy loading

HiClimR 1.0.8

• Code cleanup and bug fixes
• Region maps are unified for both gridded and ungridded data

HiClimR 1.0.7

• Hybrid hierarchical clustering feature that utilizes the pros of the available methods
  • especially the better overall homogeneity in Ward’s method and the separation and objective tree cut of the regional linkage method.
  • The logical parameter hybrid is added to enable a second clustering step
    • using regional linkage for reconstructing the upper part of the tree at a cut
    • defined by kH (number of clusters to restart with using the regional linkage method)
    • If kH = NULL, the tree will be reconstructed for the upper part with the first merging cost larger than the mean merging cost for the entire tree
      • merging cost is the loss of overall homogeneity at each merging step
• If hybrid clustering is requested, the updated upper-part of the tree will be used for cluster validation.

HiClimR 1.0.6

• Code cleanup and bug fixes

HiClimR 1.0.5

• Code cleanup and bug fixes
• Adds support to generate region maps for ungridded data

HiClimR 1.0.4

• Code cleanup and bug fixes
• The coarseR function is called inside the core HiClimR function
• Adds coords component to the output tree for the longitude and latitude coordinates
  • they may be changed by coarsening
• validClimR function does not require lon and lat arguments
  • they are now available in the output tree (coords component)

HiClimR 1.0.3

• Code cleanup and bug fixes
• One main/wrapper function HiClimR internally calls all other functions
• Unified component names for all functions
• Objective tree cut is supported only for the regional linkage method
  • Otherwise, the number of clusters k should be specified
• The new clustering method has been renamed from HiClimR to regional linkage method

HiClimR 1.0.2

• Code cleanup and bug fixes.
• adds a new feature that to return the preprocessed data used for clustering, by a logical argument retData.
  • the data will be returned in a componentdata of the output tree
  • this can be used to utilize HiCLimR preprocessing options for further analysis
• Ordered regions vector for the selected number of clusters are now returned in the region component
  • length equals the number of spatial elements N

HiClimR 1.0.1

• Code cleanup and bug fixes
• Adds a new feature in validCLimR that enables users to exclude very small clusters from validation indices interCor, intraCor, diffCor, and statSum, by setting a value for the minimum cluster size (minSize > 1)
  • the excluded clusters can be identified from the output of validClimR in clustFlag component, which takes a value of 1 for valid clusters or 0 for excluded clusters
  • in HiClimR (currently, regional linkage) method, noisy spatial elements (or stations) are isolated in very small-size clusters or individuals since they do not correlate well with any other elements
  • this should be followed by a quality control step
• Adds coarseR function for coarsening spatial resolution of the input matrix x

HiClimR 1.0.0

• Initial version of HiClimR package that modifies hclust function in stats library
• Adds a new clustering method to the set of available methods
• The new method is explained in the context of a spatiotemporal problem, in which N spatial elements (e.g., stations) are divided into k regions, given that each element has observations (or timeseries) of length M
  • minimizes the inter-regional correlation between region means
  • modifies average update formulae by incorporating the standard deviation of the mean of the merged region
  • a function of the correlation between the individual regions, and their standard deviations before merging
  • equals the average of their standard deviations if and only if the correlation between the two merged regions is 100%.
  • in this special case, the new method is reduced to the classic average linkage clustering method
• Several features are included to facilitate spatiotemporal analysis applications:
  • options for preprocessing and postprocessing
  • efficient code execution for large datasets.
  • cluster validation function validClimR
  • implements an objective tree cut to find an optimal number of clusters
• Applicable to any correlation-based clustering

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.