NEWS

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HiClimR 2.2.1 (2022-01-20)

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

HiClimR 2.2.0 (2021-05-31)

• Fixed NOTE: Found (possibly) invalid URLs

HiClimR 2.1.9 (2021-04-02)

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

HiClimR 2.1.8 (2021-01-05)

• 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 (2020-11-05)

• 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 (2020-02-22)

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

HiClimR 2.1.5 (2019-12-10)

• R: Use inherits() to check class inheritance

HiClimR 2.1.4 (2019-01-20)

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

HiClimR 2.1.3 (2019-01-11)

• 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 (2019-01-03)

• 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 (2015-08-06)

• 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 (2015-07-22)

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

HiClimR 1.2.1 (2015-05-24)

• 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 (2015-03-27)

• 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 (2015-03-02)

• 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 (2014-11-13)

• Updating description, URL, and citation info

HiClimR 1.1.4 (2014-09-02)

• 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 (2014-08-28)

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

HiClimR 1.1.2 (2014-07-27)

• 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 (2014-07-14)

• 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 (2014-05-16)

• 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 (2014-05-07)

• 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