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KernelKnn 1.1.5

• I added the order ‘p’ of the “minkowski” method as a new parameter to the ‘KernelKnn()’, ‘KernelKnnCV()’ and ‘knn.index.dist()’ functions. It defaults to ‘k’ (see https://github.com/mlampros/KernelKnn/issues/9)
• I added test cases for the ‘KernelKnn()’ and ‘knn.index.dist()’ functions

KernelKnn 1.1.4

• The pull request 7 fixed a bug in the checking of the Levels argument (see https://github.com/mlampros/KernelKnn/pull/7)
• I fixed an omission of the column names in case of classification in the KernelKnn() and distMat.KernelKnn() functions (see https://github.com/mlampros/KernelKnn/issues/8)

KernelKnn 1.1.3

• I updated the References section of the switch.ops() function in the utils.R file which explain how the combination of the kernels work
• I added an error case in all functions that make usage of the ‘Levels’ parameter if the ‘Levels’ do not match the unique ‘y’ labels
• I removed the distMat.KernelKnnCV() function (and the tests/test-dist_kernelknnCV.R file) because based on the current implementation of the distMat.KernelKnn() function the TEST_indices parameter must consist of the last indices of the input DIST_mat distance matrix and this is not the case if we run cross-validation (see issue 5)

KernelKnn 1.1.2

• I’ve fixed an error in the CITATION file

KernelKnn 1.1.1

• I’ve added the CITATION file in the inst directory

KernelKnn 1.1.0

• I fixed the “failure: the condition has length > 1” CRAN error which appeared mainly due to the misuse of the base class() function in multiple code snippets in the package (for more info on this matter see: https://developer.r-project.org/Blog/public/2019/11/09/when-you-think-class.-think-again/index.html)

KernelKnn 1.0.9

I added a test case to check equality of the results between KernelKnnCV and distMat.KernelKnnCV functions

KernelKnn 1.0.8

I added the DARMA_64BIT_WORD flag in the Makevars file to allow the package processing big datasets

KernelKnn 1.0.7

I modified the input_dist_mat function of the distance_metrics.cpp file due to a bug. I modified the distMat.KernelKnn function so that it does not return an error if the rows of the DIST_mat distance matrix is not equal to the length of y (added comments in the function documentation).

KernelKnn 1.0.6

In this version the following functions/parameters were added:

• seed_num : parameter in KernelKnnCV and distMat.KernelKnnCV cross-validation functions, which specifies the seed of R’s random number generator
• distMat.KernelKnn : this function performs kernel k-nearest-neighbor search by using a distance matrix as input
• distMat.knn.index.dist : this function returns the indices and distances for k-nearest neighbors using a distance matrix
• distMat.KernelKnnCV : this function performs cross-validated kernel k-nearest-neighbor search using a distance matrix as input

I also modified the OpenMP clauses of the .cpp file to address the ASAN errors.

KernelKnn 1.0.5

I removed OpenImageR and irlba as package dependencies. I also added an init.c file in the src folder due to a change in CRAN submissions for compiled code [ references : http://stackoverflow.com/questions/42313373/r-cmd-check-note-found-no-calls-to-r-registerroutines-r-usedynamicsymbols, https://github.com/RcppCore/Rcpp/issues/636 ]

KernelKnn 1.0.4

I added a try-catch Rcpp function to make possible the calculation of singular covariance matrices as sugggested in https://github.com/mlampros/KernelKnn/issues/1

KernelKnn 1.0.3

Reimplementation of the Rcpp function due to ASAN-memory-errors

KernelKnn 1.0.2

I updated the Description file with a URL and a BugReports web-address.

KernelKnn 1.0.1

Currently, Software platforms like OSX do not support openMP, thus I’ve made openMP optional for all cpp functions.

KernelKnn 1.0.0

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.