{"Package":"KernelKnn","Type":"Package","Title":"Kernel k Nearest Neighbors","Version":"1.1.5","Date":"2023-01-06","Authors@R":"c( person(given = \"Lampros\", family = \"Mouselimis\", email = \"mouselimislampros@gmail.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"https://orcid.org/0000-0002-8024-1546\")), person(given = \"Matthew\", family = \"Parks\", email = \"parksmatthewm@gmail.com\", role = \"ctb\", comment = c(Github = \"Github Contributor\")) )","BugReports":"https://github.com/mlampros/KernelKnn/issues","URL":"https://github.com/mlampros/KernelKnn","Description":"Extends the simple k-nearest neighbors algorithm by incorporating numerous kernel functions and a variety of distance metrics. The package takes advantage of 'RcppArmadillo' to speed up the calculation of distances between observations.","License":"MIT + file LICENSE","SystemRequirements":"libarmadillo: apt-get install -y libarmadillo-dev\n(deb), libblas: apt-get install -y libblas-dev (deb),\nliblapack: apt-get install -y liblapack-dev (deb),\nlibarpack++2: apt-get install -y libarpack++2-dev (deb),\ngfortran: apt-get install -y gfortran (deb)","LazyData":"TRUE","Encoding":"UTF-8","Depends":"R(>= 2.10.0)","Imports":"Rcpp (>= 0.12.5)","LinkingTo":"Rcpp, RcppArmadillo","Suggests":"testthat, covr, knitr, rmarkdown","VignetteBuilder":"knitr","RoxygenNote":"7.2.3","NeedsCompilation":"yes","Packaged":"2023-01-06 06:46:13 UTC; lampros","Author":"Lampros Mouselimis [aut, cre] (),\nMatthew Parks [ctb] (Github Contributor)","Maintainer":"Lampros Mouselimis ","Repository":"CRAN","Date/Publication":"2023-01-06 18:51:00 UTC","repoType":"cran","tarballUrl":"ftp://cran.r-project.org/pub/R/src/contrib/KernelKnn_1.1.5.tar.gz","jsonAuthors":[{"name":"Lampros Mouselimis","email":"mouselimislampros@gmail.com","maintainer":true},{"name":"Matthew Parks","email":"parksmatthewm@gmail.com","maintainer":false}],"readme":"\n\n## KernelKnn\n
\n\nThe KernelKnn package extends the simple k-nearest neighbors algorithm by incorporating numerous kernel functions and a variety of distance metrics. The package takes advantage of 'RcppArmadillo' to speed up the calculation of distances between observations. More details on the functionality of KernelKnn can be found in the [blog-post](http://mlampros.github.io/2016/07/10/KernelKnn/) and in the package Vignettes ( *scroll down for information on how to use the* **docker image** ).\n

\n\nTo install the package from CRAN use, \n\n```R\n\ninstall.packages(\"KernelKnn\")\n\n\n```\n
\n\nand to download the latest version from Github use the *install_github* function of the devtools package,\n

\n\n```R\n\ndevtools::install_github('mlampros/KernelKnn')\n\n\n```\n
\n\nUse the following link to report bugs/issues,\n

\n\n[https://github.com/mlampros/KernelKnn/issues](https://github.com/mlampros/KernelKnn/issues)\n\n\n
\n\n**UPDATE 29-11-2019**\n\n
\n\n**Docker images** of the *KernelKnn* package are available to download from my [dockerhub](https://hub.docker.com/r/mlampros/kernelknn) account. The images come with *Rstudio* and the *R-development* version (latest) installed. The whole process was tested on Ubuntu 18.04. To **pull** & **run** the image do the following,\n\n
\n\n```R\n\ndocker pull mlampros/kernelknn:rstudiodev\n\ndocker run -d --name rstudio_dev -e USER=rstudio -e PASSWORD=give_here_your_password --rm -p 8787:8787 mlampros/kernelknn:rstudiodev\n\n```\n\n
\n\nThe user can also **bind** a home directory / folder to the image to use its files by specifying the **-v** command,\n\n
\n\n```R\n\ndocker run -d --name rstudio_dev -e USER=rstudio -e PASSWORD=give_here_your_password --rm -p 8787:8787 -v /home/YOUR_DIR:/home/rstudio/YOUR_DIR mlampros/kernelknn:rstudiodev\n\n\n```\n\n
\n\nIn the latter case you might have first give permission privileges for write access to **YOUR_DIR** directory (not necessarily) using,\n\n
\n\n```R\n\nchmod -R 777 /home/YOUR_DIR\n\n\n```\n\n
\n\nThe **USER** defaults to *rstudio* but you have to give your **PASSWORD** of preference (see [https://rocker-project.org](https://rocker-project.org/) for more information).\n\n
\n\nOpen your web-browser and depending where the docker image was *build / run* give, \n\n
\n\n**1st. Option** on your personal computer,\n\n
\n\n```R\nhttp://0.0.0.0:8787 \n\n```\n\n
\n\n**2nd. Option** on a cloud instance, \n\n
\n\n```R\nhttp://Public DNS:8787\n\n```\n\n
\n\nto access the Rstudio console in order to give your username and password.\n\n
\n\n### **Citation:**\n\nIf you use the **KernelKnn** R package in your paper or research please cite [https://CRAN.R-project.org/package=KernelKnn/citation.html](https://CRAN.R-project.org/package=KernelKnn/citation.html):\n\n
\n\n```R\n@Manual{,\n title = {{KernelKnn}: Kernel k Nearest Neighbors},\n author = {Lampros Mouselimis},\n year = {2021},\n note = {R package version 1.1.5},\n url = {https://CRAN.R-project.org/package=KernelKnn},\n}\n```\n\n
\n","jobInfo":{"package":"KernelKnn","version":"1.1.5","parsingStatus":"success","parserVersion":2,"parsedAt":"2023-01-06T19:30:34+0000"}}

Description

Usage

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