{"Package":"banter","Type":"Package","Title":"BioAcoustic EveNT ClassifiER","Description":"Create a hierarchical acoustic event species classifier out of\nmultiple call type detectors as described in Rankin et al (2017) .","Version":"0.9.3","Authors@R":"c(\nEA = person(\"Eric\", \"Archer\", email = \"eric.archer@noaa.gov\", role = c(\"aut\", \"cre\")),\nTS = person(\"Taiki\", \"Sakai\", email = \"taiki.sakai@noaa.gov\", role = c(\"aut\")))","Depends":"magrittr","Imports":"dplyr, ggplot2, gridExtra, methods, parallel, plyr,\nrandomForest, ranger, rfPermute, rlang, stats, tibble, tidyr","License":"GNU General Public License","RoxygenNote":"6.0.1","NeedsCompilation":"no","Packaged":"2018-07-09 15:02:20 UTC; ericarcher","Author":"Eric Archer [aut, cre],\nTaiki Sakai [aut]","Maintainer":"Eric Archer ","Repository":"CRAN","Date/Publication":"2018-07-10 15:20:06 UTC","repoType":"cran","tarballUrl":"ftp://cran.r-project.org/pub/R/src/contrib/banter_0.9.3.tar.gz","jsonAuthors":{"EA":{"name":"Eric Archer","email":"eric.archer@noaa.gov","maintainer":true},"TS":{"name":"Taiki Sakai","email":"taiki.sakai@noaa.gov","maintainer":false}},"readme":"# banter\n\n### Description\n\n*banter* is a package for creating hierarchical acoustic event classifiers out of multiple call type detectors.\n\n### Installation\n\nTo install the latest version from GitHub:\n\n```r\n# make sure you have Rtools installed\nif(!require('devtools')) install.packages('devtools')\n# install from GitHub\ndevtools::install_github('ericarcher/banter')\n```\n\n### Tutorial\n\nThe BANTER model is initialized with a data.frame of events. There is one row per event and it must have a column called `event.id` which is a unique id for each event, and a column called `species` which assigns each event to a given species. Every other column in the data.frame will be used as a predictor variable for the events. \nIn the package, an example data.frame is in the `train.data` example data list as the `$events` element.\n```r\ndata(train.data)\nbant.mdl <- initBanterModel(train.data$events)\n```\n\nNext, detector data is added to the initialized BANTER model object. Each detector is a data.frame with a column called `event.id` that associates the detected call with an event that the model was initialized with, and a `call.id` column that provides a unique identifier for each call. Every other column will be used as a predictor variable for the calls. \nIn the package, example data.frames for three detectors are provided in the `$detectors` element of the `train.data` example data list. Here is an example of adding the burst pulse (`bp`) detector.\n\n```r\nbant.mdl <- addBanterDetector(\n bant.mdl, \n data = train.data$detectors$bp, \n name = \"bp\",\n ntree = 10, \n sampsize = 1\n)\n```\n\nThe `addBanterDetector` function can be called repeatedly to add additional detectors. Alternatively, if the detectors are all in a named list, they can be added at once:\n\n```r\nbant.mdl <- addBanterDetector(\n bant.mdl, \n data = train.data$detectors, \n ntree = 10, \n sampsize = 1\n)\n```\n\nOnce all of the detectors have been added, then the full BANTER model is run:\n```r\nbant.mdl <- runBanterModel(bant.mdl, ntree = 5000, sampsize = 3)\n```\n\nThe model can be easily summarized:\n```r\nsummary(bant.mdl)\n```\n\nThe actual `randomForest` model can be extracted for the event or detector models:\n```r\n# extract event Random Forest model\nevent.rf <- getBanterModel(bant.mdl, \"event\")\n\n# extract burst pulse (bp) Random Forest model\nbp.rf <- getBanterModel(bant.mdl, \"bp\")\n```\n\nThese can then be visualized using other tools, such as those in the `rfPermute` package:\n```r\nlibrary(rfPermute)\nplotVotes(event.rf)\n```\n\nTo predict novel data, it must be in a list with the event data in the `$events` element, and the detector data in a named list called `$detectors`:\n```r\ndata(test.data)\npredict(bant.mdl, test.data)\n```\n\n### Contact\n\n* submit suggestions and bug-reports: \n* send a pull request: \n* e-mail: \n\n### Reference\nRankin, S., Archer, F., Keating, J. L., Oswald, J. N., Oswald, M., Curtis, A. and Barlow, J. (2017) Acoustic classification of dolphins in the California Current using whistles, echolocation clicks, and burst pulses. Mar Mam Sci, 33: 520-540. [doi:10.1111/mms.12381](https://onlinelibrary.wiley.com/doi/abs/10.1111/mms.12381)\n\n### version 0.9.2\n\n* Initial release","jobInfo":{"package":"banter","version":"0.9.3","parsingStatus":"success","parserVersion":1,"parsedAt":"2021-10-01T23:29:48+0000"}}

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