Package: waves 0.2.7.9000

Jenna Hershberger

waves: Vis-NIR Spectral Analysis Wrapper

Originally designed application in the context of resource-limited plant research and breeding programs, 'waves' provides an open-source solution to spectral data processing and model development by bringing useful packages together into a streamlined pipeline. This package is wrapper for functions related to the analysis of point visible and near-infrared reflectance measurements. It includes visualization, filtering, aggregation, preprocessing, cross-validation set formation, model training, and prediction functions to enable open-source association of spectral and reference data. This package is documented in a peer-reviewed manuscript in the Plant Phenome Journal <doi:10.1002/ppj2.20012>. Specialized cross-validation schemes are described in detail in Jarquín et al. (2017) <doi:10.3835/plantgenome2016.12.0130>. Example data is from Ikeogu et al. (2017) <doi:10.1371/journal.pone.0188918>.

Authors:Jenna Hershberger [aut, cre], Michael Gore [ths], NSF BREAD IOS-1543958 [fnd]

waves_0.2.7.9000.tar.gz
waves_0.2.7.9000.zip(r-4.7)waves_0.2.7.9000.zip(r-4.6)waves_0.2.7.9000.zip(r-4.5)
waves_0.2.7.9000.tgz(r-4.6-any)waves_0.2.7.9000.tgz(r-4.5-any)
waves_0.2.7.9000.tar.gz(r-4.7-any)waves_0.2.7.9000.tar.gz(r-4.6-any)
waves_0.2.7.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
waves/json (API)

# Install 'waves' in R:

install.packages('waves', repos = c('https://gorelab.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/gorelab/waves/issues

Pkgdown/docs site:https://gorelab.github.io

Datasets:

ikeogu.2017 - Example vis-NIRS and reference dataset

On CRAN:

6.91 score 8 stars 57 scripts 305 downloads 3 mentions 18 exports 136 dependencies

Last updated from:2cf4636fdb. Checks:9 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	OK	240
source / vignettes	OK	303
linux-release-x86_64	OK	250
macos-release-arm64	OK	156
macos-oldrel-arm64	OK	187
windows-devel	OK	167
windows-release	OK	158
windows-oldrel	OK	152
wasm-release	OK	172

Exports:aggregate_spectra AggregateSpectra DoPreprocessing filter_spectra FilterSpectra format_cv FormatCV plot_spectra PlotSpectra predict_spectra PredictFromSavedModel pretreat_spectra save_model SaveModel test_spectra TestModelPerformance train_spectra TrainSpectralModel

Dependencies:askpass base64enc baseline BiasedUrn bit bit64 bslib cachem caret class classInt cli clipr clock codetools cpp11 crayon data.table DBI diagram digest dplyr e1071 epiR evaluate farver fastmap flextable fontawesome fontBitstreamVera fontLiberation fontquiver foreach fs future future.apply gdtools generics ggplot2 globals glue gower gtable hardhat highr hms htmltools ipred isoband iterators jquerylib jsonlite KernSmooth knitr labeling lattice lava lifecycle listenv lubridate magrittr MASS mathjaxr Matrix memoise mime ModelMetrics nlme nnet numDeriv officer openssl pander parallelly pillar pkgconfig pls plyr prettyunits pROC prodlim progress progressr prospectr proxy purrr R6 ragg randomForest rappdirs RColorBrewer Rcpp RcppArmadillo readr recipes reshape2 rlang rmarkdown rpart s2 S7 sass scales sf shape signal SparseM sparsevctrs spectacles SQUAREM stringi stringr survival sys systemfonts textshaping tibble tidyr tidyselect timechange timeDate tinytex tzdb units utf8 uuid vctrs viridisLite vroom withr wk xfun xml2 yaml zip zoo

waves

Rendered fromwaves.Rmdusingknitr::rmarkdown

Last update: 2026-06-02
Started: 2022-02-03

Help page	Topics
Aggregate data based on grouping variables and a user-provided function	aggregate_spectra
Filter spectral data frame based on Mahalanobis distance	filter_spectra
Format multiple trials with or without overlapping genotypes into training and test sets according to user-provided cross validation scheme	format_cv
Example vis-NIRS and reference dataset	ikeogu.2017
Plot spectral data, highlighting outliers as identified using Mahalanobis distance	plot_spectra
Use provided model object to predict trait values with input dataset	predict_spectra
Pretreat spectral data according to user-designated method	pretreat_spectra
Save spectral prediction model and model performance statistics	save_model
Test the performance of spectral models	test_spectra
Train a model based predict reference values with spectral data	train_spectra

Package: waves 0.2.7.9000

waves: Vis-NIR Spectral Analysis Wrapper

Citation

Development and contributors

Readme and manuals

Help Manual

Usage by other packages (reverse dependencies)