ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R
By Tarn Duong. 2007
646 citations recorded by the source corpus.
Kernel smoothing is one of the most widely used non-parametric data smoothing techniques. We introduce a new R package ks for multivariate kernel smoothing. Currently it contains functionality for kernel density estimation and kernel discriminant analysis. It is a comprehensive package for bandwidth matrix selection, implementing a wide range of data-driven diagonal and unconstrained bandwidth selectors.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/473a9239ade9085ad5fd0206fcbb22d17f5571c7
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In Artificial Intelligence
- Zuguang Gu
- S. Béthaz
- W. Smith
- G. Ballard
- Dedi Liu
- José Alquicira-Hernández
- Randolph M. Jones
- M. Heupel
In Statistics and Probability
- S. Béthaz
- W. Smith
- G. Ballard
- Dedi Liu
- José Alquicira-Hernández
- Randolph M. Jones
- M. Heupel
- A. Tobin
Citations in the corpus
- Uncertainty Analysis of Bivariate Design Flood Estimation and its Impacts on Reservoir Routing (2018, methodology): “The ‘ks’ package has advanced bandwidth selector function, and it can be used for kernel density estimation for multivariate data up to 6-dimensions (Duong 2007; Dung et al. 2015).”
- Measuring the biodiversity of microbial communities by flow cytometry (2016, methodology): “10.4) (Duong 2007).”
- Residency patterns and movements of grey reef sharks (Carcharhinus amblyrhynchos) in semi-isolated coral reef habitats (2015, methodology): “This facilitated calculation of a vertical two-dimensional kernel utilization distribution (vKUD) with the “ks” package (Duong 2007) using a plugin bandwidth selector in R (R Development Core Team 2013).”
- Hormones, parasites and male mating tactics in Alpine chamois: identifying the mechanisms of life history trade-offs (2012, methodology): “Using the ‘ks’ library (Duong 2007) we kernelsmoothed the data for each individual track with the plug-in bandwidth selector (‘hpi’) developed by Wand & Jones (1995).”
- Trophic cascades in the western Ross Sea, Antarctica: revisited (2015, methodology): “D kernel estimator using the ‘ks’ package (Duong 2013) in R. Kernels were smoothed using the default bandwidth selector (Gitzen et al. 2006, Duong 2007).”
- Copulas‐based risk analysis for inter‐seasonal combinations of wet and dry conditions under a changing climate (2018, background): “, 2001), kernel density estimators (Kim et al., 2006; Duong, 2007; Fang et al., 2018) and copula functions (Xu et al.”
- Seismic fragility curves for structures using non-parametric representations (2017, neutral): “Using the cross-validation estimation implemented in R [67], these are determined as h = 0 .”
- Nebulosa recovers single cell gene expression signals by kernel density estimation (2020, methodology): “Nebulosa makes use of weighted kernel density estimation methods (Duong, 2007; Venables and Ripley, 2002) to represent the expression of gene features from cell neighbours.”
- What Is the Proper Methodto Delineate Home Range of anAnimal Using Today’s AdvancedGPS Telemetry Systems: The Initial Step (2011, neutral): “Here, we restricted our analyses to use of adehabitat (Calenge 2006) and ks (Duong 2007) R packages for KDE and package BBMM (Nielson et al., 2011) to calculate BBMM home range.”
- Make Interactive Complex Heatmaps in R (2021, methodology): “To facilitate users to easier interprate two-dimensional distributions, InteractiveComplexHeatmap provides a general-purpose function interactivateDensity2D() that can be applied to two numeric vectors where the density is estimated by the package ks (Duong 2007).”