ks

R package ks Kernel Smoothing: Kernel smoothers for univariate and multivariate data, including density functions, density derivatives, cumulative distributions, modal clustering, discriminant analysis, significant modal regions and two-sample hypothesis testing.

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References in zbMATH (referenced in 42 articles , 1 standard article )

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  1. Tristan Mary-Huard, Sarmistha Das, Indranil Mukhopadhyay, Stéphane Robin: Querying multiple sets of p-values through composed hypothesis testing (2021) arXiv
  2. Zhu, Kailun; Kurowicka, Dorota; Nane, Gabriela F.: Simplified R-vine based forward regression (2021)
  3. Arsalane Chouaib Guidoum: Kernel Estimator and Bandwidth Selection for Density and its Derivatives: The kedd Package (2020) arXiv
  4. Arsalane Chouaib Guidoum, Kamal Boukhetala: Performing Parallel Monte Carlo and Moment Equations Methods for Ito and Stratonovich Stochastic Differential Systems: R Package Sim.DiffProc (2020) not zbMATH
  5. Borrajo, M. I.; González-Manteiga, W.; Martínez-Miranda, M. D.: Bootstrapping kernel intensity estimation for inhomogeneous point processes with spatial covariates (2020)
  6. Casa, Alessandro; Chacón, José E.; Menardi, Giovanna: Modal clustering asymptotics with applications to bandwidth selection (2020)
  7. Díaz-Coto, Susana; Martínez-Camblor, Pablo; Pérez-Fernández, Sonia: SmoothROCtime: an (\mathsfR) package for time-dependent ROC curve estimation (2020)
  8. Kandanaarachchi, Sevvandi; Muñoz, Mario A.; Hyndman, Rob J.; Smith-Miles, Kate: On normalization and algorithm selection for unsupervised outlier detection (2020)
  9. Liu, Yu; De Brabanter, Kris: Smoothed nonparametric derivative estimation using weighted difference quotients (2020)
  10. Jonas Moss, Martin Tveten: kdensity: An R package for kernel density estimation with parametric starts and asymmetric kernels (2019) not zbMATH
  11. Mickaël Binois and Victor Picheny: GPareto: An R Package for Gaussian-Process-Based Multi-Objective Optimization and Analysis (2019) not zbMATH
  12. Teter, Michael D.; Royset, Johannes O.; Newman, Alexandra M.: Modeling uncertainty of expert elicitation for use in risk-based optimization (2019)
  13. Wang, Wei; Lin, Nan; Tang, Xiang: Robust two-sample test of high-dimensional mean vectors under dependence (2019)
  14. Schellhase, Christian; Spanhel, Fabian: Estimating non-simplified vine copulas using penalized splines (2018)
  15. Yang Hu; Carl Scarrott: evmix: An R package for Extreme Value Mixture Modeling, Threshold Estimation and Boundary Corrected Kernel Density Estimation (2018) not zbMATH
  16. Bee, Marco; Benedetti, Roberto; Espa, Giuseppe: Approximate maximum likelihood estimation of the Bingham distribution (2017)
  17. Bonnéry, Daniel; Breidt, F. Jay; Coquet, François: Kernel estimation for a superpopulation probability density function under informative selection (2017)
  18. Eichner, Gerrit: Kader -- an R package for nonparametric kernel adjusted density estimation and regression (2017)
  19. Gramacki, Artur; Gramacki, Jarosław: FFT-based fast bandwidth selector for multivariate kernel density estimation (2017)
  20. Kraus, Daniel; Czado, Claudia: D-vine copula based quantile regression (2017)

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