Title: Bayesian bandwidth estimation for functional kernel regression
Maintainer: Han Lin Shang <H.Shang@soton.ac.uk>
Description: Estimate optimal bandwidth for the regression mean function approximated by the functional Nadaraya-Watson estimator and the error density approximated by a kernel density of residuals
Packaged: 2013-10-21 13:24:57 UTC; hanlinshang

Title: Bayesian bandwidth estimation for the functional kernel
Description: Estimate optimal bandwidths for the regression mean function approximated by the functional Nadaraya-Watson estimator and the error density approximated by a kernel density of residuals simultaneously in a scalar-on-function regression
Packaged: 2013-11-24 16:39:25 UTC; hanlinshang

Packaged: 2013-12-16 21:49:53 UTC; hanlinshang

Title: Bayesian bandwidth estimation and semi-metric selection for the
functional kernel regression with unknown error density
Maintainer: Han Lin Shang <hanlin.shang@anu.edu.au>
Description: Estimating optimal bandwidths for the regression mean function approximated by the functional Nadaraya-Watson estimator and the error density approximated by a kernel density of residuals simultaneously in a scalar-on-function regression. As a by-product of Markov chain Monte Carlo, the optimal choice of semi-metric is selected based on largest marginal likelihood.
Packaged: 2014-03-18 07:54:52 UTC; hanlinshang

Packaged: 2014-04-28 22:07:42 UTC; hanlinshang

Title: Bayesian bandwidth estimation for multivariate kernel regression
Maintainer: Han Lin Shang <HanLin.Shang@monash.edu.au>
Description: Bayesian bandwidth estimation for Nadaraya-Watson type

Maintainer: Han Lin Shang <HanLin.Shang@monash.edu>
multivariate kernel regression with Gaussian error

multivariate kernel regression with Gaussian error density

Packaged: 2013-12-16 18:26:34 UTC; hanlinshang

Packaged: 2014-04-05 05:03:37 UTC; hanlinshang

Authors@R: person(given = "Yuchen", family = "Li", email = "liyuchen_1995@outlook.com", role = c("aut", "cre"))
Description: A fast and intuitive batch effect removal tool for single-cell data. BBKNN is originally used in the 'scanpy' python package, and now can be used with 'Seurat' seamlessly.
Imports: dplyr, glmnet, Matrix, Rcpp, RcppAnnoy, reticulate, Rtsne,
Suggests: knitr, rmarkdown, testthat (>= 3.0.0), patchwork