Bayesian bandwidth estimation and semi-metric selection for the
functional kernel regression with unknown error density
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.

Bayesian bandwidth estimation for multivariate kernel regression
Bayesian bandwidth estimation for Nadaraya-Watson type
multivariate kernel regression

Bayesian bandwidth estimation for multivariate kernel regression
with Gaussian error
Bayesian bandwidth estimation for Nadaraya-Watson type
multivariate kernel regression with Gaussian error

Bayesian bandwidth estimation for Nadaraya-Watson type
multivariate kernel regression with Gaussian error density

person(given = "Yuchen", family = "Li", email = "liyuchen_1995@outlook.com", role = c("aut", "cre"))
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.
dplyr, glmnet, Matrix, Rcpp, RcppAnnoy, reticulate, Rtsne,
Seurat, SeuratObject, tidytable, uwot
knitr, rmarkdown, testthat (>= 3.0.0), patchwork

dplyr, glmnet, Matrix, Rcpp, RcppAnnoy, reticulate, Rtsne,
Seurat, SeuratObject, tidytable, uwot (>= 0.1.14)

c(person("Jun","Woo",role=c("aut","cre"),
email="jwoo@umn.edu",
comment=c(ORCID="0000-0003-3220-2064")),
person("Jinhua","Wang", role=c("ctb")))
methods, stats, utils, Rcpp (>= 0.12.16), pROC
Supervised learning using Boltzmann Bayes model inference,
which extends naive Bayes model to include interactions. Enables
classification of data into multiple response groups based on a large
number of discrete predictors that can take factor values of
heterogeneous levels. Either pseudo-likelihood and mean field
inference can be used with L2 regularization, cross-validation, and
prediction on new data.
Woo et al. (2016) <doi:10.1186/s12864-016-2871-3>.
Jun Woo [aut, cre] (<https://orcid.org/0000-0003-3220-2064>),
Jinhua Wang [ctb]