parallel, RcppZiggurat, utils, stats, dummies, mclust

Bootstrap, permutation tests, and other resampling functions,
featuring easy-to-use syntax.

Bootstrap, permutation tests, and jackknife,
featuring easy-to-use syntax.

Data Sets for Mathematical Statistics with Resampling in R
c(
person("Laura", "Chihara", email = "lchihara@carleton.edu", role = "aut"),
person("Tim", "Hesterberg", email = "TimHesterberg@gmail.com", role = "aut"),
person("Albert Y.", "Kim", email = "albert.ys.kim@gmail.com", role = "cre")
)
Package to load data sets from "Mathematical Statistics
with Resampling in R" (2011) by Laura Chihara and Tim Hesterberg.
Laura Chihara [aut],
Tim Hesterberg [aut],
Albert Y. Kim [cre]

Package of data sets from "Mathematical Statistics
with Resampling in R" (2011) by Laura Chihara and Tim Hesterberg.

Package of data sets from "Mathematical Statistics
with Resampling in R" (1st Ed. 2011, 2nd Ed. 2018) by Laura Chihara and Tim Hesterberg.

c(
person("Laura", "Chihara", email = "lchihara@carleton.edu", role = "aut"),
person("Tim", "Hesterberg", email = "TimHesterberg@gmail.com", role = "aut"),
person("Albert Y.", "Kim", email = "albert.ys.kim@gmail.com", role = c("aut", "cre"))
)
Laura Chihara [aut],
Tim Hesterberg [aut],
Albert Y. Kim [aut, cre]

Data Sets for "Mathematical Statistics with Resampling and R"
(3rd Ed)
c(
person("Laura", "Chihara", role = "aut", email = "lchihara@carleton.edu"),
person("Tim", "Hesterberg", role = c("aut", "cre"), email = "timhesterberg@gmail.com"))
Laura Chihara [aut],
Tim Hesterberg [aut, cre]
Data sets for Chihara and Hesterberg (2022, ISBN: 978-1-119-87404-1)
"Mathematical Statistics with Resampling in R" (3rd Ed).

Autoencoder-based Residual Deep Network with Keras Support
This package is the R implementation of the Autoencoder-based Residual Deep Network that is based on this paper (https://arxiv.org/abs/1812.11262).

Bootstrap Imputation for Single-Cell RNA-Seq Data
Given a log-transformed expression matrix and list of informative genes:
subsample informative genes, cluster samples using shared nearest neighbors clustering,
estimate missing expression values with the distribution mean of means extrapolated
from these cell clusterings, and return an imputed expression matrix. See Tracy, S.,
Yuan, G.C. and Dries, R. (2019) <doi:10.1186/s12859-019-2977-0> for more details.
list( packages = list( list(package = "pandas"),
list(package = "networkx"), list(package = "python-louvain") )
)
data.table, dbscan (>= 1.1-3), igraph (>= 1.2.4.1), irlba,
Matrix, methods, parallel, reticulate (>= 1.14)