Parallel Bayesian Optimization of Hyperparameters
person("Samuel", "Wilson", email = "samwilson303@gmail.com", role = c("aut", "cre"))
Fast, flexible framework for implementing Bayesian optimization of model
hyperparameters according to the methods described in Snoek et al. <arXiv:1206.2944>.
The package allows the user to run scoring function in parallel, save intermediary
results, and tweak other aspects of the process to fully utilize the computing resources
available to the user.
R (>= 3.5.0), data.table (>= 1.11.8), GauPro, stats, foreach,
dbscan

data.table (>= 1.11.8), GauPro, stats, foreach, dbscan, lhs

data.table (>= 1.11.8), GauPro, stats, foreach, dbscan, lhs,
ggplot2, plotly, crayon

data.table (>= 1.11.8), GauPro, stats, foreach, dbscan, lhs,
plotly, crayon, ggplot2, ggpubr
knitr, rmarkdown, xgboost, doParallel, testthat

data.table (>= 1.11.8), DiceKriging, stats, foreach, dbscan,
lhs, plotly, crayon, ggplot2, ggpubr

data.table (>= 1.11.8), DiceKriging, stats, foreach, dbscan,
lhs, plotly, crayon, ggplot2, ggpubr (>= 0.2.4)

data.table (>= 1.11.8), DiceKriging, stats, foreach, dbscan,
lhs, crayon, ggplot2, ggpubr (>= 0.2.4)

parallel implementation of the "segment" function from package
"DNAcopy"
parallelized version of the "segment" function from
Bioconductor package "DNAcopy", utilizing multi-core
computation on host CPU

parallel implementation of the "segment" function of package
"DNAcopy"

Parallel implementation of the "segment" function of package
"DNAcopy"
Parallelized version of the "segment" function from Bioconductor package "DNAcopy", utilizing multi-core computation on host CPU

Parallel Digital Soil Mapping using Machine Learning
c(
person("Xiaodong","Song",role = c("aut"),email = "xdsong@issas.ac.cn"),
person("Gaoqiang","Ge",role = c("aut","cre"),email = "gaoqiangge2020@163.com"),
person("Jun","Zhu",role = c("aut")),
person("Ganlin","Zhang",role = c("ctb"))
)
Parallel computing, multi-core CPU is used to efficiently compute
and process multi-dimensional soil data.This package includes the
parallelized Quantile Regression Forests algorithm for Digital Soil Mapping
and is mainly dependent on the package 'quantregForest' and 'snowfall'.
Detailed references to the R package and the web site are described in the
methods, as detailed in the method documentation.
methods, utils, caret, geoR, gstat, quantregForest,
randomForest, rgdal, sp,
Xiaodong Song [aut],
Gaoqiang Ge [aut, cre],
Jun Zhu [aut],
Ganlin Zhang [ctb]