hwriterPlus: Extending the hwriter Package
R (>= 2.10.0), hwriter, TeachingDemos, RUnit
This package extends the package hwriter providing
facilities such as the inclusion of output from R, the results
of an R session, the display of mathematical expressions using
LaTeX notation and the incorporation of SVG graphical objects.
It uses MathJax to display mathematical expressions, in
contrast to R2HTML which uses MathML.

This package extends the package hwriter providing
facilities such as the inclusion of output from R, the
results of an R session, the display of mathematical
expressions using LaTeX notation and the incorporation of
SVG graphical objects.  It uses MathJax to display
mathematical expressions, in contrast to R2HTML which
uses ASCIIMathML.

Programmatic interface to the Harmonized World Soil Database
'HWSD' web services (<https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1247>).
Allows for easy downloads of 'HWSD' soil data directly to your R workspace
or your computer. Routines for both single pixel data downloads and
gridded data are provided.
rgdal, ncdf4, magrittr, knitr, markdown, rmarkdown, covr,
testthat

c(person("Delyan", "Savchev",
role=c("aut"),email="madbss@bristol.ac.uk"),
person("Guy", "Nason", role=c("aut","cre"),
email="g.p.nason@bristol.ac.uk"))
Provides methods to test whether time series is consistent
with white noise.
Delyan Savchev [aut],
Guy Nason [aut, cre]

c(
person("Claudia",   "Beleites",  role = c("aut","cre"), email = "Claudia.Beleites@chemometrix.gmbh"),
person("Erick",     "Oduniyi",   role = "aut",          email = "eeoduniyi@gmail.com"))
Enhance package 'testthat' by allowing tests to be attached to the function/object they test.
This allows to keep functional and unit test code together.
Claudia Beleites [aut, cre],
Erick Oduniyi [aut]

Build, deploy and evaluate a Hybrid Ensemble
Michel Ballings, Dauwe Vercamer, and Dirk Van den Poel
This package contains functions to build and deploy an ensemble
consisting of six different sub-ensembles: Bagged Logistic Regressions,
Random Forest, Stochastic AdaBoost, Kernel Factory, Bagged Neural Networks,
Bagged Support Vector Machines. Functions to cross-validate the Hybrid
Ensemble and plot and summarize the results are also provided. There is
also a function to assess the importance of the predictors.
'auxiliary.R' 'CVhybridEnsemble.R' 'hybridEnsemble.R'
'hybridEnsembleNews.R' 'importance.hybridEnsemble.R'
'plot.CVhybridEnsemble.R' 'predict.hybridEnsemble.R'
'summary.CVhybridEnsemble.R' 'zzz.R'