Ordered homogeneity pursuit lasso (OHPL)
algorithm for group variable selection proposed in Lin et al. (2017)
<DOI:10.1016/j.chemolab.2017.07.004>. The OHPL method exploits the
homogeneity structure in high-dimensional data and enjoys the
grouping effect to select groups of important variables
automatically. This feature makes it particularly useful for
high-dimensional datasets with strongly correlated variables,
such as spectroscopic data.

c(
person("You-Wu", "Lin", email = "lyw015813@126.com", role = c("aut")),
person("Nan", "Xiao", email = "me@nanx.me", role = "cre",
comment = c(ORCID = "0000-0002-0250-5673")))
You-Wu Lin [aut],
Nan Xiao [cre] (<https://orcid.org/0000-0002-0250-5673>)

A collection of data sets from several sources that may be
useful for teaching, practice, or other purposes. Functions
have also been included to assist in the retrieval of table
data from websites or in visualizing sample data.
'OIdata-internal.R' 'getTables.R' 'mapvar.R'
'print.htmlTables.R' 'processTable.R' 'rowScrape.R'
'scrapeTable.R' 'stripTags.R'

Survival analysis supplement to OpenIntro guide
Supplemental functions and data for the OpenIntro guide to
the survival package in R.

Supplemental functions and data for the OpenIntro guide to the survival package in R.

Phil Chalmers <rphilip.chalmers@gmail.com> and Carrie Smith
<carrie.elizabeth@gmail.com> and Matthew Sigal
<matthewsigal@gmail.com>
Provides tools for more easily organizing and plotting
ordinary least square (OLS) growth curve trajectories.

Provides tools for more easily organizing and plotting
individual ordinary least square (OLS) growth curve
trajectories.

c(person('Phil', 'Chalmers',
email='rphilip.chalmers@gmail.com',
role=c('cre', 'aut')),
person('Carrie', 'Smith',
email='carrie.elizabeth@gmail.com',
role=c('ctb')),
person('Matthew', 'Sigal',
email='matthewsigal@gmail.com',
role=c('ctb')))
Provides tools for more easily organizing and
plotting individual ordinary least square (OLS) growth curve
trajectories.
Phil Chalmers [cre, aut],
Carrie Smith [ctb],
Matthew Sigal [ctb]