Methods for Identification of Outliers in Environmental Data
c(
person("Martina", "Campulova", email = "martina.campulova@mendelu.cz", role = c("cre")),
person("Martina", "Campulova", email = "martina.campulova@mendelu.cz", role = c("aut")),
person("Roman", "Campula", email = "roman.campula@cdv.cz", role = c("ctb")))
Three semi-parametric methods for detection of outliers in environmental data based on kernel regression and subsequent analysis of smoothing residuals. The first method (Campulova, Michalek, Mikuska and Bokal (2018) <DOI: 10.1002/cem.2997>) analyzes the residuals using changepoint analysis, the second method is based on control charts (Campulova, Veselik and Michalek (2017) <DOI: 10.1016/j.apr.2017.01.004>) and the third method (Holesovsky, Campulova and Michalek (2018) <DOI: 10.1016/j.apr.2017.06.005>) analyzes the residuals using extreme value theory (Holesovsky, Campulova and Michalek (2018) <DOI: 10.1016/j.apr.2017.06.005>).
MASS, car, changepoint, ecp, graphics, ismev, lokern,
robustbase, stats
Martina Campulova [cre],
Martina Campulova [aut],
Roman Campula [ctb]

Configurable Reporting on your External Compute Environment
person(given = "Mark",
family = "Sellors",
email = "rstats@5vcc.com",
role = c("aut", "cre"))
Runs a series of configurable tests against a user's compute
environment. This can be used for checking that things like a specific
directory or an environment variable is available before you start an analysis.
Alternatively, you can use the package's situation report when filing error
reports with your compute infrastructure.

Data Management Package (Exposure and Occurrence Data in R)
c(person("Rainer", "Walke", role = c("aut", "cre"),
email = "walke@demogr.mpg.de",
comment = c(ORCID = "0000-0002-4269-6531")))
This data management package provides some helper
classes for publicly available data sources (HMD, DESTATIS) in
Demography. Similar to ideas developed in the Bioconductor
project <https://bioconductor.org> we strive to encapsulate data in easy
to use S4 objects.
If original data is provided in a text file, the resulting S4
object contains all information from that text file. But the
information is somehow structured (header, footer, etc).
Further the classes provide methods to make a subset for selected
calendar years or selected regions. The resulting subset
objects still contain the original header and footer information.