The IDPmisc package contains different high-level graphics functions for displaying large datasets, displaying circular data in a very flexible way, finding local maxima, brewing color ramps, drawing nice arrows, zooming 2D-plots, creating figures with differently colored margin and plot region.  In addition, the package contains auxiliary functions for data manipulation like omitting observations with irregular values or selecting data by logical vectors, which include NAs. Other functions are especially useful in spectroscopy and analyses of environmental data: robust baseline fitting, finding peaks in spectra.

Utilities of Institute of Data Analyses and Process Design (www.idp.zhaw.ch)
Andreas Ruckstuhl, Thomas Unternaehrer, Rene Locher

Utilities of Institute of Data Analyses and Process Design
(www.idp.zhaw.ch)
The IDPmisc package contains different high-level graphics
functions for displaying large datasets, displaying circular
data in a very flexible way, finding local maxima, brewing
color ramps, drawing nice arrows, zooming 2D-plots, creating
figures with differently colored margin and plot region.  In
addition, the package contains auxiliary functions for data
manipulation like omitting observations with irregular values
or selecting data by logical vectors, which include NAs. Other
functions are especially useful in spectroscopy and analyses of
environmental data: robust baseline fitting, finding peaks in
spectra.

'Utilities of Institute of Data Analyses and Process Design
(www.zhaw.ch/idp)'
Rene Locher [aut, cre],
Andreas Ruckstuhl [ctb]
c(person("Rene", family = "Locher", role = c("aut", "cre"), email = "rene.locher@zhaw.ch"),
person("Andreas", family = "Ruckstuhl", role = c("ctb")))
Different high-level graphics functions for displaying large datasets, displaying circular data in a very flexible way, finding local maxima, brewing color ramps, drawing nice arrows, zooming 2D-plots, creating figures with differently colored margin and plot region.  In addition, the package contains auxiliary functions for data manipulation like omitting observations with irregular values or selecting data by logical vectors, which include NAs. Other functions are especially useful in spectroscopy and analyses of environmental data: robust baseline fitting, finding peaks in spectra.