plotESDA(). These escaped LaTeX specials
were resulting in notes in CRAN package check results for some
flavors.skewness():
compute the moment coefficient of skewness of a continuous, possibly
non-normal variable.plotHD() is now
computed using the new function skewness().pedometrics-defunct.R.\dontrun{}. This was fixed. Now all functions have at least
one example that can be run conditionally on the availability of certain
packages or if R is being used interactively.plotCor() required RColorBrewer::brewer.pal()
to set the color palette. Now the color palette is hard-coded and
package RColorBrewer was thus removed from
Suggests.Additional_repositories to DESCRIPTION as a means
to deal with archived packages such as geoR and
georob.cdfPlot(), cdfStats(),
cdfTable(), and coordenadas(). As a result,
packages spsurvey and xtable were
removed from Suggests. The ASRtools package is
available at https://github.com/samuel-rosa/ASRtools.plotHD and stepVIF: reviews documentation
and plans future changes.coordenadas(),
cdfTable(), cdfStats() and
cdfPlot(). These functions were developed to fulfill the
demands created by the PhD research project (2012-2016) and are no
longer (or rarely) used. Some will be replaced, but all will move to a
package currently under developed at https://github.com/samuel-rosa/ASRtools.Suggests and using it conditionally by employing
requireNamespace().plotCor: reverses default color ramp so that positive
and negative correlations are depicted using red and blue colors,
respectively.plotHD: adds grid lines to histogram.plotESDA: choose the position of the legend of the
bubble plotplotCor to deal with long column names
in the correlation matrix. Long column names (> 5 characters) are now
replaced with x1, x2, …, xn, where n is the number of columns in the
correlation matrix. A message is issues identifying columns and their
respective index.plotCor, a function to create correlation plots
using a colorblind-friendly palette.plotHD.p_sill is used when the variance is
spatially uncorrelated. It is defined as 1e-3 times the guess of
nugget (before the p_sill was set to 1e-12,
which caused errors in optim and nlminb).vgmICP caused by the recent
update of package georob.vgmICP: Improved estimation of the scale parameter
(range) for monotonous variograms by using function
practicalRange from package
geoR.vgmICP: Improve the guess of the partial sill for a
pure nugget effect model.georob::sample.variogram in vgmLags was
incorrect due to an error in the documentation the former. Instead of “a
numeric vector with the upper bounds of a set of contiguous bins”, the
argument lag.dist.def of
georob::sample.variogram requires “a numeric vector with
the lower and upper bounds of a set of contiguous bins”. The
author and maintainer of the georob-package
has been warned about this documentation error.vgmLags was incorrect because it neglected the
fact that, in a full distance matrix, two points a and
b form two pairs, i.e. ab and ba. The mistake
is due to the fact that we use SpatialTools::dist1 to
compute the distance matrix instead of stats::dist.vgmICP to guess the initial covariance
parameters when fitting a variogram model.vgmLags-function, and
added functionality to count the number of points or point-pairs per
lag-distance class so that the user can decide if the returned
lag-distance classes meet his/her needs.vgmSCV to compute the proportion of the
variance that is spatially correlated.checkGMU which can be used to
check the quality of a geostatistical model on uncertainty.base are now
imported to comply with the new change to the CRAN policy described at
https://developer.r-project.org/blosxom.cgi/R-devel/NEWS/2015/06/29#n2015-06-29;Imports: field
in the DESCRIPTION file now are under the
Suggests: field;cramer(): NAs are now removed when
computing the chi-squared statistic;plotHD(): if the Box-Cox transformation is required,
the data is now first checked for zeros and negative values. If zeros
and negative values are present, the data is transformed into a
positive-valued data, with the minimum value equal to 1.plotESDA(): new argument to define the lag-distance
classes;likfit (see the geoR package for more info):
trend.terms() and trend.matrix();breakPoints() and stratify();vgmLags().cramer(): the cross-tabulation is now computed using
table() instead of bigtabulate() to avoid
dependency problems in Windows."numeric" and
"integer.spJitter(),
spSANN(), spsannMSSD(),
spsannPPL(), and spsannCLHS().spSANN(),
spJitter(), and those used as objective functions in
spatial simulated annealing.spSANN().objPairs(),
objPoints(), pairs_per_lag(),
points_per_lag() and spJitter(). Added new
criterion to objPairs() and objPoints(). Added
new method to spJitter() (finite set of candidate
locations). Most of the changes speed up the functions and increase
their functionalities.objPairs(),
objPoints(), pairs_per_lag() and
points_per_lag(). These functions were designed to be used
with spatial simulated annealing.spJitter() and
bbox2sp().plotHD() and
plotESDA().toar() and readsat() because they
are better implemented using GRASS GIS commands directly.buildMS(), used to build a series of
linear models using automated variable selection. Included function
statsMS(), used to obtain performance statistics of a
series of linear models. Included function plotMS(), used
to plot the statistics of a models series. Corrected bugs detected
during the first built.cdfTable(): added argument to return a
data.frame object.cdfPlot(): added argument to define if the
confidence limits of the CDF should be included in the plot.cdfPlot() now has options to show CDF
parameters.stepVIF(). Renamed many of the
functions to comply with the style guide. The package was also renamed
to pedometrics.readsat() and
toar().deltagcp().cdfplot(),
cdfstats(), cdftable(),
coordenadas(), deltagcp(), and
gcpvector().
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