small fix in
coef.svm for sparse data
Remove configure code testing for gcc 2.96.
gknn(): wrong behavior in case of tied k-nearest neighbors (for
use_all=TRUE), and also in case of an overall class tie.
Bugfix in examples of
scale_data_frame() - now calls
scale() if x is not a data frame.
NaiveBayes: better handling od character and logical features
gknn() for generalized k-Nearest Neighbours (using arbitrary proximity measures)
scale_data_frame() for scaling the numeric columns of a data frame.
Bug fix: "inverse" argument for class.weights argument
svm.default() did not work
Change license to GPL-2 OR GPL-3
add coef() method for SVMs with linear kernel
add warning in
predict.naiveBayes() if the variable
type (numeric/factor) does not match for training and new data.
Fix bug in tune when parameter space is sampled
Fix formula interface for NaiveBayes to account for variable removal
Bug fix in
class.weights argument of
svm() now accepts
"inverse", setting the weights inversely proportional to the
predict.naiveBayes now fixes the factor levels of
newdata to be identical with the training data.
libsvm updated to version 3.23
add and use native symbols for C-code
naiveBayes() now supports logical variables
fix some bug in handling weights in
fix numeric issue in
add functions from recommended packages to NAMESPACE
fix bug in svm.default (incorrect handling of subset= argument)
fix bug in predict.svm (new data with NA in response got removed)
residuals are now correctly computed for regression in case of scaled data
hamming.distance() no longer converts input to binary
tune() now uses
mean() to aggregate error
measures from cross-fold replications
remove library("SparseM") statements in code, use namespace semantics instead
Fix memory leak and uninitialized read error in
add warning in
predict.svm() if probabilities should be
predicted, but the model was not trained with
eps to laplace smoothing in
predict.naiveBayes() to account for close-zero probabilities
use R's random number generator for cross-validation and probability computation instead of the system one.
remove require() statements and dependency on stats
vignettes moved to
libsvm upgrade to version 3.17, getting rid of stdout and stderr
write.matrix.csr() now accepts a
read.matrix.csr(), writing factor levels instead
of the numeric codes.
naiveBayes() uses a numerically more stable formula for
calculating the a-posterior probabilities.
predict.naiveBayes() now accepts data with predictors in
an order different from the training data, and also ignores variables
not in the model (especially the response variable).
svm() checks whether parameters which are passed to the
C-code are set to NULL to avoid segfaults.
bug fix in tune with sparse matrices
version bump of libsvm to 3.1
Fixed partial argument matching in several places
NEWS file changed to .Rd format and moved to ‘inst/’
bug fix in svm cross validation
svm() now accepts to set the random seed for libsvm.
tune() now allows user-specified error functionals.
add auto-coercion from Matrix and simple_triplet_matrix objects
Bug fix in
tune.svm(): when a data frame was
provided as validation sample, the response variable was not correctly
sQuote() instead of hard-coded quotes in
warnings and error messages in several places
Bug fix in labeling of decision values
decision.values of fitted values to a svm object
Bug fix in
svm(): Error messages returned by the C
function have not been correctly handled, causing segfaults.
Allow sparse_triplet_matrix objects for
More flexible interface to
Fix bugs in docs for
Fix bugs in
Allow Matrix objects for
Version bump of libsvm to 2.88
Improve ‘DESCRIPTION’ install metadata
tune() now also returns a dispersion measure of all
Bootstrap is done with replacement.
tune.svm() now also accepts the
write.svm() now also stores the scaling information for
the dependent variable.
data sets Glass, HouseVotes84, and Ozone removed (are in package mlbench)
merged help pages for
Bug in ‘NAMESPACE’ file fixed (conditional import from utils failed in R 2.3.1)
predict.naiveBayes() sped up
Bug fix in
plot.svm() (error in case of training
categories without predictions)
methods now added to ‘Suggests’, and grDevices to ‘Imports’
Bug fix: sparse handling was broken since 1.5-9
update to libsvm 2.81
laplace smoothing added to
tune(): allow list of vectors as tune parameter range so that
class.weights in svm-models can be tuned
better default color palette for
probplot() for probability plots
Bug fix: class probability prediction was broken since 1.5-9
tune() now returns the split indices into
training/validation set. Information added about cross validation
plot.svm(): wrong labeling order in levels fixed
predict.svm() now adds row numbers to predictions, and
correctly handles the
na.action argument using
Update to libsvm 2.8 (uses a faster optimization algorithm)
read.matrix.csr() did not work correctly with
svm(): Fixed wrong labeling for predicted decision values and
probabilities in case of a Class factor created from a non-ordered
cmeans() is substantially enhanced, with a complete
rewrite of the underlying C code. It is now possible to specify
case weights and the relative convergence tolerance. For Manhattan
distances, centers are correctly computed as suitably weighted
medians (rather than means) of the observations. The print method
for fclust objects is now more in parallel with related methods, and
registered in the name space.
read.octave() is now deprecated in favor of a
substantially enhanced version in package foreign for reading
in files in Octave text data format.
Use lazy loading
New arguments in
plot.svm() for customizing plot
symbols and colors
Fix of broken code in
plot.svm() for the
fill = FALSE (non-default) case
Fixed memory leak in
Fixed C++ style comments
Example for weighting added in
svm() help page
upgrade to libsvm 2.6: support for probabilities added
NaiveBayes() is more accurate for small probabilities
call is more sensible in
control parameter of
tune() changed to
tunecontrol to solve name space conflict with training
fixed a bug in
bclust() triggered when a cluster had
only one center
adjusted to restructering of R base packages
added a ‘NAMESPACE’ file
write.svm() now also creates a file with
Small bug fixes in
write.svm() added which saves models created
svm() in the format libsvm can read.
Bug fix in
plot.svm(): non-SVs had wrong colors
data sets Ozone and Glass added
Several Docu bug fixes (for functions
upgrade to libsvm 2.5. New feature:
optionally returns decision values for multi-class classification
svm-vignette gave warnings due to rank deficiency in Ozone data
naiveBayes() now also supports metric predictors, and
the standard interface.
Bug fixes in svm:
Prediction of 1 single observation gave an error
k instead of
coefficients have been returned by svm (
k number of classes),
having caused nonsensical results for
k > 3.
The ‘svmdoc’ file in ‘inst/doc’ now is a vignette.
x argument of
is now automatically coerced to a matrix.
Started ‘tests’ directory
naiveBayes() classifier for categorical predictors
read.matrix.csr() which used to be rather slow
Bug fixes for the
svm() interface: when the data included
categorical predictors, the scaling procedure did not only
affect the metric variables, but also the binary variables
in the model matrix.
scaclust() removed. Bug has to be fixed.
Now supports libsvm 2.4
rdiscrete() is now simply a wrapper for
and provided for backwards compatibility only.
Minor bug fixes in
interface issues). New plot function for objects of class
svm working for the 2d-classification case.
svm() now supports the matrix.csr format, as handled by the
SparseM package. Predictors and response variable (if
numeric) are scaled per default.
plot() function for
models by plotting data and support vectors in the data input
space, along with the class borders.
A new generic
tune() function allows parameter tuning
of arbitrary functions using, e.g., boot strapping, or cross validation.
Several convenience wrappers (e.g., for
rpart()) do exist.
Bug fixes in various bclust routines:
required packages are not found
svm() now interfaces LIBSVM 2.35 which is a bug fix release.
A call with invalid parameters now no longer causes R to be
terminated, and the C(++) code became completely silent.
Bugs fixed in
fclustIndex() function and
multivariate normal distributions have been moved to package
Bug fixes in
fixed ‘floyd.c’ (ANSI C pedantic warnings)
Bug fixes in ‘cmeans.c’, ‘cshell.c’ and ‘scaclust.c’ (R header files included and unused variables removed)
Bug fixes in ‘Rsvm.c’ and ‘svm.R’ (incomplete list of returned Support Vectors).
Encapsulate kmeans call in
bclust() in a
construct, because kmeans gives an error when a cluster becomes
empty (which can happen for almost every data set from time to
Added functions for bagged clustering, see help(bclust).
write.pgm() have been removed
from e1071, much improved versions can now be found in the
Lots of documentation updates and bugfixes.
Support Vector Machine interface now upgraded to libsvm V. 2.31 featuring:
weighting of classes for C-classification (for asymmetric sample sizes)
In addition, an introductory article is provided in directory ‘docs/’ (‘svmdoc.pdf’).
classAgreement() now features an option to match factor levels
updated API design for the fuzzy clustering functions
Documentation updates and function name changes (