News for Package e1071

Changes in version 1.7-16

• fix bug in svmdoc vignette

• fix possible glitch in svm.cpp regarding memory allocation

Changes in version 1.7-15

• fix bug in read.matrix.csr()

Changes in version 1.7-14

• fix incomplete arguments to avoid partial matches

• fix small bug in svm.cpp (Rprintf() wrongly called)

• fix predict.gknn() and predict.svn() to allow for newdata argument with character variables instead of factors (levels are taken from original data).

Changes in version 1.7-13

• add ORCID ids

Changes in version 1.7-12

• add tune.gknn() wrapper

Changes in version 1.7-11

• cosmetic changes

Changes in version 1.7-10

• small fix in coef.svm for sparse data

Changes in version 1.7-9

• Remove configure code testing for gcc 2.96.

Changes in version 1.7-8

• Bugfixes in gknn(): wrong behavior in case of tied k-nearest neighbors (for use_all=TRUE), and also in case of an overall class tie.

Changes in version 1.7-7

• Bugfix in examples of cshell()

Changes in version 1.7-6

• Bugfix in scale_data_frame() - now calls scale() if x is not a data frame.

Changes in version 1.7-5

• NaiveBayes: better handling od character and logical features

• Added: gknn() for generalized k-Nearest Neighbours (using arbitrary proximity measures)

• Added: scale_data_frame() for scaling the numeric columns of a data frame.

Changes in version 1.7-4

• Bug fix: "inverse" argument for class.weights argument in svm.default() did not work

Changes in version 1.7-2

• Change license to GPL-2 OR GPL-3

• add coef() method for SVMs with linear kernel

Changes in version 1.7-1

• 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

Changes in version 1.7-0

• Bug fix in lca()

• The class.weights argument of svm() now accepts "inverse", setting the weights inversely proportional to the class distribution

• predict.naiveBayes now fixes the factor levels of newdata to be identical with the training data.

• libsvm updated to version 3.23

Changes in version 1.6-8

• add and use native symbols for C-code

• naiveBayes() now supports logical variables

Changes in version 1.6-7

• fix some bug in handling weights in svm.default()

Changes in version 1.6-6

• fix numeric issue in classAgreement()

• 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

Changes in version 1.6-5

• hamming.distance() no longer converts input to binary

• tune() now uses mean() to aggregate error measures from cross-fold replications

Changes in version 1.6-4

• remove library("SparseM") statements in code, use namespace semantics instead

• Fix memory leak and uninitialized read error in write.svm()

• add warning in predict.svm() if probabilities should be predicted, but the model was not trained with probability = TRUE

• add eps to laplace smoothing in predict.naiveBayes() to account for close-zero probabilities also.

• use R's random number generator for cross-validation and probability computation instead of the system one.

Changes in version 1.6-3

• remove require() statements and dependency on stats

Changes in version 1.6-2

• vignettes moved to vignettes folder.

• libsvm upgrade to version 3.17, getting rid of stdout and stderr

Changes in version 1.6-1

• write.matrix.csr() now accepts a fac argument similar to 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.

Changes in version 1.6

• 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/’

Changes in version 1.5-28

• bug fix in svm cross validation

Changes in version 1.5-27

• svm() now accepts to set the random seed for libsvm.

Changes in version 1.5-26

• tune() now allows user-specified error functionals.

Changes in version 1.5-25

• add auto-coercion from Matrix and simple_triplet_matrix objects to predict.svm()

• Bug fix in tune.svm(): when a data frame was provided as validation sample, the response variable was not correctly extracted

Changes in version 1.5-24

• Cosmetics: use sQuote() instead of hard-coded quotes in warnings and error messages in several places

• Bug fix in labeling of decision values

• add decision.values of fitted values to a svm object

Changes in version 1.5-23

• Bug fix in svm(): Error messages returned by the C function have not been correctly handled, causing segfaults.

Changes in version 1.5-22

• minor fix

Changes in version 1.5-21

• Allow sparse_triplet_matrix objects for svm()

Changes in version 1.5-20

• More flexible interface to naiveBayes()

• Fix bugs in docs for kurtosis()

Changes in version 1.5-19

• Fix bugs in read.matrix.csr() and write.matrix.csr()

• Allow Matrix objects for svm()

• Version bump of libsvm to 2.88

Changes in version 1.5-18

• Improve ‘DESCRIPTION’ install metadata

Changes in version 1.5-17

• tune() now also returns a dispersion measure of all training samples.

• Bootstrap is done with replacement.

• tune.svm() now also accepts the epsilon parameter.

Changes in version 1.5-16

• 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 naiveBayes() and predict.naiveBayes()

Changes in version 1.5-15

• Bug in ‘NAMESPACE’ file fixed (conditional import from utils failed in R 2.3.1)

Changes in version 1.5-14

• 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’

Changes in version 1.5-13

• Bug fix: sparse handling was broken since 1.5-9

Changes in version 1.5-12

• update to libsvm 2.81

• laplace smoothing added to naiveBayes()

Changes in version 1.5-11

• tune(): allow list of vectors as tune parameter range so that class.weights in svm-models can be tuned

• better default color palette for plot.tune()

• New function probplot() for probability plots

Changes in version 1.5-10

• Bug fix: class probability prediction was broken since 1.5-9

Changes in version 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 napredict().

Changes in version 1.5-8

• Update to libsvm 2.8 (uses a faster optimization algorithm)

Changes in version 1.5-7

• read.matrix.csr() did not work correctly with matrix-only objects.

• svm(): Fixed wrong labeling for predicted decision values and probabilities in case of a Class factor created from a non-ordered character vector

Changes in version 1.5-6

• 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.

Changes in version 1.5-5

• read.octave() is now deprecated in favor of a substantially enhanced version in package foreign for reading in files in Octave text data format.

Changes in version 1.5-4

• Use lazy loading

Changes in version 1.5-3

• 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

Changes in version 1.5-2

• Fixed memory leak in svm()

Changes in version 1.5-1

• Fixed C++ style comments

Changes in version 1.5-0

• Example for weighting added in svm() help page

• upgrade to libsvm 2.6: support for probabilities added

Changes in version 1.4-1

• NaiveBayes() is more accurate for small probabilities

• call is more sensible in tune(), tune.foo(), and best.foo() objects.

• control parameter of tune() changed to tunecontrol to solve name space conflict with training methods using control themselves

• new function matchControls()

• fixed a bug in bclust() triggered when a cluster had only one center

Changes in version 1.4-0

• adjusted to restructering of R base packages

• added a ‘NAMESPACE’ file

• Function write.svm() now also creates a file with scaling information

Changes in version 1.3.16

• Small bug fixes in predict.svm() and plot.svm()

• Function write.svm() added which saves models created with svm() in the format libsvm can read.

Changes in version 1.3.15

• Bug fix in plot.svm(): non-SVs had wrong colors

• data sets Ozone and Glass added

Changes in version 1.3.14

• Several Docu bug fixes (for functions plot.bclust(), impute(), stft(), svm.formula(), svm.default())

• upgrade to libsvm 2.5. New feature: predict.svm() 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.

Changes in version 1.3.13

• Bug fixes in svm:

  • Prediction of 1 single observation gave an error

  • Only k instead of k*(k-1)/2 \rho 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.

Changes in version 1.3-12

• The x argument of cmeans() and bclust() is now automatically coerced to a matrix.

• Started ‘tests’ directory

• New method: naiveBayes() classifier for categorical predictors

• optimization of 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.

• Function scaclust() removed. Bug has to be fixed.

Changes in version 1.3-10

• Now supports libsvm 2.4

Changes in version 1.3-9

• rdiscrete() is now simply a wrapper for sample() and provided for backwards compatibility only.

• Minor bug fixes in svm() and tune() (mostly interface issues). New plot function for objects of class svm working for the 2d-classification case.

Changes in version 1.3-7

• svm() now supports the matrix.csr format, as handled by the SparseM package. Predictors and response variable (if numeric) are scaled per default.

• A new plot() function for svm() objects visualizes classification 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 svm(), nnet(), and rpart()) do exist.

Changes in version 1.3-3

• Bug fixes in various bclust routines: stop() if 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 print.fclust().

Changes in version 1.3-1

• Functions rmvnorm() and dmvnorm() for multivariate normal distributions have been moved to package mvtnorm.

• Bug fixes in print.fclust() and fclustIndex().

• fixed ‘floyd.c’ (ANSI C pedantic warnings)

Changes in version 1.2-1

• 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 try() construct, because kmeans gives an error when a cluster becomes empty (which can happen for almost every data set from time to time).

Changes in version 1.2-0

• Added functions for bagged clustering, see help(bclust).

• read.pnm() and write.pgm() have been removed from e1071, much improved versions can now be found in the new packagepixmap.

• Lots of documentation updates and bugfixes.

• Support Vector Machine interface now upgraded to libsvm V. 2.31 featuring:

  • Multi-Class Classification

  • weighting of classes for C-classification (for asymmetric sample sizes)

  • \nu-regression

  • Formula Interface

  • k-fold cross-validation

  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 (cmeans(), cshell(), scaclust()). Documentation updates and function name changes (cmeanscl() to cmeans(), validity.measures() to fclustIndex())