Added support for

`e1071::naiveBayes()`

, an implementation of the standard naive Bayes classifier (#42).Fixed a bug in

`plotPartial()`

that caused the`col.regions`

argument to have no effect when`levelplot = FALSE`

(#58).Fixed a bug with categorical variables in

`gbm`

models when`recursive = TRUE`

(#63).More informative progress bars (with estimated time to completion!!) powered by the

`progress`

package. To use, simply call`partial()`

with the option`progress = "progress"`

(#66).Added ORCiD ID to the author field in the

`DESCRIPTION`

file.Way cooler logo?

`partial()`

gained several new plotting options:`plot.engine`

, which controls the plotting engine used whenever`plot = TRUE`

(current options include`"lattice"`

(the default) and`"ggplot2"`

(#71).The arguments to

`autoplot()`

and`plotPartial()`

are now more consistent with each other.The names of (most) helper functions have changed from lowerCamelCase to snake_case.

`partial()`

now works (better) with tibbles (#59).`partial()`

now treats`"xgb.Booster"`

objects with`objective = "reg:logistc"`

as regression (#68).Removed use of

`ggplot2::aes_string()`

in`autoplot()`

(which is soft deprecated as of`ggplot2`

version 3.0.0) (#73).

Properly registered native routines and disabled symbol search.

Fixed a bug for

`gbm`

models using the multinomial distribution.Refactored code to improve structure.

`partial()`

gained three new options:`inv.link`

(experimental),`ice`

, and`center`

. The latter two have to do with constructing individual conditional expectation (ICE) curves and centered ICE (c-ICE) curves. The`inv.link`

option is for transforming predictions from models that can use non-Gaussian distributions (e.g.,`glm`

,`gbm`

, and`xgboost`

). Note that these options were added for convenience and the same results (plus much more) can still be obtained using the flexible`pred.fun`

argument. (#36).`plotPartial()`

gained five new options:`center`

,`plot.pdp`

,`pdp.col`

,`pdp.lwd`

, and`pdp.lty`

; see`?plotPartial`

for details.Fixed default y-axis label for

`autoplot()`

with two numeric predictors (#48).Added

`CITATION`

file.Better support for neural networks from the

`nnet`

package.Fixed a bug for

`nnet::multinom()`

models with binary response.

Fixed minor pandoc conversion issue with

`README.md`

.Added subdirectory called

`tools`

to hold figures for`README.md`

.

- Registered native routines and disabled symbol search.

Added support for

`MASS::lda()`

,`MASS::qda()`

, and`mda::mars()`

.New arguments

`quantiles`

,`probs`

, and`trim.outliers`

in`partial`

. These arguments make it easier to construct PDPs over the relevant range of a numeric predictor without having to specify`pred.grid`

, especially when outliers are present in the predictors (which can distort the plotted relationship).The

`train`

argument can now accept matrices; in particular, object of class`"matrix"`

or`"dgCMatrix"`

. This is useful, for example, when working with XGBoost models (i.e., objects of class`"xgb.Booster"`

).New logical argument

`prob`

indicating whether or not partial dependence values for classification problems should be returned on the original probability scale, rather than the centered logit; details for the centered logit can be found on page 370 in the second edition of*The Elements of Statistical Learning*.Fixed some typos in

`NEWS.md`

.New function

`autoplot`

for automatically creating`ggplot2`

graphics from`"partial"`

objects.

`partial()`

is now much faster with`"gbm"`

object due to a call to`gbm::plot.gbm()`

whenever`pred.grid`

is not explicitly given by the user. (`gbm::plot.gbm()`

exploits a computational shortcut that does not involve any passes over the training data.)New (experimental) function

`topPredictors()`

for extracting the names of the most “important” predictors. This should make it one step easier (in most cases) to construct PDPs for the most “important”" features in a fitted model.A new argument,

`pred.fun`

, allows the user to supply their own prediction function. Hence, it is possible to obtain PDPs based on the median, rather than the mean. It is also possible to obtain PDPs for classification problems on the probability scale. See`?partial`

for examples.Minor bug fixes and documentation tweaks.

The

`...`

argument in the call to`partial()`

now refers to additional arguments to be passed onto`stats::predict()`

rather than`plyr::aaply()`

. For example, using`partial()`

with`"gbm"`

objects will require specification of`n.trees`

which can now simply be passed to`partial()`

via the`...`

argument.Added the following arguments to

`partial()`

:`progress`

(`plyr`

-based progress bars),`parallel`

(`plyr`

/`foreach`

-based parallel execution), and`paropts`

(list of additional arguments passed onto`foreach`

when`parallel = TRUE`

).Various bug fixes.

`partial()`

now throws an informative error message when the`pred.grid`

argument refers to predictors not in the original training data.The column name for the predicted value has been changed from

`"y"`

to`"yhat"`

.

`randomForest`

is no longer imported.Added support for the

`caret`

package (i.e., objects of class`"train"`

).Added example data sets:

`boston`

(corrected Boston housing data) and`pima`

(corrected Pima Indians diabetes data).Fixed error that sometimes occurred when

`chull = TRUE`

causing the convex hull to not be computed.Refactored

`plotPartial()`

to be more modular.Added

`gbm`

support for most non-`"binomial"`

families`.

`randomForest`

is now imported.Added examples.

- Fixed a non canonical CRAN URL in the README file.

`partial()`

now makes sure each column of`pred.grid`

has the correct class, levels, etc.`partial()`

gained a new option,`levelplot`

, which defaults to`TRUE`

. The original option,`contour`

, has changed and now specifies whether or not to add contour lines whenever`levelplot = TRUE`

.

Fixed a number of URLs.

More thorough documentation.

Fixed a couple of URLs and typos.

Added more thorough documentation.

Added support for C5.0, Cubist, nonlinear least squares, and XGBoost models.

- Initial release.