Changelog
Source:NEWS.md
parsnip 1.2.0
Bug Fixes
Tightened logic for outcome checking. This resolves issues—some errors and some silent failures—when atomic outcome variables have an attribute (#1060, #1061).
Fixed bug in fitting some model types with the
"spark"
engine (#1045).Fixed issues in metadata for the
"brulee"
engine where several arguments were mistakenly protected. (#1050, #1054)Fixed documentation for
mlp(engine = "brulee")
: the default values forlearn_rate
andepochs
were swapped (#1018).Fixed a bug in the integration with workflows where using a model formula with a formula preprocessor could result in a double intercept (#1033).
Other Changes
We no longer add
eval_time
arguments to the prediction specification for the engine (#1039).parsnip now lets the engines for [mlp()] check for acceptable values of the activation function (#1019)
rpart_train()
has been deprecated in favor of usingdecision_tree()
with the"rpart"
engine orrpart::rpart()
directly (#1044)..filter_eval_time()
was moved to the survival standalone file.Improved errors and documentation related to special terms in formulas. See
?model_formula
to learn more. (#770, #1014)Improved errors in cases where the outcome column is misspecified. (#1003)
The
new_data
argument for thepredict()
method forcensoring_model_reverse_km
objects has been deprecated (#965).When computing censoring weights, the resulting vectors are no longer named (#1023).
The
predict()
method forcensoring_model_reverse_km
objects now checks that...
are empty (#1029).
parsnip 1.1.1
CRAN release: 20230817
Fixed bug where prediction on rank deficient
lm()
models produced.pred_res
instead of.pred
. (#985)Fixed bug where sparse data was being coerced to nonsparse format doing
predict()
.For BART models with the
dbarts
engine,predict()
can now also return the standard error for confidence and prediction intervals (#976).augment()
now works for censored regression models.A few censored regression helper functions were exported:
.extract_surv_status()
and.extract_surv_time()
(#973, #980).Fixed bug where
boost_tree()
models couldn’t be fit with 1 predictor ifvalidation
argument was used. (#994)
parsnip 1.1.0
CRAN release: 20230412
This release of parsnip contains a number of new features and bug fixes, accompanied by several optimizations that substantially decrease the time to fit()
and predict()
with the package.
Improvements to "glmnet"
engine interfaces
glmnet models fitted with baseR family objects are now supported for
linear_reg()
,logistic_reg()
, andmultinomial_reg()
(#890).multi_predict()
methods forlinear_reg()
,logistic_reg()
, andmultinom_reg()
models fitted with the"glmnet"
engine now check thetype
better and error accordingly (#900)..organize_glmnet_pred()
now expects predictions for a single penalty value (#876).
Survival analysis
The
time
argument topredict_survival()
andpredict_hazard()
is deprecated in favor of the neweval_time
argument (#936).Added several internal functions (to help work with
Surv
objects) as a standalone file that can be used in other packages viausethis::use_standalone("tidymodels/parsnip")
. These changes provide tooling for downstream packages to handle inverse probability censoring weights (#893, #897, #937).An internal method for generating inverse probability of censoring weights (IPCW) of Graf et al (1999) is available via
.censoring_weights_graf()
.
Bug fixes
Made
fit()
behave consistently with respect to missingness in the classification setting. Previously,fit()
erroneously raised an error about the class of the outcome when there were no complete cases, and now always passes along complete cases to be handled by the modeling function (#888).Fixed bug where model fits with
engine = "earth"
would fail when the package’s namespace hadn’t been attached (#251).Fixed bug where model fits with factor predictors and
engine = "kknn"
would fail when the package’s namespace hadn’t been attached (#264).Fixed bug with prediction from a boosted tree model fitted with
"xgboost"
using a custom objective function (#875).
Other changes
Implemented a number of optimizations in parsnip’s backend that substantially decrease evaluation time to
fit()
andpredict()
(#901, #902, #910, #921, #929, #923, #931, #932, #933).logistic_reg()
will now warn atfit()
when the outcome has more than two levels (#545).Rather than being implemented in each method, the check for the
new_data
argument being mistakenly passed asnewdata
tomulti_predict()
now happens in the generic. Packages reexporting themulti_predict()
generic and implementing nowduplicate checks may see new failures and can remove their own analogous checks. This check already existed in allpredict()
methods (viapredict.model_fit()
) and all parsnipmulti_predict()
methods (#525).Functions now indicate what class the outcome was if the outcome is the wrong class (#887).
The minimum version for R is now 3.5 (#926).
Moved forward with the deprecation of
req_pkgs()
in favor ofrequired_pkgs()
. The function will now error (#871).Transitioned all softdeprecations that were at least a year old to warndeprecations. These changes apply to
fit_control()
,surv_reg()
,varying()
,varying_args()
, and the"liquidSVM"
engine.Various bug fixes and improvements to documentation.
parsnip 1.0.4
CRAN release: 20230222
For censored regression models, a “reverse KaplanMeier” curve is computed for the censoring distribution. This can be used when evaluating this type of model (#855).
The model specification methods for
generics::tune_args()
andgenerics::tunable()
are now registered unconditionally (tidymodels/workflows#192).
parsnip 1.0.3
CRAN release: 20221111
Adds documentation and tuning infrastructure for the new
flexsurvspline
engine for thesurvival_reg()
model specification from thecensored
package (@mattwarkentin, #831).The matrix interface for fitting
fit_xy()
now works for the"censored regression"
mode (#829).The
num_leaves
argument ofboost_tree()
slightgbm
engine (via the bonsai package) is now tunable.A change in our data checking code resulted in about a 3fold speedup in parsnip (#835)
parsnip 1.0.2
CRAN release: 20221001
A bagged neural network model was added (
bag_mlp()
). Engine implementations will live in the baguette package.Fixed installation failures due to undocumented knitr installation dependency (#785).
fit_xy()
now fails when the model mode is unknown.brulee enginespecific tuning parameters were updated. These changes can be used with dials version > 1.0.0.
fit()
andfit_xy()
doesn’t error anymore ifcontrol
argument isn’t acontrol_parsnip()
object. Will work as long as the object passed tocontrol
includes the same elements ascontrol_parsnip()
.Improved prompts related to missing (or not loaded) extension packages as well as better handling of model mode conflicts.
parsnip 1.0.1
CRAN release: 20220818
 Enabled passing additional engine arguments with the xgboost
boost_tree()
engine. To supply enginespecific arguments that are documented inxgboost::xgb.train()
as arguments to be passed viaparams
, supply the list elements directly as named arguments toset_engine()
. Read more in?details_boost_tree_xgboost
(#787).
parsnip 1.0.0
CRAN release: 20220616
Model Specification Changes
Enable the use of case weights for models that support them.
show_model_info()
now indicates which models can utilize case weights.Model type functions will now message informatively if a needed parsnip extension package is not loaded (#731).
Refactored internals of model specification printing functions. These changes are nonbreaking for extension packages, but the new
print_model_spec()
helper is exported for use in extensions if desired (#739).
Bug fixes
Fixed bug where previously set engine arguments would propagate through
update()
methods despitefresh = TRUE
(#704).Fixed a bug where an error would be thrown if arguments to model functions were namespaced (#745).
predict(type = "prob")
will now provide an error if the outcome variable has a level called"class"
(#720).An inconsistency for probability type predictions for twoclass GAM models was fixed (#708)
Fixed translated printing for
null_model()
(#752)
Other changes
Added a
glm_grouped()
function to convert long data to the grouped format required byglm()
for logistic regression.xgb_train()
now allows for case weightsAdded
ctree_train()
andcforest_train()
wrappers for the functions in the partykit package. Engines for these will be added to other parsnip extension packages.Exported
xgb_predict()
which wraps xgboost’spredict()
method for use with parsnip extension packages (#688).Added a developer function,
.model_param_name_key
that translates names of tuning parameters.
parsnip 0.2.1
CRAN release: 20220317
Fixed a major bug in spark models induced in the previous version (#671).
Updated the parsnip addin with new models and engines.
Updated parameter ranges for some
tunable()
methods and added a missing engine argument for brulee models.Added information about how to install the mixOmics package for PLS models (#680)
parsnip 0.2.0
CRAN release: 20220309
Model Specification Changes
Bayesian additive regression trees (BART) were added via the
bart()
function.Added the
"glm"
engine forlinear_reg()
for numeric outcomes (#624).Added
brulee
engines forlinear_reg()
,logistic_reg()
,multinom_reg()
andmlp()
.
Bug fixes
A bug for class predictions of twoclass GAM models was fixed (#541)
Fixed a bug for
logistic_reg()
with the LiblineaR engine (#552).The list column produced when creating survival probability predictions is now always called
.pred
(with.pred_survival
being used inside of the list column).Fixed outcome type checking affecting a subset of regression models (#625).
Prediction using
multinom_reg()
with thennet
engine with a single row no longer fails (#612).
Other Changes
When the xy interface is used and the underlying model expects to use a matrix, a better warning is issued when predictors contain nonnumeric columns (including dates).
The fit time is only calculated when the
verbosity
argument ofcontrol_parsnip()
is 2L or greater. Also, the call tosystem.time()
now usesgcFirst = FALSE
. (#611)fit_control()
is softdeprecated in favor ofcontrol_parsnip()
.New
extract_parameter_set_dials()
method to extract parameter sets from model specs.New
extract_parameter_dials()
method to extract a single parameter from model specs.Argument
interval
was added for prediction: For types"survival"
and"quantile"
, estimates for the confidence or prediction interval can be added if available (#615).set_dependency()
now allows developers to create package requirements that are specific to the model’s mode (#604).varying_args()
is softdeprecated in favor oftune_args()
.An
autoplot()
method was added for glmnet objects, showing the coefficient paths versus the penalty values (#642).parsnip is now more robust working with keras and tensorflow for a larger range of versions (#596).
xgboost engines now use the new
iterationrange
parameter instead of the deprecatedntreelimit
(#656).
Developer
 Models information can be reregistered as long as the information being registered is the same. This is helpful for packages that add new engines and use
devtools::load_all()
(#653).
parsnip 0.1.7
CRAN release: 20210721
Model Specification Changes
A model function (
gen_additive_mod()
) was added for generalized additive models.Each model now has a default engine that is used when the model is defined. The default for each model is listed in the help documents. This also adds functionality to declare an engine in the model specification function.
set_engine()
is still required if enginespecific arguments need to be added. (#513)parsnip now checks for a valid combination of engine and mode (#529)
The default engine for
multinom_reg()
was changed tonnet
.
Other Changes
The helper functions
.convert_form_to_xy_fit()
,.convert_form_to_xy_new()
,.convert_xy_to_form_fit()
, and.convert_xy_to_form_new()
for converting between formula and matrix interface are now exported for developer use (#508).Fix bug in
augment()
when nonpredictor, nonoutcome variables are included in data (#510).New article “Fitting and Predicting with parsnip” which contains examples for various combinations of model type and engine. ( #527)
parsnip 0.1.6
CRAN release: 20210527
Model Specification Changes
A new linear SVM model
svm_linear()
is now available with theLiblineaR
engine (#424) and thekernlab
engine (#438), and theLiblineaR
engine is available forlogistic_reg()
as well (#429). These models can use sparse matrices viafit_xy()
(#447) and have atidy
method (#474).
For models with
glmnet
engines: A single value is required for
penalty
(either a single numeric value or a value oftune()
) (#481).  A special argument called
path_values
can be used to set thelambda
path as a specific set of numbers (independent of the value ofpenalty
). A pure ridge regression models (i.e.,mixture = 1
) will generate incorrect values if the path does not include zero. See issue #431 for discussion (#486).
 A single value is required for
The
liquidSVM
engine forsvm_rbf()
was deprecated due to that package’s removal from CRAN. (#425)The xgboost engine for boosted trees was translating
mtry
to xgboost’scolsample_bytree
. We now mapmtry
tocolsample_bynode
since that is more consistent with how random forest works.colsample_bytree
can still be optimized by passing it in as an engine argument.colsample_bynode
was added to xgboost after theparsnip
package code was written. (#495)For xgboost,
mtry
andcolsample_bytree
can be passed as integer counts or proportions, whilesubsample
andvalidation
should always be proportions.xgb_train()
now has a new optioncounts
(TRUE
orFALSE
) that states which scale formtry
andcolsample_bytree
is being used. (#461)
Other Changes
Relicensed package from GPL2 to MIT. See consent from copyright holders here.
set_mode()
now checks ifmode
is compatible with the model class, similar tonew_model_spec()
(@jtlandis, #467). Bothset_mode()
andset_engine()
now error forNULL
or missing arguments (#503).
Reorganized model documentation:

update
methods were moved out of the model help files (#479).  Each model/engine combination has its own help page.
 The model help page has a dynamic bulleted list of the engines with links to the individual help pages.

generics::required_pkgs()
was extended forparsnip
objects.Prediction functions now give a consistent error when a user uses an unavailable value of
type
(#489)The
augment()
method was changed to avoid failing if the model does not enable class probabilities. The method now returns tibbles despite the input data class (#487) (#478)xgboost engines now respect the
event_level
option for predictions (#460).
parsnip 0.1.5
CRAN release: 20210119
An RStudio addin is available that makes writing multiple
parsnip
model specifications to the source window. It can be accessed via the IDE addin menus or by callingparsnip_addin()
.For
xgboost
models, users can now passobjective
toset_engine("xgboost")
. (#403)Changes to test for cases when CRAN cannot get
xgboost
to work on their Solaris configuration.There is now an
augument()
method for fitted models. Seeaugment.model_fit
. (#401)Column names for
x
are now required whenfit_xy()
is used. (#398)There is now an
event_level
argument for thexgboost
engine. (#420)New mode “censored regression” and new prediction types “linear_pred”, “time”, “survival”, “hazard”. (#396)
Censored regression models cannot use
fit_xy()
(usefit()
). (#442)
parsnip 0.1.4
CRAN release: 20201027
show_engines()
will provide information on the current set for a model.For three models (
glmnet
,xgboost
, andranger
), enable sparse matrix use viafit_xy()
(#373).Some added protections were added for function arguments that are dependent on the data dimensions (e.g.,
mtry
,neighbors
,min_n
, etc). (#184)Infrastructure was improved for running
parsnip
models in parallel using PSOCK clusters on Windows.
parsnip 0.1.2
CRAN release: 20200703
Breaking Changes

parsnip
now has options to set specific types of predictor encodings for different models. For example,ranger
models run usingparsnip
andworkflows
do the same thing by not creating indicator variables. These encodings can be overridden using theblueprint
options inworkflows
. As a consequence, it is possible to get a different model fit that previous versions ofparsnip
. More details about specific encoding changes are below. (#326)
Other Changes
tidyr
>= 1.0.0 is now required.SVM models produced by
kernlab
now use the formula method (see breaking change notice above). This change was due to howksvm()
made indicator variables for factor predictors (with onehot encodings). Since the ordinary formula method did not do this, the data are passed asis toksvm()
so that the results are closer to what one would get ifksmv()
were called directly.MARS models produced by
earth
now use the formula method.For
xgboost
, a onehot encoding is used when indicator variables are created.Underthehood changes were made so that nonstandard data arguments in the modeling packages can be accommodated. (#315)
New Features
A new main argument was added to
boost_tree()
calledstop_iter
for early stopping. Thexgb_train()
function gained arguments for early stopping and a percentage of data to leave out for a validation set.If
fit()
is used and the underlying model uses a formula, the actual formula is pass to the model (instead of a placeholder). This makes the model call better.A function named
repair_call()
was added. This can help change the underlying modelscall
object to better reflect what they would have obtained if the model function had been used directly (instead of viaparsnip
). This is only useful when the user chooses a formula interface and the model uses a formula interface. It will also be of limited use when a recipes is used to construct the feature set inworkflows
ortune
.The
predict()
function now checks to see if required modeling packages are installed. The packages are loaded (but not attached). (#249) (#308) (tidymodels/workflows#45)The function
req_pkgs()
is a user interface to determining the required packages. (#308)
parsnip 0.0.5
CRAN release: 20200107
Other Changes
glmnet
was removed as a dependency since the new version depends on 3.6.0 or greater. Keeping it would constrainparsnip
to that same requirement. Allglmnet
tests are run locally.A set of internal functions are now exported. These are helpful when creating a new package that registers new model specifications.
New Features

nnet
was added as an engine tomultinom_reg()
#209
Breaking Changes
 There were some mismapped parameters (going between
parsnip
and the underlying model function) forspark
boosted trees and somekeras
models. See 897c927.
parsnip 0.0.4
CRAN release: 20191102
New Features
The time elapsed during model fitting is stored in the
$elapsed
slot of the parsnip model object, and is printed when the model object is printed.Some default parameter ranges were updated for SVM, KNN, and MARS models.
The model
udpate()
methods gained aparameters
argument for cases when the parameters are contained in a tibble or list.fit_control()
is softdeprecated in favor ofcontrol_parsnip()
.
Fixes
A bug was fixed standardizing the output column types of
multi_predict
andpredict
formultinom_reg
.A bug was fixed related to using data descriptors and
fit_xy()
.A bug was fixed related to the column names generated by
multi_predict()
. The toplevel tibble will always have a column named.pred
and this list column contains tibbles across submodels. The column names for these submodel tibbles will have names consistent withpredict()
(which was previously incorrect). See 43c15db.A bug was fixed standardizing the column names of
nnet
class probability predictions.
parsnip 0.0.3
CRAN release: 20190731
Unplanned release based on CRAN requirements for Solaris.
Breaking Changes
The method that
parsnip
stores the model information has changed. Any custom models from previous versions will need to use the new method for registering models. The methods are detailed in?get_model_env
and the package vignette for adding models.The mode needs to be declared for models that can be used for more than one mode prior to fitting and/or translation.
For
surv_reg()
, the engine that uses thesurvival
package is now calledsurvival
instead ofsurvreg
.For
glmnet
models, the full regularization path is always fit regardless of the value given topenalty
. Previously, the model was fit with passingpenalty
toglmnet
’slambda
argument and the model could only make predictions at those specific values. (#195)
New Features
add_rowindex()
can create a column called.row
to a data frame.If a computational engine is not explicitly set, a default will be used. Each default is documented on the corresponding model page. A warning is issued at fit time unless verbosity is zero.
nearest_neighbor()
gained amulti_predict
method. Themulti_predict()
documentation is a little better organized.A suite of internal functions were added to help with upcoming model tuning features.
A
parsnip
object always saved the name(s) of the outcome variable(s) for proper naming of the predicted values.
parsnip 0.0.2
CRAN release: 20190322
Small release driven by changes in sample()
in the current rdevel.
New Features
A “null model” is now available that fits a predictorfree model (using the mean of the outcome for regression or the mode for classification).
fit_xy()
can take a single column data frame or matrix fory
without error
Other Changes
varying_args()
now has afull
argument to control whether the full set of possible varying arguments is returned (as opposed to only the arguments that are actually varying).fit_control()
not returns an S3 method.For classification models, an error occurs if the outcome data are not encoded as factors (#115).
The prediction modules (e.g.
predict_class
,predict_numeric
, etc) were deexported. These were internal functions that were not to be used by the users and the users were using them.An event time data set (
check_times
) was included that is the time (in seconds) to runR CMD check
using the “rdevelwindowsix86+x86_64` flavor. Packages that errored are censored.
Bug Fixes
varying_args()
now uses the version from thegenerics
package. This means that the first argument,x
, has been renamed toobject
to align with generics.For the recipes step method of
varying_args()
, there is now error checking to catch if a user tries to specify an argument that cannot be varying as varying (for example, theid
) (#132).find_varying()
, the internal function for detecting varying arguments, now returns correct results when a size 0 argument is provided. It can also now detect varying arguments nested deeply into a call (#131, #134).For multinomial regression, the
.pred_
prefix is now only added to prediction column names once (#107).For multinomial regression using glmnet,
multi_predict()
now pulls the correct default penalty (#108).Confidence and prediction intervals for logistic regression were only computed the intervals for a single level. Both are now computed. (#156)
parsnip 0.0.0.9005
 The engine, and any associated arguments, are now specified using
set_engine()
. There is noengine
argument
parsnip 0.0.0.9004
 Arguments to modeling functions are now captured as quosures.

others
has been replaced by...
 Data descriptor names have been changed and are now functions. The descriptor definitions for “cols” and “preds” have been switched.
parsnip 0.0.0.9003

regularization
was changed topenalty
in a few models to be consistent with this change.  If a mode is not chosen in the model specification, it is assigned at the time of fit. 51
 The underlying modeling packages now are loaded by namespace. There will be some exceptions noted in the documentation for each model. For example, in some
predict
methods, theearth
package will need to be attached to be fully operational.
parsnip 0.0.0.9002
 To be consistent with
snake_case
,newdata
was changed tonew_data
.  A
predict_raw
method was added.
parsnip 0.0.0.9000
 The
fit
interface was previously used to cover both the x/y interface as well as the formula interface. Now,fit()
is the formula interface andfit_xy()
is for the x/y interface.  Added a
NEWS.md
file to track changes to the package. 
predict
methods were overhauled to be consistent.  MARS was added.