News

Version Updates

2.5.0

• Add options argument to step_lincomp() and step_sbf().
• CRAN release.

2.4.3

• Add recipe step_sbf() function for variable selection by filtering.
• Inherit step_kmedoids objects from step_sbf, and refactor methods.
  • Support user-specified center and scale functions.
  • Append prefix to selected variable names.
  • Rename tidy() column medoids to selected.
  • Rename tidy() column names to name.
  • Set tidy() non-selected variable names to NA.
• Add recipe step_lincomp() function for linear components variable reduction.
• Inherit step_kmeans objects from step_lincomp, and refactor methods.
  • Support user-specified center and scale functions.
  • Rename tidy() column names to name.
• Inherit step_spca objects from step_lincomp, and refactor methods.
  • Support user-specified center and scale functions.
  • Rename tidy() column value to weight.
  • Rename tidy() column component to name.
• Set GBMModel distribution to bernoulli, instead of multinomial, for binary responses.

2.4.2

• Add global setting RHS.formula for listing of operators and functions allowed on right-hand side of traditional formulas.
• Add clara clustering method to step_kmedoids().
• Support Cox and accelerated failure time regression for survival responses in XGBModel, XGBDARTModel, XGBLinearModel, and XGBTreeModel.

2.4.1

• Set NNetModel linout argument automatically according to the response variable type (numeric: TRUE, other: FALSE). Previously, linout had a default value of FALSE as defined in the nnet package.

2.4.0

• CRAN release.

2.3.2

• Display progress bars for sequential resampling iterations.

2.3.1

• R 4.0 data.frame compatibility updates for calibration curves.
• Fix recipe prediction with StackedModel and SuperModel

2.3.0

• Display progress messages for any foreach parallel backend.

2.2.5

• Show all error messages when resample selection stops.
• Preserve predictor names in NNetModel fit() method.
• Fix aggregation of performance curves with infinite values.
• Add progress bar and verbose output options for resample() methods.
• Get non-negative probabilities for survival confusion matrix.
• Update Using webpages and vignette.

2.2.4

• Fix BARTMachineModel to predict highest binary response level.
• Grid tune BARTMachineModel nu parameter for numeric responses only.

2.2.3

• Extend ModeledInput() to SelectedModelFrame, SelectedModelRecipe, and TunedModelRecipe.

2.2.2

• Fix updating of recipe parameters in TunedInput().

2.2.1

• Print StackedModel and SuperModel training information.
• Fix missing case names when resampling with recipes.

2.2.0

• CRAN release.

2.1.4

• Add cost-complexity pruning parameters to TreeModel.
• Perform stratified resampling automatically for ModeledInput() and SelectedInput() objects constructed with formulas and matrices.

2.1.3

• Revisions needed to some fit() methods to ensure that unprepped recipes are passed to models, like TunedModed, StackedModel, SelectedModel and SuperModel, needing to replicate preprocessing steps in their resampling routines.
• Extend GLMModel to factor and matrix responses.
• Use fun instead of deprecated fun.y in ggplot2 functions.
• Capture user-supplied parameters passed in to the ellipsis of model constructor functions that have them.

2.1.2

• Compatibility fix for tibble 3.0.0.
• Include missing values in model matrices created internally from formulas.

2.1.1

• Improve specificity of metricinfo() results for factor responses.
• Correct SplitControl() to train on the split sample instead of the full dataset.
• Perform stratified resampling automatically when fit() formula and matrix methods are called with meta-models.

2.1.0

• CRAN release.

2.0.4

• Extend print() argument n to data frame and matrix columns for more concise display of large data structures.
• Add preprocessing recipe functions step_kmeans(), step_kmedoids(), and step_spca().

2.0.3

• Internal changes:
  • Remove MLModel slot y.
  • Rename ModelFrame and ModelRecipe columns (casenames) to (names).
  • Register ModelFrame inheritance from data.frame.
  • Define Terms S4 classes for ModelFrame slot terms.

2.0.2

• Implement ModeledInput, SelectedInput and TunedInput classes and methods.
• Deprecate SelectedFormula(), SelectedMatrix(), SelectedModelFrame(), SelectedRecipe(), and TunedRecipe().
• Remove deprecated tune().
• Rename global setting stat.Curves to stat.Curve.

2.0.1

• Rename global setting stat.Train to stat.train.
• Add print methods for SelectedModel, StackedModel, SuperModel, and TunedModel.
• Revise training methods to ensure nested resampling of SelectedRecipe and TunedRecipe.
• Return list of all training steps in MLModel trainbits slot.

2.0.0

• Rename global setting stat.Tune to stat.Train.
• Enable selection of formulas, design matrices, and model frames with SelectedFormula(), SelectedMatrix(), and SelectedModelFrame().
• Rename discrete variable classes: BinomialMatrix → BinomialVariate, DiscreteVector → DiscreteVariate, NegBinomialVector → NegBinomialVariate, and PoissonVector → PoissonVariate.
• Add global setting require for user-specified packages to load during parallel execution of resampling algorithms.
• Rename recipe role case_strata to case_stratum.
• Rename object argument to data in ConfusionMatrix(), SurvEvents(), and SurvProbs().
• Add c methods for BinomialVariate, DiscreteVariate, ListOf, and SurvMatrix.
• Add role_binom(), role_case(), role_surv(), and role_term() to set recipe roles.
• Support base argument to varimp() for log-transformed p-values.
• Rename ParamSet to ParameterGrid.
• Add option to reset global settings individually.
• Add as.data.frame methods for Performance, Performance summary, PerformanceDiff, PerformanceDiffTest, and Resamples.

1.99.0

• Implement DiscreteVector class and subclasses BinomialVector, NegBinomialVector, and PoissonVector for discrete response variables.
• Extend model support to DiscreteVector classes as follows.
  • DiscreteVector: all models applicable to numeric responses.
  • BinomialVector/NegBinomialVector/PoissonVector: BlackBoostModel, GAMBoostModel, GLMBoostModel, GLMModel, and GLMStepAICModel.
  • BinomialVector/PoissonVector: GLMNetModel.
  • PoissonVector: GBMModel and XGBModel
• Add support for offset terms in formulas, model matrices, and recipes.
• Add recipe tune information to fitted MLModel.
• Replace Calibration(), Confusion(), Curves(), Lift(), and Resamples() with c methods.
• Redefine Confusion S3 class as ConfusionList S4 class.
• Remove support for one-element list to metricinfo() and modelinfo().
• Remove deprecated expand.model().
• Expire deprecated tune().

1.6.4

• Calculate regression variable importance as negative log p-values.
• Support empty vectors in metricinfo() and modelinfo().
• Add support for dials package parameter sets with ParamSet().

1.6.3

• Add as.MLModel() for coercing MLModelFit to MLModel.
• Deprecate tune(); call fit() with a SelectedModel or TunedModel instead.

1.6.2

• Implement optimism-corrected cross-validation (CVOptimismControl).
• Fix BootOptimismControl error with 2D responses.
• Add global option max.print for the number of models and data frame rows to show with print methods.
• Enable recipe selection with SelectedRecipe().
• Refactor tune() methods.
• Replace MLModelFit element fitbits (MLFitBits object) with mlmodel (MLModel object).
• Rename VarImp slot center to shift.

1.6.1

• Use tibbles for parameter grids.
• Add random sampling option to expand_model(), expand_params(), and expand_steps().
• Display information for model functions and objects more compactly.

1.6.0

• Add global setting for default cutoff threshold value.
• Add option to reset all global settings.
• Enable recipe tuning with TunedRecipe().
• Add expand_model() for model expansion over tuning parameters.
• Add expand_params() for model parameters expansion.
• Add expand_steps() for recipe step parameters expansion.
• Implement MLModelFunction and MLModelList classes.
• Add fit methods for MLModel, MLModelFunction, and MLModelList.
• Fix NNetModel fit error with binary and factor responses.
• Fix modelinfo() function not found error.

1.5.2

• Implement exception handling of tune() resampling failures.
• Remove deprecated types and design arguments from MLModel().

1.5.1

• Implement global settings for default resampling control, performance metrics, summary statistics, and tuning grid.
• Support vector arguments in metricinfo() and modelinfo().
• Update package documentation.

1.5.0

• Implement model: SelectedModel.
• Remove maximize argument from tune() and TunedModel.
• Support lists as arguments to StackedModel() and SuperModel.

1.4.2

• Revert renaming of expand.model().
• Exclude 0 distance from KNNModel tuning grid.
• Improve random tuning grid coverage.

1.4.1

• Implement model: TunedModel.
• Remove deprecated na.action argument from ModelFrame methods.
• Rename MLModel() argument types to response_types.
• Rename MLModel() argument design to predictor_encoding.
• Rename expand.model() to expand_model().

1.4.0

• CRAN release.

1.3.3

• Implement optimism-corrected bootstrap resampling (BootOptimismControl).
• Store case names in ModelFrame and ModelRecipe and save to Resamples.

1.3.2

• Add BinaryConfusionMatrix and OrderedConfusionMatrix classes.
• Export ConfusionMatrix constructor.
• Extend metricinfo() to confusion matrices.
• Refactor performance metrics methods code.

1.3.1

• Check and convert ordered factors in response methods.
• Check consistency of extracted variables in response methods.
• Add metrics methods for Resamples.

1.3.0

• Improve compatibility with preprocessing recipes.
• Allow base math functions and operators in ModelFrame formulas.

1.2.5

• Save ModelFrame response in first column.
• Unexport response formula method.
• Add ICHomes dataset.
• Add center and scale slot to VarImp.

1.2.4

• Prohibit in-line functions in ModelFrame formulas.
• Rename response function argument from data to newdata.

1.2.3

• Add fit, resample, and tune methods for design matrices.
• Reduce computational overhead for design matrices and recipes.
• Rename ModelFrame() argument na.action to na.rm.

1.2.2

• Implement parametric ("exponential", "rayleigh", "weibull") estimation of baseline survival functions.
• Set "weibull" as the default distribution for survival mean estimation.
• Add extract method for Resamples.
• Add na.rm argument to calibration(), confusion(), performance(), and performance_curve().
• Add loess span argument to calibration().
• Change SurvMatrix from S4 to S3 class.

1.2.1

• Add method option to predict() for Breslow, Efron (default), or Fleming-Harrington estimation of survival curves for Cox proportional hazards-based models.
• Add dist option to predict() for exponential or Weibull approximation to estimated survival curves.
• Add dist option to calibration() for distributional estimation of observed mean survival.
• Add dist option to r2() for distributional estimation of the total sum of squares mean.
• Handle unnamed arguments in metricinfo() and modelinfo().

1.2.0

• Implement metrics: auc, fnr, fpr, rpp, tnr, tpr.
• Implement performance curves, including ROC and precision recall.
• Implement SurvMatrix classes for predicted survival events and probabilities to eliminate need for separate times arguments in calibration, confusion, metrics, and performance functions.
• Add calibration curves for predicted survival means.
• Add lift curves for predicted survival probabilities.
• Add recipe support for survival and matrix outcomes.
• Rename MLControl argument surv_times to times.
• Fix identification of recipe case_weight and case_strata variables.
• Launch package website.
• Bring Introduction vignette up to date with package features.

1.1.0

• Implement model: BARTModel.
• Implement model tuning over automatically generated grids of parameter values and random sampling of grid points.
• Add metrics for predicted survival times: accuracy, f_score, kappa2, npv, ppv, pr_auc, precision, recall, roc_index, sensitivity, specificity
• Add metrics for predicted survival means: cindex, gini, mae, mse, msle, r2, rmse, rmsle.
• Add performance and metric methods for ConfusionMatrix.
• Add confusion matrices for predicted survival times.
• Standardize predict functions to return mean survival when times are not specified.
• Replace MLModel slot and constructor argument nvars with design.

1.0.0

• Implement models: BARTMachineModel, LARSModel.
• Implement performance metrics: gini, multi-class pr_auc and roc_auc, multivariate rmse, msle, rmsle.
• Implement smooth calibration curves.
• Implement MLMetric class for performance metrics.
• Add as.data.frame method for ModelFrame.
• Add expand.model function.
• Add label slot to MLModel.
• Expand metricinfo/modelinfo support for mixed argument types.
• Rename calibration argument n to breaks.
• Rename modelmetrics function to performance.
• Rename ModelMetrics/Diff classes to Performance/Diff.
• Change MLModelTune slot resamples to performance.

0.4.0

• Implement models: AdaBagModel, AdaBoostModel, BlackBoostModel, EarthModel, FDAModel, GAMBoostModel, GLMBoostModel, MDAModel, NaiveBayesModel, PDAModel, RangerModel, RPartModel, TreeModel
• Implement user-specified performance metrics in modelmetrics function.
• Implement metrics: accuracy, brier, cindex, cross_entropy, f_score, kappa2, mae, mse, npv, ppv, pr_auc, precision, r2, recall, roc_auc, roc_index, sensitivity, specificity, weighted_kappa2.
• Add cutoff argument to confusion function.
• Add modelinfo and metricinfo functions.
• Add modelmetrics method for Resamples.
• Add ModelMetrics class with print and summary methods.
• Add response method for recipe.
• Export Calibration constructor.
• Export Confusion constructor.
• Export Lift constructor.
• Extend calibration arguments to observed and predicted responses.
• Extend confusion arguments to observed and predicted responses.
• Extend lift arguments to observed and predicted responses.
• Extend metrics and stats function arguments to accept function names.
• Extend Resamples to arguments with multiple models.
• Change CoxModel, GLMModel, and SurvRegModel constructor definitions so that model control parameters are specified directly instead of with a separate control argument/structure.
• Change predict(..., times = numeric()) function calls to survival model fits to return predicted values in the same direction as survival times.
• Change predict(..., times = numeric()) function calls to CForestModel fits to return predicted means instead of medians.
• Change tune function argument metrics to be defined in terms of a user-specified metric or metrics.
• Deprecate MLControl arguments cutoff, cutoff_index, na.rm, and summary.

0.3.0

• Implement linear models (LMModel), linear discriminant analysis (LDAModel), and quadratic discriminant analysis (QDAModel).
• Implement confusion matrices.
• Support matrix response variables.
• Support user-specified stratification variables for resampling via the strata argument of ModelFrame or the role of "case_strata" for recipe variables.
• Support user-specified case weights for model fitting via the role of "case_weight" for recipe variables.
• Provide fallback for models with undefined variable importance.
• Update the importing of prepper due to its relocation from rsample to recipes.

0.2.0

• Implement partial dependence, calibration, and lift estimation and plotting.
• Implement k-nearest neighbors model (KNNModel), stacked regression models (StackedModel), super learner models (SuperModel), and extreme gradient boosting (XGBModel).
• Implement resampling constructors for training resubstitution (TrainControl) and split training and test sets (SplitControl).
• Implement ModelFrame class for general model formula and dataset specification.
• Add multi-class Brier score to modelmetrics().
• Extend predict() to automatically preprocess recipes and to use training data as the newdata default.
• Extend tune() to lists of models.
• Extent summary() argument stats to functions.
• Fix survival probability calculations in GBMModel and GLMNetModel.
• Change MLControl argument na.rm default from FALSE to TRUE.
• Removed na.rm argument from modelmetrics().

0.1

• Initial public release