funcml
funcml provides a machine learning framework
for R. The package also includes native interpretability helpers for
permutation importance, partial dependence, ICE, ALE, local surrogate
explanations, SHAP, interaction strength, greedy breakdown profiles, and
global surrogate models.
It also exposes native ensemble learners through
model = "stacking" and
model = "superlearner".
The package is intentionally opinionated. It is not designed to
compete feature for feature with broader frameworks such as
tidymodels, mlr3, or caret.
Instead, funcml focuses on a compact and explicit framework
for users who want to fit, compare, tune, interpret, and estimate models
through one coherent interface, with formula syntax as the main
model-specification surface.
This design comes with a deliberate tradeoff:
preprocessing is expected to happen outside
funcml, so the data passed tofit()is the exact data being modeledthe package favors explicit inputs and direct model specification over hidden preprocessing state inside training pipelines
cross-validation is still the default, but the package also supports holdout splits, grouped CV, and time-aware rolling evaluation through the same resampling interface
Users who need broader preprocessing orchestration or specialized resampling designs should use a more complete framework.
Learner coverage currently includes:
regression and classification:
glm,rpart,glmnet,ranger,nnet,e1071_svm,randomForest,gbm,kknn,ctree,cforest,lightgbm,xgboost,stacking,superlearnerregression and binary classification:
earth,gam,bartclassification only:
C50,naivebayes,fda,lda,qdabinary classification only:
adaboostregression only:
pls
fit_obj <- fit(mpg ~ wt + hp, data = mtcars, model = "ranger")
permute_obj <- interpret(fit_obj, mtcars, method = "permute", nsim = 5)
pdp_obj <- interpret(fit_obj, mtcars, method = "pdp", features = "wt")
ale_obj <- interpret(fit_obj, mtcars, method = "ale", features = "wt")
local_obj <- interpret(fit_obj, mtcars, method = "local_model", newdata = mtcars[1, , drop = FALSE], k = 2)
shap_obj <- interpret(fit_obj, mtcars, method = "shap", newdata = mtcars[1, , drop = FALSE], nsim = 20)
profile_obj <- interpret(fit_obj, mtcars, method = "profile", newdata = mtcars[1, , drop = FALSE])
surrogate_obj <- interpret(fit_obj, mtcars, method = "surrogate")eval_obj <- evaluate(mpg ~ wt + hp, data = mtcars, model = "glm", resampling = cv(5))
eval_obj
#> <funcml_eval> model: glm | task: regression
#> metric mean sd n std_error conf_level conf_low conf_high
#> 1 rmse 2.6832 1.0347 5 0.4627 0.95 1.3984 3.9680
#> 2 mae 2.1913 0.8367 5 0.3742 0.95 1.1525 3.2302
#> 3 mse 8.0560 5.5846 5 2.4975 0.95 1.1219 14.9902
#> 4 medae 1.9705 0.9344 5 0.4179 0.95 0.8102 3.1307
#> 5 mape 0.1150 0.0567 5 0.0254 0.95 0.0446 0.1855
#> 6 rsq 0.1750 1.2412 5 0.5551 0.95 -1.3661 1.7162tune_grid <- expand.grid(intercept = c(TRUE, FALSE))
tune_obj <- tune(mpg ~ wt + hp, data = mtcars, model = "glm", grid = tune_grid, search = "random", n_evals = 1, resampling = cv(v = 3, seed = 1), seed = 1)
tune_obj
#> <funcml_tune> metric=rmse direction=min search=random
#> Best:
#> intercept mean sd n std_error conf_level conf_low conf_high
#> 1 TRUE 2.7726 0.9774 3 0.5643 0.95 0.3445 5.2007Nested CV is available through outer_resampling. The
inner resampling argument still selects the best
configuration, and the outer resampling loop provides an unbiased
estimate of tuned model-selection performance.
nested_tune_obj <- tune(
mpg ~ wt + hp,
data = mtcars,
model = "glm",
grid = tune_grid,
resampling = cv(v = 3, seed = 1),
outer_resampling = cv(v = 4, seed = 2),
metric = "rmse",
seed = 1
)
nested_tune_obj
#> <funcml_tune> metric=rmse direction=min search=grid
#> Best:
#> intercept mean sd n std_error conf_level conf_low conf_high
#> 1 TRUE 2.7726 0.9774 3 0.5643 0.95 0.3445 5.2007
#> Nested resampling:
#> metric mean sd n std_error conf_level conf_low conf_high
#> 1 rmse 2.8862 1.0851 4 0.5425 0.95 1.1596 4.6128learners()
#> [1] "glm" "rpart" "glmnet" "ranger" "nnet"
#> [6] "mlp" "densemlp" "e1071_svm" "randomForest" "gbm"
#> [11] "C50" "kknn" "earth" "gam" "naivebayes"
#> [16] "fda" "adaboost" "pls" "ctree" "cforest"
#> [21] "lda" "qda" "lightgbm" "bart" "xgboost"
#> [26] "fastgbm" "stacking" "superlearner"
