funcml

In funcml: Functional Machine Learning Framework

knitr::opts_chunk$set(collapse = TRUE, comment = "#>")
library(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 to fit() is the exact data being modeled

• the 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, superlearner

• regression and binary classification: earth, gam, bart

• classification only: C50, naivebayes, fda, lda, qda

• binary classification only: adaboost

• regression 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

tune_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

Nested 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

interaction_obj <- interpret(fit_obj, mtcars, method = "interaction")
plot(interaction_obj)

learners()

plot(permute_obj)
plot(pdp_obj)
plot(ale_obj)
plot(local_obj)
plot(shap_obj, kind = "waterfall")
plot(profile_obj)
plot(surrogate_obj)

funcml documentation built on Aug. 22, 2026, 5:08 p.m.