Fast, Low-Dependency Machine Learning Algorithms • roadrunner

Fast, low-dependency machine learning algorithms in R.

roadrunner ships C++ backed implementations of classical ML algorithms with thin, base-R-style interfaces. Two algorithms today:

ares() – Multivariate Adaptive Regression Splines (MARS).
krls() – Kernel Regularized Least Squares (KRLS).

Package design

Low dependency. Only Rcpp, RcppArmadillo, and RcppParallel at runtime. No tidyverse, no rlang, no S4.
Fast. C++ engines via Rcpp with multi-core scoring via RcppParallel.
Deterministic. Fits are bit-for-bit identical across thread counts at a fixed seed.
Familiar API. Base-R style: formula and matrix interfaces and standard S3 methods (predict, print, summary, plot).

Install

# install.packages("pak")
pak::pak("CetiAlphaFive/roadrunner")

Usage

MARS via `ares()`

library(roadrunner)

# Regression
fit <- ares(mpg ~ ., data = mtcars, degree = 2)
predict(fit, head(mtcars))

# Classification
y <- as.integer(mtcars$am)
fitc <- ares(as.matrix(mtcars[, -9]), y, family = "binomial")
predict(fitc)

# Hands-free hyperparameter tuning
fitt <- ares(mpg ~ ., data = mtcars, autotune = TRUE,
             autotune.speed = "fast", seed.cv = 1L)
fitt$autotune$degree

See the ares vignette for autotune, classification, weights, bagging, and prediction-interval examples.

KRLS via `krls()`

krls() fits a Kernel Regularized Least Squares model (Hainmueller and Hazlett 2014) with closed-form leave-one-out selection of the ridge penalty and per-observation marginal effects.

set.seed(1)
n <- 200
X <- matrix(rnorm(n * 3), n, 3)
y <- sin(X[, 1]) + 0.5 * X[, 2]^2 - 0.3 * X[, 3] + rnorm(n, sd = 0.2)

fit <- krls(X, y)             # default sigma = ncol(X); lambda by LOO
fit$avgderivatives            # average marginal effects per variable
predict(fit, X)$fit           # in-sample fitted values

# Predict on new data with pointwise SEs
Xnew <- matrix(rnorm(20 * 3), 20, 3)
pr <- predict(fit, Xnew, se.fit = TRUE)
head(pr$fit); head(pr$se.fit)

krls() mirrors KRLS::krls() numerically (fits agree to ~1e-13 at matched sigma and lambda) and is 6-10x faster on benchmarks at n >= 500.

References

Friedman, J. H. (1991). Multivariate Adaptive Regression Splines. Annals of Statistics, 19(1):1-67.
Hainmueller, J. and Hazlett, C. (2014). Kernel Regularized Least Squares: Reducing Misspecification Bias with a Flexible and Interpretable Machine Learning Approach. Political Analysis, 22(2):143-168.

License

MIT (c) Jack T. Rametta

roadrunner