Package index

MARS: ares()

Fast MARS with GCV / CV pruning, autotune, bagging, weights, families, and prediction intervals.

ares()

Fast Multivariate Adaptive Regression Splines

predict(<ares>)

Predictions from an ares fit

print(<ares>)

Print method for ares fits

summary(<ares>) print(<summary.ares>)

Summary method for ares fits

plot(<ares>)

Diagnostic plots for an ares fit

KRLS: krls()

Kernel Regularized Least Squares with closed-form LOO lambda selection and marginal effects.

krls() predict(<krls_rr>)

Kernel Regularized Least Squares

plot(<krls_rr>)

Diagnostic plot for KRLS fits

slim_krls()

Strip heavy fields off a KRLS fit

unslim_krls()

Undo slim_krls() (placeholder)

get_landmarks()

Extract landmark coordinates from a Nystrom krls fit

Causal ensemble: meep()

Cross-fitted, stacked ensemble of the package’s base learners (and optionally ranger/dbarts) for DML and causal-forest nuisance estimation.

meep()

Fast cross-fitted causal ensemble

predict(<meep>)

Predict from a fitted meep object

plot(<meep>)

Diagnostic plots for a meep cross-fitted ensemble

Penalized LDA: plda()

Penalized Fisher’s linear discriminant (Witten & Tibshirani 2011) with L1 and fused-lasso penalties, multi-class, and CV autotune.

plda()

Penalized linear discriminant analysis

predict(<plda>)

Predict from a penalized LDA fit

print(<plda>)

Print method for plda fits

summary(<plda>) print(<summary.plda>)

Summary method for plda fits

plot(<plda>)

Projection plot for plda fits

Linear models: ols()

Ordinary and weighted least squares with classical and HC robust standard errors, bagging, and prediction intervals.

ols()

Ordinary and weighted least squares

predict(<ols>)

Predictions from an ols fit

print(<ols>)

Print method for ols fits

summary(<ols>) print(<summary.ols>)

Summary method for ols fits

plot(<ols>)

Diagnostic plots for an ols fit

Boosted GAM: bgam()

Component-wise P-spline gradient boosting (gamboost-style) with weights, bagging, and CV mstop autotune. Gaussian and binomial families.

bgam()

Component-wise P-spline gradient boosting

predict(<bgam>)

Predictions from a bgam fit

print(<bgam>)

Print a bgam fit

summary(<bgam>) print(<summary.bgam>)

Summarise a bgam fit

plot(<bgam>)

Plot a bgam fit

Logistic regression: logreg()

Binary logistic regression by IRLS with classical and HC robust standard errors and bagging.

logreg()

Binary logistic regression

predict(<logreg>)

Predictions from a logreg fit

print(<logreg>)

Print method for logreg fits

summary(<logreg>) print(<summary.logreg>)

Summary method for logreg fits

plot(<logreg>)

Diagnostic plots for a logreg fit

Factor encoding

Standalone factor/categorical encoders (one-hot, sufrep means, integer, auto, manual) with a replayable recipe for predict-time reuse.

rr_encode_factors()

Encode factor / categorical covariates into a numeric matrix

rr_encode_apply()

Replay a stored factor encoding on new data

Package

roadrunner roadrunner-package

roadrunner: Fast, Low-Dependency Machine Learning Algorithms