greybox is a toolbox for regression model building, selection, and forecasting evaluation. It provides the Augmented Linear Model (ALM) — a flexible regression framework supporting 26 distributions and 7 loss functions — along with stepwise selection, model combination, rolling origin cross-validation, and a comprehensive suite of forecast accuracy measures.

The package exists in two implementations:

• R package (CRAN) — the mature original, actively maintained
• Python package (PyPI) — a port covering core functionality with a scikit-learn-compatible API

See details on Installation page.

The easiest way to install Greybox in R is from CRAN:

install.packages("greybox")

The easiest way to install Greybox in Python is from PyPI:

pip install greybox

install.packages("greybox")
library(greybox)

# Fit a model
model <- alm(y ~ x1 + x2, data=mydata, distribution="dnorm")
summary(model)

# Stepwise selection
best <- stepwise(mydata, ic="AICc", distribution="dnorm")

# Forecast evaluation
ro_result <- ro(y, h=5, origins=10,
                call="predict(alm(y~1, data=data), h=5)")

pip install greybox

import numpy as np
from greybox import ALM, formula, stepwise

# Fit a model
y, X = formula("y ~ x1 + x2", data)
model = ALM(distribution="dnorm")
model.fit(X, y)
print(model.summary())

# Stepwise selection
best = stepwise(data, ic="AICc", distribution="dnorm")

# Forecast evaluation
from greybox.measures import measures
result = measures(actual, forecast, insample)

Python version also includes the standard dataset from the R stats package called mtcars, which is a pandas data frame. It can be imported in Python via:

import from greybox mtcars

R uses camelCase, Python uses snake_case:

• Svetunkov, I. (2023). Statistics for Business Analytics. https://openforecast.org/sba/
• Svetunkov, I. (2023). greybox: Toolbox for Model Building and Forecasting. R package. https://cran.r-project.org/package=greybox