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fabletools

The R package fabletools provides tools for building modelling packages, with a focus on time series forecasting. This package allows package developers to extend fable with additional models, without needing to depend on the models supported by fable.

Installation

You could install the stable version on CRAN:

install.packages("fabletools")

You can install the development version from GitHub:

# install.packages("remotes")
remotes::install_github("tidyverts/fabletools")

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Version

Install

install.packages('fabletools')

Monthly Downloads

15,111

Version

0.6.0

License

GPL-3

Issues

Pull Requests

Stars

Forks

Repository

https://github.com/tidyverts/fabletools

Homepage

https://fabletools.tidyverts.org/

Maintainer

Mitchell O'Hara-Wild

Last Published

February 9th, 2026

Functions in fabletools (0.6.0)

Construct a new set of forecasts

distribution_var

Return distribution variable

Create a dable object

decomposition_model

Decomposition modelling

Internal vctrs methods

fabletools-package

fabletools: Core Tools for Packages in the 'fable' Framework

Create a feature set from tags

interpolate.mdl_df

Interpolate missing values

autoplot.dcmp_ts

Decomposition plots

components.mdl_df

Extract components from a fitted model

Is the object a dable

Create a fable object

Augment a mable

Is the object a mable

Is the object a model

features_by_tag

Features by tag

Extract fitted values from models

Internal vctrs methods

hypothesize.mdl_df

Run a hypothesis test from a mable

Common exogenous regressors

Box Cox Transformation

Extract the left hand side of a model

generate.mdl_df

Generate responses from a mable

Extract features from a dataset

Estimate a model

features_by_pkg

Features by package

Provide a succinct summary of a model

Is the object a fable

new_model_class

Create a new class of models

Internal vctrs methods

Create a new mable

parse_model_rhs

Parse the RHS of the model formula for specials

Point estimate accuracy measures

Extract the response variable from a model

Return response variables

validate_formula

Validate the user provided model

Extract the right hand side of a model

Middle out forecast reconciliation

Return model column variables

Parse the model specification for specials

combination_ensemble

Ensemble combination

Extract frequencies for common seasonal periods

forecast.mdl_df

Produce forecasts

Estimate models

Is the element an aggregation of smaller data

Minimum trace forecast reconciliation

Objects exported from other packages

parse_model_lhs

Parse the RHS of the model formula for transformations

percentile_score

Distribution accuracy measures

Forecast reconciliation

Directional accuracy measures

Interval estimate accuracy measures

Refit a mable to a new dataset

new_transformation

Create a new modelling transformation

Create evaluation environment for specials

Identify outliers

A set of future scenarios for forecasting

Forecast skill score measure

Unpack a hilo column

Helper special for producing a model matrix of exogenous regressors

Extend a fitted model with new data

Recursively traverse an object

register_feature

Register a feature function

Report information about an object

residuals.mdl_df

Extract residuals values from models

Extract model coefficients from a mable

Top down forecast reconciliation

Coerce to a dable object

aggregate_index

Expand a dataset to include temporal aggregates

Coerce a dataset to a mable

accuracy.mdl_df

Evaluate accuracy of a forecast or model

Coerce to a fable object

Mean Arctangent Absolute Percentage Error

Create an aggregation vector

Expand a dataset to include other levels of aggregation

aggregation-vctrs

Internal vctrs methods

Compute Impulse Response Function (IRF)

autoplot.fbl_ts

Plot a set of forecasts

autoplot.tbl_ts

Plot time series from a tsibble

combination_weighted

Weighted combination

combination_model

Combination modelling

Bottom up forecast reconciliation

Bias adjust back-transformation functions