R Observatory: Metrics for fasster version 0.2.0

Balamuta, James Joseph

doi:10.5281/zenodo.21843040

fasster

0.2.0Package record

Fast Additive Switching of Seasonality, Trend, and Exogenous Regressors

0packages depend

1.9Kdownloads / year

70.4%test coverage

13/13flavors OK

Overview

About

Maintained by Mitchell O'Hara-WildCRAN page ↗GitHub ↗Website ↗Issues ↗

Implementation of the FASSTER (Forecasting with Additive Switching of Seasonality, Trend, and Exogenous Regressors) model for forecasting time series with multiple seasonal patterns. The model combines state space methodology with a switching component in the observation equation to allow flexible modeling of complex seasonal patterns, including time-varying effects and multiple seasonalities.

Changes in 0.2.0

Fast Additive Switching of Seasonality, Trend and Exogenous Regressors

(FASSTER) is a state space model designed for forecasting time series with

multiple seasonal patterns. The model extends traditional state space models

by introducing a switching component to the measurement equation, enabling

flexible modeling of complex seasonal patterns, and time series dynamics with

rapid structural changes.

FASSTER model implementation:
Model specification: Flexible formula interface supporting:
trend() for polynomial trends
season() for seasonal factors
fourier() for trigonometric seasonal terms
ARMA() for autoregressive moving average components

Show all changes in 0.2.0Hide the rest of the changes in 0.2.0

xreg() for exogenous regressors
%S% switching operator for group-specific model structures
%?% conditional operator for time-varying components
Model methods: Full integration with the fable framework:
fitted() and residuals() for model diagnostics
augment() for augmenting data with model estimates
tidy() for extracting coefficients (initial state estimates)
glance() for model summary statistics (AIC, BIC, log-likelihood)
report() for displaying estimated state and observation variances
components() for decomposing fitted values into trend and seasonal components
forecast() for generating predictions
interpolate() for filling missing values
refit() for applying a fitted model to new data with optional re-estimation
stream() for extending models with new observations
Heuristic estimation: Model parameters are estimated using a heuristic approach based on filtering and smoothing to obtain initial state parameters and variance estimates.

From NEWS.md in the release

Install

R (CRAN)pak (dev)Ubuntu (r2u)Fedora (COPR)openSUSE (OBS)

install.packages("fasster", repos = "https://cloud.r-project.org/")

Installs the current CRAN release. Prebuilt binaries on Windows and macOS.

if (!requireNamespace("pak", quietly = TRUE))
  install.packages("pak", repos = "https://cloud.r-project.org/")
pak::pak("tidyverts/fasster")

Development version from tidyverts/fasster. Installs pak first if it is not already available.

Release

# enable cranapt (r2u) if it is not already set up, then install
grep -rqs r2u.stat.illinois.edu /etc/apt/sources.list /etc/apt/sources.list.d/ \
  || curl -fsSL https://raw.githubusercontent.com/eddelbuettel/r2u/master/inst/scripts/add_cranapt_noble.sh | sudo bash
sudo apt install -y r-cran-fasster

Fast Ubuntu binaries via r2u (no compilation). The cranapt setup runs once and is skipped when it is already enabled. We list this channel from recorded r2u download activity rather than from a live index of the repository, so treat it as last known rather than guaranteed.

bspm (used by r2u) also needs make: sudo apt install make

# enable the iucar/cran COPR if it is not already present, then install
ls /etc/yum.repos.d/*iucar*cran*.repo >/dev/null 2>&1 \
  || sudo dnf copr enable -y iucar/cran
sudo dnf install -y R-CRAN-fasster

RPMs from the iucar/cran COPR, rebuilt from CRAN.

For install.packages() integration in R, also: sudo dnf install R-CoprManager

# add the autoCRAN OBS repo (Tumbleweed) if it is missing, then install
zypper -q lr | grep -qi autoCRAN \
  || sudo zypper addrepo https://download.opensuse.org/repositories/devel:/languages:/R:/autoCRAN/openSUSE_Tumbleweed/devel:languages:R:autoCRAN.repo
sudo zypper install -y R-fasster

RPMs auto-built from CRAN on openSUSE OBS (Tumbleweed). We list this channel from recorded autoCRAN download activity rather than from a live index of the repository, so treat it as last known rather than guaranteed.

Health

CRAN checks

13OK

CRAN check page ↗

Slowest check: 2.8 min · r-oldrel-macos-x86_64

Status changes

OK13 OK2026-08-05
NOTE12 OK, 1 NOTE2026-08-01
OK12 OK2026-04-25
ERROR11 OK, 3 ERROR2026-03-10

Advisories

none known

How this is measured·How advisories are found

Documentation

In release 0.2.0

READMEYes · 468 wordsVignettesYes · 1 · dynamicNEWSYes · 67% structured · mentions 0.2.0Citation fileNoWebsiteYes · pkgdown

Help pages: 12 (1 internal)includes a package overview page
Examples that run: 100%9 run in a plain check (1 of them under a condition)
Exported functions with examples: 100%
Documented parameters: 100%
Return-value docs: 100%
Help pages citing references: 0%

Vignettes (1)

Introduction to FASSTER HTML

How these are measured

Downloads

1.9K

CRAN downloads in the past year

Rank #18,957 · ~5/day · ~161/mo

Releases R versions

Daily download trend is not available in this view yet.

28030 days

69690 days

1.9K1 year

Compare downloads with other packages →

Also on51 r2u5 autocran

How downloads are counted

Repository

tidyverts/fasster ↗Organization: tidyverts

153Stars

15Forks

3Open issues

0Open PRs

1Releases

430Commits

2Commit identities

430 commits · Last activity 2026-02-02 · 0% stars, 30d · Default branch at 0.2.0.9000, ahead of CRAN 0.2.0 (checked 2026-10-21)

Responsiveness

How these are sampled

PRs merged (all time): 100%
Median days to close, 50 most recently updated closed issues: 55
Median days to close, 50 most recently updated closed PRs: 12

Stars over time

2025-08-23 · 1492026-07-07 · 153

How these are measured

PracticesBeta

13 development-tooling and community-health practices detected across 6 families in the upstream repository

Checks run against github.com/tidyverts/fasster on 2026-10-21.

Continuous integration (2)

GitHub ActionsR CMD check workflow

The R CMD check workflow runs on Linux, macOS and Windows, and on R-devel

Governance and community (4)

Code of conductContributing guideIssue templatesSupport guide

Docs source (4)

README.Rmdpkgdown

GitHub Pages site last deployed 2026-01-28 · The repository has a site deploy workflow

pkgdown site last built 2026-01-28, before release 0.2.0 (2026-01-31)

Coverage (1)

Coverage reporting (Codecov)

Show all practicesHide 2 practices

CRAN release process (1)

cran-comments.md

Lint, format, editor (1)

RStudio project

How this is detected·Detection ruleset v4 (2026-10-20)

Dependencies

Declared dependencies

15 external dependencies (excludes base and recommended)

Installing fasster adds 42 packages (22 with compiled code, 49 MB of source tarballs). Heaviest: BH 14 MB, stringi 11 MB, ggplot2 6.0 MB.

Depends (2)

R >= 4.1.0fabletools

Imports (8)

dlm tsibble purrr rlangstatsdplyr (>= 1.0.0)distributional vctrs

LinkingTo (0)

none

Suggests (7)

tsibbledata lubridate knitr rmarkdown testthat spelling covr

Enhances (0)

none

Dependency license breakdown →

Reverse dependencies

0direct

0indirect

Nothing depends on this yet.

How these are counted

Code & Tests

People & History

People (2)

Mitchell O'Hara-Wild

Author, Maintainer since 0.2.0 (2026)

Rob Hyndman

Author, Thesis advisor

Releases (1)

R
R 4.6.0 released · 2026-04-24
0.2.0Latest
record
2026-01-31 · current release
R
R 4.5.0 released · 2025-04-11

Package metadata

First published: 2026-01-31
Total releases: 1 / 1 yrs
License: GPL-3 OSI
Minimum R: ≥ 4.1.0
Download size: 573 KB

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("fasster")

Format

O'Hara-Wild, M., & Hyndman, R. (2026). fasster: Fast Additive Switching of Seasonality, Trend, and Exogenous Regressors (Version 0.2.0) [Computer software]. https://doi.org/10.32614/CRAN.package.fasster

This package has no citation file, so citation() returns this entry built from DESCRIPTION.

Cite the R Observatory

For a number measured here: a download total, a coverage figure, an archival date.

Format

APA

Balamuta, J. J. (2026). R Observatory: Metrics for fasster version 0.2.0 [Data set]. HJJB, LLC. Data release v2026-10-24. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-24, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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