Skip to content

Bayesian Exponential Smoothing Models with Trend Modifications

0packages dependsuggested by 1
8.8Kdownloads / year
–test coverage
13/13flavors OK

Overview

About

An implementation of a number of Global Trend models for time series forecasting that are Bayesian generalizations and extensions of some Exponential Smoothing models. The main differences/additions include 1) nonlinear global trend, 2) Student-t error distribution, and 3) a function for the error size, so heteroscedasticity. The methods are particularly useful for short time series. When tested on the well-known M3 dataset, they are able to outperform all classical time series algorithms. The models are fitted with MCMC using the 'rstan' package.

Install

Health

CRAN checks
13OK
Slowest check: 46.8 min · r-oldrel-windows-x86_64
Status changes
  • OK13 OK2026-08-05
  • NOTE12 OK, 1 NOTE2026-08-01
  • OK13 OK2026-07-29
  • ERROR12 OK, 1 ERROR2026-07-27
  • OK13 OK2026-06-09
Show 5 earlier changesHide 5 earlier changes
  • ERROR12 OK, 1 ERROR2026-06-08
  • OK12 OK2026-04-25
  • NOTE11 OK, 3 NOTE2026-04-22
  • ERROR10 OK, 3 NOTE, 1 ERROR2026-04-18
  • NOTE11 OK, 3 NOTE2026-03-10
Advisories

none known

Documentation

In release 0.2-3
READMENoVignettesYes · 2 · dynamicNEWSNoCitation fileNoWebsitenot declared in DESCRIPTION
Help pages
11includes a package overview page
Examples that run
75%3 run in a plain check · 1 never run
Exported functions with examples
50%
Documented parameters
100%
Return-value docs
100%
Help pages citing references
18%

Also includes 17 demo scripts (run with demo())

Downloads

8.8K
CRAN downloads in the past year
Rank #3,667 · ~24/day · ~737/mo
Daily download trend is not available in this view yet.
1.8K30 days
2.7K90 days
8.8K1 year
Compare downloads with other packages →
Also on377 r2u3 autocran2.4K conda_forge136 c2d4u

Repository

23Stars
11Forks
2Open issues
0Open PRs
0Releases
296Commits
11Commit identities
296 commits · Last activity 2025-04-19 · 0% stars, 30d
Responsiveness
How these are sampled
PRs merged (all time)
100%
Median days to close, 50 most recently updated closed issues
0
Median days to close, 50 most recently updated closed PRs
3

Stars over time

2025-06-04 · 222026-07-07 · 23
PracticesBeta

2 development-tooling and community-health practices detected across 1 family in the upstream repository

Checks run against github.com/cbergmeir/rlgt on 2026-10-11.

Reproducibility and dev environment (2)
data-raw/Makefile
How this is detected·Detection ruleset v3 (2026-09-26)

Dependencies

Declared dependencies
20 external dependencies (excludes base and recommended)

Installing Rlgt adds 62 packages (33 with compiled code, 51 MB of source tarballs). Heaviest: BH 14 MB, ggplot2 6.0 MB, loo 2.7 MB.

Depends (6)
Imports (2)
Enhances (0)
none
Reverse dependencies
0direct
0indirect
Suggested by 1
Suggests, most downloaded first

Datasets

People & History

People (8)
Author, Maintainer since 0.1-2 (2019)
Author · added in 0.2-0
Author · added in 0.2-0
Author · added in 0.2-0
Copyright holder
tools/make_cpp.R, R/stanmodels.R
Releases (7)

Pick two releases to compare their code metrics.

  • R
    R 4.6.0 released · 2026-04-24
  • 0.2-3Latest
    record
    2025-04-30 · current release · diff ↗
  • R
    R 4.5.0 released · 2025-04-11
  • 0.2-2
    record
    2024-07-16 · diff ↗ ·
    added 1 export
    blgt.multi.forecast
  • R
    R 4.4.0 released · 2024-04-24
  • 0.2-1
    record
    2023-09-16 · diff ↗
  • 0.2-0
    record
    2023-08-30 · diff ↗
  • R
    R 4.3.0 released · 2023-04-21
  • 0.1-4
    record
    2022-05-17 · diff ↗
  • R
    R 4.2.0 released · 2022-04-22
  • R
    R 4.1.0 released · 2021-05-18
  • R
    R 4.0.0 released · 2020-04-24
  • 0.1-3
    record
    2019-06-14 · diff ↗
  • R
    R 3.6.0 released · 2019-04-26
  • 0.1-2
    record
    2019-02-22
  • R
    R 3.5.0 released · 2018-04-23

Package metadata

First published
2019-02-22
Total releases
7 / 7 yrs
License
GPL-3 OSI
Minimum R
≥ 3.4.0
Bundled data
10 KB / 2 files
Download size
372 KB
Appears in task views

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("Rlgt")
Bergmeir, C., Trustees of Columbia University, Dokumentov, A., Long, X., Ng, T. W., Schmidt, D., Smyl, S., & Wibowo, E. (2025). Rlgt: Bayesian Exponential Smoothing Models with Trend Modifications (Version 0.2-3) [Computer software]. https://doi.org/10.32614/CRAN.package.Rlgt

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.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for Rlgt version 0.2-3 [Data set]. HJJB, LLC. Data release v2026-10-19. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-19, which the citation names so these numbers can be found later. More on citing and the projects behind them.

Report a problem with this page →

Privacy choices

These apply to this browser and are stored on this device only. Nothing about your choice is sent to us.

Read the privacy policy