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Sequence Clustering with Discrete-Output HMMs

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2.4Kdownloads / year
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Overview

About

Provides an implementation of a mixture of hidden Markov models (HMMs) for discrete sequence data in the Discrete Bayesian HMM Clustering (DBHC) algorithm. The DBHC algorithm is an HMM Clustering algorithm that finds a mixture of discrete-output HMMs while using heuristics based on Bayesian Information Criterion (BIC) to search for the optimal number of HMM states and the optimal number of clusters.

Changes in 0.0.4

Bug fixes

  • cluster.bic() passes its eps argument on to count.parameters(), it always used the default threshold before
  • hmm.clust() passes its smoothing argument on to assign.clusters(), cluster assignment always smoothed with the default before
  • smooth.probabilities() returns a uniform distribution for a set of probabilities that are all zero, which used to stay zero and is then rejected by build_hmm()
  • smooth.probabilities() no longer returns negative probabilities. Absolute discounting assumes the smoothing parameter is smaller than the smallest non-zero probability, and the amount discounted is now lowered to respect that. A probability below the parameter used to be discounted to a negative value, which survived because the row still sums to one, and grew more negative every time the model was smoothed again. Estimates for data with sparse emissions change, see below
Show all changes in 0.0.4Hide the rest of the changes in 0.0.4
  • select.seeds() stops with a message when too few sequences are left to fill a seed, it used to select NA ids
  • Loops over sequences and cluster models use seq_along(), they could run an iteration on an empty set

Changes

  • Sequences are evaluated against a cluster model all at once in assign.clusters() and select.seeds() instead of one at a time, which makes the algorithm about five times faster on 1000 sequences and leaves all results unchanged
  • hmm.clust() has an argument max.iter and size.search() has an argument max.states to bound the search for cluster memberships and hidden states
  • An infeasible HMM raises a warning instead of printing one
  • The smaller Swiss Household example is fast enough to run on test again
  • Both Swiss Household examples are wrapped in \donttest instead of \dontrun, they are slow rather than impossible to run
  • Results for data with sparse emission probabilities change slightly because of the fix to smooth.probabilities() above. On the simulation study of the accompanying paper nothing changes but the BIC, by 0.0001 percent. On the full Swiss Household data the cluster models and a small number of cluster memberships change, the number of clusters and their sizes do not

Maintenance

  • Replaced Author and Maintainer with Authors@R
  • Dropped LazyData, the package has no data directory
  • Requires seqHMM (>= 2.0.0), the version this release is tested against
  • Heatmaps refer to their data with the .data pronoun, which silences the deprecation warnings from ggplot2

From NEWS.md in the release

Install

Health

CRAN checks
13OK
Slowest check: 3.4 min · r-release-macos-x86_64
Status changes
  • OK13 OK2026-08-05
  • NOTE12 OK, 1 NOTE2026-08-01
  • OK13 OK2026-07-29
  • NOTE11 OK, 2 NOTE2026-06-09
  • ERROR11 OK, 1 NOTE, 1 ERROR2026-06-08
Show 1 earlier changeHide 1 earlier change
  • NOTE12 OK, 2 NOTE2026-03-10
Advisories

none known

Documentation

In release 0.0.4
READMEYes · 123 wordsVignettesNoNEWSYes · 100% structured · mentions 0.0.4Citation fileNoWebsitenot declared in DESCRIPTION
Help pages
13
Examples that run
100%1 run in a plain check
Exported functions with examples
8%
Documented parameters
100%
Return-value docs
85%
Help pages citing references
0%

Downloads

2.4K
CRAN downloads in the past year
Rank #16,231 · ~7/day · ~201/mo
Daily download trend is not available in this view yet.
25230 days
77590 days
2.4K1 year
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Also on82 r2u4 autocran

Repository

1Stars
1Forks
1Open issues
0Open PRs
0Releases
51Commits
1Commit identities
51 commits · Last activity 2026-08-07 · Default branch at 0.0.5, ahead of CRAN 0.0.4 (checked 2026-10-21)
Responsiveness
not measured

Forks over time

2021-10-22 · 12026-07-07 · 1
PracticesBeta

4 development-tooling and community-health practices detected across 3 families in the upstream repository

Checks run against github.com/gabybudel/dbhc on 2026-10-21.

Continuous integration (1)
Travis CI (config file)
CRAN release process (2)
cran-comments.mdCRAN-SUBMISSION
Lint, format, editor (1)
RStudio project
How this is detected·Detection ruleset v4 (2026-10-20)

Dependencies

Declared dependencies
6 external dependencies (excludes base and recommended)

Installing DBHC adds 87 packages (49 with compiled code, 67 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, data.table 5.7 MB.

Depends (0)
none
LinkingTo (0)
none
Suggests (1)
Enhances (0)
none
Reverse dependencies
0direct
0indirect

Nothing depends on this yet.

People & History

People (2)
Author, Maintainer since 0.0.2 (2018)
Releases (3)

Pick two releases to compare their code metrics.

  • 0.0.4Latest
    record
    2026-07-24 · current release · diff ↗
  • R
    R 4.6.0 released · 2026-04-24
  • R
    R 4.5.0 released · 2025-04-11
  • R
    R 4.4.0 released · 2024-04-24
  • R
    R 4.3.0 released · 2023-04-21
  • unarchivedReturned to CRAN
    2022-12-22
  • 0.0.3
    record
    2022-12-22 · 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
  • archivedRemoved from CRAN
    2020-04-03
    check problems were not corrected in time Errors in '\donttest' parts of examples
  • R
    R 3.6.0 released · 2019-04-26
  • R
    R 3.5.0 released · 2018-04-23
  • 0.0.2
    record
    2018-04-13
  • R
    R 3.4.0 released · 2017-04-21

Package metadata

First published
2018-04-13
Total releases
3 / 8 yrs
License
GPL (>= 3) OSI
Download size
19 KB

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("DBHC")
Budel, G., & Frasincar, F. (2026). DBHC: Sequence Clustering with Discrete-Output HMMs (Version 0.0.4) [Computer software]. https://doi.org/10.32614/CRAN.package.DBHC

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 DBHC version 0.0.4 [Data set]. HJJB, LLC. Data release v2026-10-25. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-25, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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