The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

LaMa: Fast Numerical Maximum Likelihood Estimation for Latent Markov Models

A variety of latent Markov models, including hidden Markov models, hidden semi-Markov models, state-space models and continuous-time variants can be formulated and estimated within the same framework via directly maximising the likelihood function using the so-called forward algorithm. Applied researchers often need custom models that standard software does not easily support. Writing tailored 'R' code offers flexibility but suffers from slow estimation speeds. We address these issues by providing easy-to-use functions (written in 'C++' for speed) for common tasks like the forward algorithm. These functions can be combined into custom models in a Lego-type approach, offering up to 10-20 times faster estimation via standard numerical optimisers. To aid in building fully custom likelihood functions, several vignettes are included that show how to simulate data from and estimate all the above model classes.

Version: 2.0.2
Depends: R (≥ 3.5.0), RTMB
Imports: Rcpp, mgcv, Matrix, stats, utils, MASS, mvtnorm, splines, methods, CircStats, circular
LinkingTo: Rcpp, RcppArmadillo
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0), PHSMM, MSwM, scales
Published: 2024-11-20
DOI: 10.32614/CRAN.package.LaMa
Author: Jan-Ole Koslik ORCID iD [aut, cre]
Maintainer: Jan-Ole Koslik <jan-ole.koslik at uni-bielefeld.de>
License: GPL-3
URL: https://janoleko.github.io/LaMa/
NeedsCompilation: yes
Materials: README
CRAN checks: LaMa results

Documentation:

Reference manual: LaMa.pdf
Vignettes: Continuous-time_HMMs (source, R code)
Hidden semi-Markov models (source, R code)
Inhomogeneous HMMs (source, R code)
Introduction to LaMa (source, R code)
LaMa_and_RTMB (source, R code)
Longitudinal data (source, R code)
MMMPPs (source, R code)
Penalised_splines (source, R code)
Periodic HMMs (source, R code)
State space models (source, R code)

Downloads:

Package source: LaMa_2.0.2.tar.gz
Windows binaries: r-devel: LaMa_2.0.2.zip, r-release: LaMa_2.0.2.zip, r-oldrel: LaMa_2.0.2.zip
macOS binaries: r-release (arm64): LaMa_2.0.2.tgz, r-oldrel (arm64): LaMa_2.0.2.tgz, r-release (x86_64): LaMa_2.0.2.tgz, r-oldrel (x86_64): LaMa_2.0.2.tgz
Old sources: LaMa archive

Linking:

Please use the canonical form https://CRAN.R-project.org/package=LaMa to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.