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license: MIT
title: 'LaMa: Fast Numerical Maximum Likelihood Estimation for Latent Markov Models'
version: 2.1.1
doi: 10.32614/CRAN.package.LaMa
abstract: 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.
authors:
- family-names: Fischer
  given-names: Jan-Ole
  email: jan-ole.fischer@mailbox.org
  orcid: https://orcid.org/0009-0004-1556-9053
repository: https://janolefi.r-universe.dev
commit: 7035273e0e481e3767ff7ec555b69be27651a1f9
url: https://janolefi.github.io/LaMa/
date-released: '2026-05-28'
contact:
- family-names: Fischer
  given-names: Jan-Ole
  email: jan-ole.fischer@mailbox.org
  orcid: https://orcid.org/0009-0004-1556-9053