LaMa

Description

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.

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installed size is  7.9Mb
  sub-directories of 1Mb or more:
    doc    3.1Mb
    libs   3.4Mb

installed size is  8.0Mb
  sub-directories of 1Mb or more:
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installed size is  5.7Mb
  sub-directories of 1Mb or more:
    doc    3.1Mb
    libs   1.3Mb

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installed size is  7.9Mb
  sub-directories of 1Mb or more:
    doc    3.1Mb
    libs   3.4Mb

installed size is  8.0Mb
  sub-directories of 1Mb or more:
    doc    3.1Mb
    libs   3.5Mb

installed size is  5.7Mb
  sub-directories of 1Mb or more:
    doc    3.1Mb
    libs   1.3Mb

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