Package: LaMa 2.0.2
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.
Authors:
LaMa_2.0.2.tar.gz
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LaMa.pdf |LaMa.html✨
LaMa/json (API)
# Install 'LaMa' in R: |
install.packages('LaMa', repos = c('https://janoleko.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/janoleko/lama/issues
Last updated 9 hours agofrom:8e995e2fdf. Checks:OK: 9. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 21 2024 |
R-4.5-win-x86_64 | OK | Nov 21 2024 |
R-4.5-linux-x86_64 | OK | Nov 21 2024 |
R-4.4-win-x86_64 | OK | Nov 21 2024 |
R-4.4-mac-x86_64 | OK | Nov 21 2024 |
R-4.4-mac-aarch64 | OK | Nov 21 2024 |
R-4.3-win-x86_64 | OK | Nov 21 2024 |
R-4.3-mac-x86_64 | OK | Nov 21 2024 |
R-4.3-mac-aarch64 | OK | Nov 21 2024 |
Exports:buildSmoothDenscalc_trackInddgamma2dgmrf2dvmforwardforward_gforward_hsmmforward_ihsmmforward_pforward_phsmmforward_sforward_spgeneratormake_matricesmake_matrices_denspenaltypgamma2pred_matrixpseudo_respseudo_res_discretepvmqgamma2qremlrgamma2rvmsdreportMCstateprobsstateprobs_gstateprobs_pstationarystationary_contstationary_pstationary_p_sparsestationary_sparsetpmtpm_conttpm_embtpm_emb_gtpm_gtpm_hsmmtpm_hsmm2tpm_ihsmmtpm_ptpm_phsmmtpm_phsmm2tpm_thinnedtrigBasisExpviterbiviterbi_gviterbi_p
Dependencies:bootCircStatscircularlatticeMASSMatrixmgcvmvtnormnlmeRcppRcppArmadilloRcppEigenRTMBTMB
Continuous-time HMMs
Rendered fromContinuous_time_HMMs.Rmd
usingknitr::rmarkdown
on Nov 21 2024.Last update: 2024-10-30
Started: 2024-02-26
Hidden semi-Markov models
Rendered fromHSMMs.Rmd
usingknitr::rmarkdown
on Nov 21 2024.Last update: 2024-11-07
Started: 2024-02-23
Inhomogeneous HMMs
Rendered fromInhomogeneous_HMMs.Rmd
usingknitr::rmarkdown
on Nov 21 2024.Last update: 2024-10-30
Started: 2024-04-02
Introduction to LaMa
Rendered fromIntro_to_LaMa.Rmd
usingknitr::rmarkdown
on Nov 21 2024.Last update: 2024-11-12
Started: 2024-04-02
LaMa and RTMB
Rendered fromLaMa_and_RTMB.Rmd
usingknitr::rmarkdown
on Nov 21 2024.Last update: 2024-11-12
Started: 2024-08-06
Longitudinal data
Rendered fromLongitudinal_data.Rmd
usingknitr::rmarkdown
on Nov 21 2024.Last update: 2024-11-07
Started: 2024-04-23
Markov-modulated (marked) Poisson processes
Rendered fromMMMPPs.Rmd
usingknitr::rmarkdown
on Nov 21 2024.Last update: 2024-10-30
Started: 2024-02-23
Penalised splines
Rendered fromPenalised_splines.Rmd
usingknitr::rmarkdown
on Nov 21 2024.Last update: 2024-11-21
Started: 2024-10-30
Periodic HMMs
Rendered fromPeriodic_HMMs.Rmd
usingknitr::rmarkdown
on Nov 21 2024.Last update: 2024-10-31
Started: 2024-10-31
State-space models
Rendered fromState_space_models.Rmd
usingknitr::rmarkdown
on Nov 21 2024.Last update: 2024-11-18
Started: 2024-01-24