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LaMa - Latent Markov model toolbox

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Version

Install

install.packages('LaMa')

Version

2.0.3

License

GPL-3

Maintainer

Jan-Ole Koslik

Last Published

January 29th, 2025

Functions in LaMa (2.0.3)

make_matrices_dens

Build a standardised P-Spline design matrix and the associated P-Spline penalty matrix
make_matrices

Build the design matrix and the penalty matrix for models involving penalised splines based on a formula and a data set
generator

Build the generator matrix of a continuous-time Markov chain
gdeterminant

Computes generalised determinant
forward_phsmm

Forward algorithm for hidden semi-Markov models with periodically inhomogeneous state durations and/ or conditional transition probabilities
forward_sp

Forward algorithm for hidden semi-Markov models with periodically varying transition probability matrices
gamma2

Reparametrised gamma distribution
penalty

Computes penalty based on quadratic form
nessi

Loch Ness Monster Acceleration Data
forward_s

Forward algorithm for hidden semi-Markov models with homogeneous transition probability matrix
pseudo_res

Calculate pseudo-residuals
sdreportMC

Monte Carlo version of sdreport
pseudo_res_discrete

Calculate pseudo-residuals for discrete-valued observations
pred_matrix

Build the prediction design matrix based on new data and model_matrices object created by make_matrices
stationary

Compute the stationary distribution of a homogeneous Markov chain
stateprobs

Calculate conditional local state probabilities for homogeneous HMMs
qreml

Quasi restricted maximum likelihood (qREML) algorithm for models with penalised splines or simple i.i.d. random effects
stateprobs_p

Calculate conditional local state probabilities for periodically inhomogeneous HMMs
stateprobs_g

Calculate conditional local state probabilities for inhomogeneous HMMs
skewnorm

Skew normal distribution
stationary_p_sparse

Sparse version of stationary_p
stationary_cont

Compute the stationary distribution of a continuous-time Markov chain
tpm_hsmm

Builds the transition probability matrix of an HSMM-approximating HMM
tpm_g

Build all transition probability matrices of an inhomogeneous HMM
tpm_emb

Build the embedded transition probability matrix of an HSMM from unconstrained parameter vector
trigBasisExp

Compute the design matrix for a trigonometric basis expansion
trex

T-Rex Movement Data
viterbi

Viterbi algorithm for state decoding in homogeneous HMMs
tpm

Build the transition probability matrix from unconstrained parameter vector
tpm_cont

Calculate continuous time transition probabilities
tpm_emb_g

Build all embedded transition probability matrices of an inhomogeneous HSMM
wrpcauchy

wrapped Cauchy distribution
tpm_ihsmm

Builds all transition probability matrices of an inhomogeneous-HSMM-approximating HMM
viterbi_g

Viterbi algorithm for state decoding in inhomogeneous HMMs
viterbi_p

Viterbi algorithm for state decoding in periodically inhomogeneous HMMs
vm

von Mises distribution
tpm_hsmm2

Build the transition probability matrix of an HSMM-approximating HMM
stationary_sparse

Sparse version of stationary
tpm_phsmm

Builds all transition probability matrices of an periodic-HSMM-approximating HMM
tpm_p

Build all transition probability matrices of a periodically inhomogeneous HMM
tpm_phsmm2

Build all transition probability matrices of an periodic-HSMM-approximating HMM
stationary_p

Compute the periodically stationary distribution of a periodically inhomogeneous Markov chain
tpm_thinned

Compute the transition probability matrix of a thinned periodically inhomogeneous Markov chain.
calc_trackInd

Calculate the index of the first observation of each track based on an ID variable
forward_g

General forward algorithm with time-varying transition probability matrix
dirichlet

Dirichlet distribution
forward

Forward algorithm with homogeneous transition probability matrix
forward_ihsmm

Forward algorithm for hidden semi-Markov models with inhomogeneous state durations and/ or conditional transition probabilities
forward_p

Forward algorithm with for periodically varying transition probability matrices
dgmrf2

Reparametrised multivariate Gaussian distribution
forward_hsmm

Forward algorithm for homogeneous hidden semi-Markov models
LaMa-package

LaMa: Fast Numerical Maximum Likelihood Estimation for Latent Markov Models
buildSmoothDens

Build the design and penalty matrices for smooth density estimation