Fast Numerical Maximum Likelihood Estimation for Latent Markov Models

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Documentation for package ‘LaMa’ version 2.0.4

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

calc_trackInd	Calculate the index of the first observation of each track based on an ID variable
cosinor	Evaluate trigonometric basis expansion
ddirichlet	Dirichlet distribution
ddwell	State dwell-time distributions of periodically inhomogeneous Markov chains
dgamma2	Reparametrised gamma distribution
dgmrf2	Reparametrised multivariate Gaussian distribution
dirichlet	Dirichlet distribution
dskewnorm	Skew normal distribution
dvm	von Mises distribution
dwrpcauchy	wrapped Cauchy distribution
forward	Forward algorithm with homogeneous transition probability matrix
forward_g	General forward algorithm with time-varying transition probability matrix
forward_hsmm	Forward algorithm for homogeneous hidden semi-Markov models
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
forward_phsmm	Forward algorithm for hidden semi-Markov models with periodically inhomogeneous state durations and/ or conditional transition probabilities
forward_s	Forward algorithm for hidden semi-Markov models with homogeneous transition probability matrix
forward_sp	Forward algorithm for hidden semi-Markov models with periodically varying transition probability matrices
gamma2	Reparametrised gamma distribution
gdeterminant	Computes generalised determinant
generator	Build the generator matrix of a continuous-time Markov chain
logLik.qremlModel	Extract log-likelihood from qremlModel object
make_matrices	Build the design and the penalty matrix for models involving penalised splines based on a formula and a data set
make_matrices_dens	Build a standardised P-Spline design matrix and the associated P-Spline penalty matrix
max2	AD-compatible minimum and maximum functions
min2	AD-compatible minimum and maximum functions
minmax	AD-compatible minimum and maximum functions
nessi	Loch Ness Monster Acceleration Data
penalty	Computes penalty based on quadratic form
penalty2	Computes generalised quadratic-form penalties
pgamma2	Reparametrised gamma distribution
predict.LaMa_matrices	Build the prediction design matrix based on new data and model_matrices object created by 'make_matrices'
pred_matrix	Build the prediction design matrix based on new data and model_matrices object created by 'make_matrices'
pseudo_res	Calculate pseudo-residuals
pseudo_res_discrete	Calculate pseudo-residuals for discrete-valued observations
pskewnorm	Skew normal distribution
pvm	von Mises distribution
qgamma2	Reparametrised gamma distribution
qreml	Quasi restricted maximum likelihood (qREML) algorithm for models with penalised splines or simple i.i.d. random effects
qreml_old	Quasi restricted maximum likelihood (qREML) algorithm for models with penalised splines or simple i.i.d. random effects
qskewnorm	Skew normal distribution
rgamma2	Reparametrised gamma distribution
rskewnorm	Skew normal distribution
rvm	von Mises distribution
rwrpcauchy	wrapped Cauchy distribution
sdreportMC	Monte Carlo version of 'sdreport'
sdreport_outer	Report uncertainty of the estimated smoothing parameters or variances
skewnorm	Skew normal distribution
smooth_dens_construct	Build the design and penalty matrices for smooth density estimation
stateprobs	Calculate conditional local state probabilities for homogeneous HMMs
stateprobs_g	Calculate conditional local state probabilities for inhomogeneous HMMs
stateprobs_p	Calculate conditional local state probabilities for periodically inhomogeneous HMMs
stationary	Compute the stationary distribution of a homogeneous Markov chain
stationary_cont	Compute the stationary distribution of a continuous-time Markov chain
stationary_p	Periodically stationary distribution of a periodically inhomogeneous Markov chain
stationary_p_sparse	Sparse version of 'stationary_p'
stationary_sparse	Sparse version of 'stationary'
summary.qremlModel	Summary method for 'qremlModel' objects
tpm	Build the transition probability matrix from unconstrained parameter vector
tpm_cont	Calculate continuous time transition probabilities
tpm_emb	Build the embedded transition probability matrix of an HSMM from unconstrained parameter vector
tpm_emb_g	Build all embedded transition probability matrices of an inhomogeneous HSMM
tpm_g	Build all transition probability matrices of an inhomogeneous HMM
tpm_hsmm	Builds the transition probability matrix of an HSMM-approximating HMM
tpm_hsmm2	Build the transition probability matrix of an HSMM-approximating HMM
tpm_ihsmm	Builds all transition probability matrices of an inhomogeneous-HSMM-approximating HMM
tpm_p	Build all transition probability matrices of a periodically inhomogeneous HMM
tpm_phsmm	Builds all transition probability matrices of an periodic-HSMM-approximating HMM
tpm_phsmm2	Build all transition probability matrices of an periodic-HSMM-approximating HMM
tpm_thinned	Compute the transition probability matrix of a thinned periodically inhomogeneous Markov chain.
trex	T-Rex Movement Data
trigBasisExp	Compute the design matrix for a trigonometric basis expansion
viterbi	Viterbi algorithm for state decoding in homogeneous HMMs
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
wrpcauchy	wrapped Cauchy distribution
zero_inflate	Zero-inflated density constructer