Fast Numerical Maximum Likelihood Estimation for Latent Markov Models

---

Documentation for package ‘LaMa’ version 1.0.0

• 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
forward	Forward algorithm with homogeneous transition probability matrix
forward_g	General forward algorithm with time-varying transition probability matrix
forward_p	Forward algorithm with (only) periodically varying transition probability matrix
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
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_p	Compute the periodically stationary distribution of a periodically inhomogeneous Markov chain
tpm	Build the transition probability matrix from unconstraint parameter vector
tpm_cont	Calculation of continuous time transition probabilities
tpm_g	Build all transition probability matrices of an inhomogeneous HMM
tpm_hsmm	Build the transition probability matrix of an HSMM-approximating HMM
tpm_p	Build all transition probability matrices of a periodically inhomogeneous HMM
tpm_phsmm	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.
trigBasisExp	Trigonometric Basis Expansion
viterbi	Viterbi algorithm for decoding states
viterbi_g	Viterbi algorithm for decoding states of inhomogeneous HMMs
viterbi_p	Viterbi algorithm for decoding states of periodically inhomogeneous HMMs