LaMa - Latent Markov model toolbox

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Install

install.packages('LaMa')

Monthly Downloads

325

Version

2.1.2

License

MIT + file LICENSE

Maintainer

Jan-Ole Fischer

Last Published

July 25th, 2026

Functions in LaMa (2.1.2)

generator

Build the generator matrix of a continuous-time Markov chain

make_matrices

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

logLik.LaMaModel

Extract log-likelihood from LaMaModel object

logLik.qremlModel

Extract log-likelihood from qremlModel object

minmax

AD-compatible minimum and maximum functions

make_matrices_old

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

minmax0_smooth

Smooth approximations to max(x, 0) and min(x, 0)

plot.LaMaResiduals

Plot pseudo-residuals

nessi

Loch Ness Monster Acceleration Data

penalty_uni

Penalty approximation of unimodality constraints for univariates smooths

penalty

Computes penalty based on quadratic form

report

Get reported quantities from and RTMB object and return a LaMaModel

penalty2

Computes generalised quadratic-form penalties

stationary

Compute the stationary distribution of a homogeneous Markov chain

process_hid_formulas

Process and standardise formulas for the state process of hidden Markov models

pseudo_res

Calculate pseudo-residuals

pred_matrix

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

qreml

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

sdreport_outer

Report uncertainty of the estimated smoothing parameters or variances

make_matrices_dens

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

skewnorm

Skew normal distribution

qreml_old

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

tpm_g

Build all transition probability matrices of an inhomogeneous HMM

stationary_ct

Compute the stationary distribution of a continuous-time Markov chain

tpm_g2

Build all transition probability matrices of an inhomogeneous HMM

sdreportMC

Monte Carlo version of sdreport

stateprobs_p

Calculate conditional local state probabilities for periodically inhomogeneous HMMs

stateprobs_g

Calculate conditional local state probabilities for inhomogeneous HMMs

tpm_emb_g

Build all embedded transition probability matrices of an inhomogeneous HSMM

tpm_emb

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

predict.LaMa_matrices

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

trex

T-Rex Movement Data

tpm_ct

Calculate continuous time transition probabilities

tpm_thinned

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

stationary_p

Periodically stationary distribution of a periodically inhomogeneous Markov chain

tpm

Build the transition probability matrix from unconstrained parameter vector

tpm_hsmm2

Build the transition probability matrix of an HSMM-approximating HMM

smooth_dens_construct

Build the design and penalty matrices for smooth density estimation

tpm_hsmm

Builds the transition probability matrix of an HSMM-approximating HMM

tpm_p

Build all transition probability matrices of a periodically inhomogeneous HMM

stateprobs

Calculate conditional local state probabilities in HMMs

tpm_ihsmm

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

tpm_phsmm

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

stationary_p_sparse

Sparse version of stationary_p

tpm_phsmm2

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

viterbi_g

Viterbi algorithm for state decoding in inhomogeneous HMMs

trigBasisExp

Compute the design matrix for a trigonometric basis expansion

stationary_sparse

Sparse version of stationary

viterbi

Viterbi algorithm for state decoding in HMMs

summary.qremlModel

Summary method for qremlModel objects

Von Mises distribution

viterbi_p

Viterbi algorithm for state decoding in periodically inhomogeneous HMMs

wrpcauchy

Wrapped Cauchy distribution

zero_inflate

Zero-inflated density constructer

cosinor

Trigonometric basis expansion

MCreport

Sample parameters from approximate Gaussian posterior distribution

forward_g

Forward algorithm with time-varying transition probability matrix

calc_trackInd

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

forward

Forward algorithm to calculate the HMM log-likelihood

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

LaMaColors

Generate a colour-blind-friendly palette

ddwell

State dwell-time distributions of periodically inhomogeneous Markov chains

forward_sp

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

generator_g

Build generator matrices of a continuous-time Markov chain

forward_ihsmm

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

gamma2

Reparametrised gamma distribution

forward_phsmm

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

forward_p

Forward algorithm with for periodically varying transition probability matrices

gdeterminant

Computes generalised determinant

%sp%

Sparsity-retaining matrix multiplication

forward_s

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