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LaMa - Latent Markov model toolbox

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Version

Install

install.packages('LaMa')

Monthly Downloads

331

Version

2.0.6

License

GPL-3

Maintainer

Jan-Ole Koslik

Last Published

September 23rd, 2025

Functions in LaMa (2.0.6)

make_matrices_dens

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

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

Computes penalty based on quadratic form
plot.LaMaResiduals

Plot pseudo-residuals
nessi

Loch Ness Monster Acceleration Data
minmax

AD-compatible minimum and maximum functions
penalty2

Computes generalised quadratic-form penalties
minmax0_smooth

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

Penalty approximation of unimodality constraints for univariates smooths
pred_matrix

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

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

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

Build the design and penalty matrices for smooth density estimation
sdreport_outer

Report uncertainty of the estimated smoothing parameters or variances
pseudo_res_discrete

Calculate pseudo-residuals for discrete-valued observations
pseudo_res

Calculate pseudo-residuals
sdreportMC

Monte Carlo version of sdreport
stationary_p_sparse

Sparse version of stationary_p
stationary_p

Periodically stationary distribution of a periodically inhomogeneous Markov chain
summary.qremlModel

Summary method for qremlModel objects
skewnorm

Skew normal distribution
stationary_cont

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

Sparse version of stationary
tpm

Build the transition probability matrix from unconstrained parameter vector
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_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_cont

Calculate continuous time transition probabilities
tpm_g

Build all transition probability matrices of an inhomogeneous HMM
tpm_g2

Build all transition probability matrices of an inhomogeneous HMM
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
wrpcauchy

wrapped Cauchy distribution
zero_inflate

Zero-inflated density constructer
vm

von Mises distribution
viterbi

Viterbi algorithm for state decoding in homogeneous HMMs
trex

T-Rex Movement Data
trigBasisExp

Compute the design matrix for a trigonometric basis expansion
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.
viterbi_p

Viterbi algorithm for state decoding in periodically inhomogeneous HMMs
viterbi_g

Viterbi algorithm for state decoding in inhomogeneous HMMs
LaMa-package

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

General 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 with homogeneous transition probability matrix
forward_hsmm

Forward algorithm for homogeneous hidden semi-Markov models
dgmrf2

Reparametrised multivariate Gaussian distribution
forward_ihsmm

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

Dirichlet distribution
cosinor

Evaluate trigonometric basis expansion
ddwell

State dwell-time distributions of periodically inhomogeneous Markov chains
make_matrices

Build the design and the penalty matrix for models involving penalised splines based on a formula and a data set
gamma2

Reparametrised gamma distribution
generator

Build the generator matrix of a continuous-time Markov chain
logLik.qremlModel

Extract log-likelihood from qremlModel object
forward_s

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

Sparsity-retaining matrix multiplication
forward_sp

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

Forward algorithm with for periodically varying transition probability matrices
gdeterminant

Computes generalised determinant
forward_phsmm

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