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LaMa - Latent Markov model toolbox
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Version
Version
2.0.6
2.0.5
2.0.4
2.0.3
2.0.2
1.0.0
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)
Search all functions
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