Package index • LaMa

All functions

calc_trackInd(): Calculate the index of the first observation of each track based on an ID variable

cosinor(): Evaluate trigonometric basis expansion

ddwell(): State dwell-time distributions of periodically inhomogeneous Markov chains

dgmrf2(): Reparametrised multivariate Gaussian distribution

ddirichlet(): Dirichlet 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

dgamma2() pgamma2() qgamma2() rgamma2(): 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

min2() max2(): 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

plot(<LaMaResiduals>): Plot pseudo-residuals

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

pseudo_res(): Calculate pseudo-residuals

pseudo_res_discrete(): Calculate pseudo-residuals for discrete-valued observations

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

sdreportMC(): Monte Carlo version of sdreport

sdreport_outer(): Report uncertainty of the estimated smoothing parameters or variances

dskewnorm() pskewnorm() qskewnorm() rskewnorm(): 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

dvm() pvm() rvm(): von Mises distribution

dwrpcauchy() rwrpcauchy(): wrapped Cauchy distribution

zero_inflate(): Zero-inflated density constructer