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All functions

buildSmoothDens()
Build the design and penalty matrices for smooth density estimation
calc_trackInd()
Calculate the index of the first observation of each track based on an ID variable
dgmrf2()
Reparametrised multivariate Gaussian 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
generator()
Build the generator matrix of a continuous-time Markov chain
make_matrices()
Build the design matrix 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
nessi
Loch Ness Monster Acceleration Data
penalty()
Computes penalty based on quadratic form
pred_matrix()
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
sdreportMC()
Monte Carlo version of sdreport
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()
Compute the periodically stationary distribution of a periodically inhomogeneous Markov chain
stationary_p_sparse()
Sparse version of stationary_p
stationary_sparse()
Sparse version of stationary
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