Skip to contents
-
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