Package index • LaMa

All functions

LaMaColors(): Generate a colour-blind-friendly palette
MCreport(): Sample parameters from approximate Gaussian posterior distribution
calc_trackInd(): Calculate the index of the first observation of each track based on an ID variable
coef(<LaMaModel>): Estimated parameters of a LaMaModel object
cosinor(): Trigonometric basis expansion
ddwell(): State dwell-time distributions of periodically inhomogeneous Markov chains
dgmrf2(): Reparametrised multivariate Gaussian distribution
forward(): Forward algorithm to calculate the HMM log-likelihood
forward_g(): 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
generator_g(): Build generator matrices of a continuous-time Markov chain
`%sp%`: Sparsity-retaining matrix multiplication
lama() report(): Build a LaMaModel from an optimised RTMB object
logLik(<LaMaModel>): Extract log-likelihood from LaMaModel object
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
max0_smooth() min0_smooth(): Smooth approximations to max(x, 0) and min(x, 0)
modmat() make_matrices(): Build the design and the penalty matrix for models involving penalised splines based on a formula and a data set
multistart(): Optimise an RTMB object from several starting values
nessi: Loch Ness Monster Acceleration Data
penalty(): Computes penalty based on quadratic form
penalty2(): Computes generalised quadratic-form penalties
penalty_uni(): Penalty approximation of unimodality constraints for univariate smooths
plot(<LaMaModel>): Plot a LaMaModel object
plot(<LaMaResiduals>): Plot pseudo-residuals
pred_matrix(): Build the prediction design matrix based on new data and model_matrices object created by modmat
predict(<LaMa_matrices>): Build the prediction design matrix based on new data and model_matrices object created by modmat
predict(<smoothdens>): Estimated smooth densities
print(<LaMaModel>): Print a LaMaModel object
print(<LaMa_matrices>): Print a LaMa_matrices object
process_hid_formulas(): Process and standardise formulas for the state process of hidden Markov models
pseudo_res(): Calculate pseudo-residuals
qreml(): Automatic smoothness selection for arbitrary statistical models with penalised splines, simple random effects, or tensorproducts
qreml_old(): Quasi restricted maximum likelihood (qREML) algorithm, previous version
rep_sp(): Repeat initial penalty strengths for replicated smooths
residuals(<LaMaModel>): Pseudo-residuals of a LaMaModel object
scores(): Per-observation score contributions
sdreportMC(): Monte Carlo version of sdreport
sdreport_outer(): Report uncertainty of the estimated smoothing parameters or variances
simulate(<LaMaModel>): Simulate from a fitted model
dskewnorm() pskewnorm() qskewnorm() rskewnorm(): Skew normal distribution
smoothdens(): Build the design and penalty matrices for smooth density estimation
statedens(): State-dependent densities of one data stream
stateprobs(): Calculate conditional local state probabilities in 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_ct() 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(<LaMaModel>) print(<summary.LaMaModel>): Summary of a LaMaModel object
summary(<qremlModel>) print(<summary.qremlModel>): Summary of a qremlModel object
tpm(): Build the transition probability matrix from unconstrained parameter vector
tpm_ct() 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_g2(): 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 a 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
vcov(<LaMaModel>): Variance-covariance matrix of a LaMaModel object
viterbi(): Viterbi algorithm for state decoding in 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() pwrpcauchy() rwrpcauchy(): Wrapped Cauchy distribution
zero_inflate(): Zero-inflated density constructer