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This is a major release: models are now built around statedens() and lama(), which give print(), summary(), plot(), simulate(), pseudo_res() and scores() from the model object alone. Several functions are renamed (old names still work but are deprecated), a few are removed, and some outputs change (e.g. llk instead of ll, sp instead of lambda), so existing code may need small adjustments.
statedens() builds the matrix of state-dependent densities of one data stream for allprobs, with parameters given per state, for all states or per observation and state; missing observations are handled for each stream separately, and log = TRUE gives log-densities for forward(…, logspace = TRUE); pseudo_res(mod) works with it
forward() without AD gives an error for negative allprobs, suggesting logspace = TRUE
forward(delta = “stationary”) computes the initial distribution from Gamma: the stationary distribution (per track if track-specific, of the first slice of each track if time-varying) or, with tod, the periodically stationary distribution at the first time point of each track
plot(mod, type = “statedens”) shows the state-dependent densities of each data stream built with statedens() on a histogram, weighted by the local state probabilities (or decoded states, the stationary distribution or given weights), which also works with covariates, several tracks and inhomogeneous models; the weighted densities are returned invisibly
plot(mod, stream = …) selects data streams, for residuals and state-dependent densities
plot(mod, type = “statedens”, xlim = …) evaluates the density curves on the plotted range, not only the range of the data
simulate(mod) simulates states and observations of models built with statedens() on the observed design: same tracks and lengths, with delta, Gamma and the parameters as reported (conditional on the covariates, for continuous-time models on the observed time points); missing observations stay missing unless fill_na = TRUE, and rdens gives random number generators for densities without a matching r-function; a data stream that is NA throughout is a placeholder, so a likelihood function can be used to simulate data and then be fitted to them
simulate(mod, random = TRUE) and obj$simulate() of RTMB work with forward() and statedens(): statedens() registers its data like OBS(x), so random effects (with their density evaluated first, e.g. u %~% dnorm(…)) are drawn first, then the states and data given them; forward() reports the simulated states as states
print() and summary() for models from lama() and qreml(): print() gives a short overview, summary() the estimated state process (transition probability or generator matrix, stationary distribution and mean dwell times, averaged over tracks if track-specific) and the reported quantities, with those of length nStates in one table; summary.qremlModel() adds the smooth terms and returns an object now
qreml() models have class c(“qremlModel”, “LaMaModel”), so all LaMaModel methods apply to them
forward() reports type “track-specific” for one Gamma per track (before “homogeneous”)
multistart(obj) optimises an RTMB object from several starting values (obj$par and perturbations of it, or of the first fit scaled by the unit information with scale = “unit_info”), stops by a Bayesian stopping rule or once the best optimum is reached often enough, and returns the best fit with a table of all runs; lama(obj) then builds the model from the best fit
modmat() returns sp0, initial penalty strengths from mgcv::initial.sp() (times 100, capped at 1e6), one per penalty matrix and named as S; rep_sp() repeats them as lambda for smooths used more than once, e.g. one per state
The penalty strengths are now called sp: qreml() takes them from dat\(sp if present, otherwise from dat\)lambda as before, so lambda is free for other things, e.g. a Poisson mean; spname still overrides this. penalty() and penalty2() call their argument sp, lambda still works
print() for objects from modmat() shows the formulas and the initial penalty strengths
smoothdens() builds the objects for smooth (P-spline) densities from a data vector, or one object per stream from a data frame or list; density(beta) in the returned object gives the allprobs matrix and predict() the estimated densities, e.g. on the grid xseq
make_matrices() is renamed to modmat(); make_matrices() still works
report() is renamed to lama(), as it does much more than calling obj$report(); report() still works but is deprecated
old functions still work but are deprecated, with a message pointing to the new implementation: forward_s() and forward_sp() (use forward_hsmm() and forward_phsmm()), tpm_hsmm2() and tpm_phsmm2() (use tpm_hsmm() and tpm_phsmm()), tpm_g2() (use tpm()), qreml_old() (use qreml()), stationary_cont() and tpm_cont() (use stationary_ct() and tpm_ct())
deprecation messages are shown once per session, not at every call
lama(): the log-likelihood is called llk (before ll), as for qreml()
lama() and qreml(): nobs does not count missing observations (rows of allprobs that are all 1), which changes BIC for data with missing observations
lama() stores the maximum gradient component mgc to check convergence
pseudo_res(mod) computes pseudo-residuals from the model object alone: the likelihood function is evaluated again with its densities replaced by their CDFs, after checking that this is valid; several data streams give one set of residuals each; also for qreml() models and densities in helper functions
scores(mod) computes the score contribution of each observation for models using forward(), e.g. for robust (sandwich) standard errors; it takes the model from lama() or the RTMB object, respects map, and the likelihood function does not need to change
lama() also stores obj (the same object, not a copy), so MCreport() can be called on the model object from lama() or qreml()
pred_matrix() is deprecated, use predict() on the object from modmat() instead
make_matrices_old() is removed
smooth_dens_construct() is deprecated, use smoothdens() instead
make_matrices_dens() and smooth_dens_construct() no longer print messages; basis$knots includes the boundary knots
Examples and vignettes: the periodically stationary initial distribution matches forward()’s tod, where Gamma[,,tod[t]] is the transition into t, i.e. stationary_p(Gamma, tod[1] %% L + 1)
lama(): the log-likelihood was evaluated at the last parameters the optimiser tried, not the best
summary.qremlModel() failed for reported lists or character vectors, and if Gamma was not reported but delta was
pseudo_res(): missing observations give NA in all variants (before 0 with normal = FALSE, -Inf for non-randomised discrete residuals)
pseudo_res(): parameters given per observation (matrix or vector of length nObs) work with missing observations
forward(): with track-specific delta or Gamma, the AD version matched them to the wrong tracks if the track IDs were not sorted (e.g. “ID1”, …, “ID10”)
forward(): tracks of length one failed with AD
logspace = TRUE is now exact (delta is no longer modified) and also stable without AD
one-state models are correct with logspace = TRUE and report their quantities
forward_p() works if tod does not contain all values 1, …, L
forward_g() works without AD for two observations
stateprobs_g(forecast = TRUE) with trackID used the transition matrix of the previous time point, which also affected pseudo_res() for inhomogeneous models with tracks
stateprobs() passes forecast on for time-varying Gamma
viterbi() and stateprobs() work for tracks of length one
stateprobs(forecast = TRUE): the first row of each track is delta, not the filtered probabilities given x_1, so pseudo_res() is correct for the first observation
modmat(): smooths with several penalties but not one per margin (e.g. bs = “fs”, “ad” or t2(full = TRUE)) kept only the first, so later smooths got the wrong penalty matrices
modmat(): a nested formula list failed if one data stream had no smooth terms
modmat(): tensor product margins over several covariates are named e.g. “x,y” (two such margins failed); unpenalised smooths (fx = TRUE) give an informative error
predict() on objects from modmat() ignored exclude
smooth_dens_construct(): coefficients of one state are a matrix (before a vector, so cbind(coef, 0) was wrong); circular data with custom knots failed; angles outside [-pi, pi) are wrapped; par accepts kappa next to concentration
make_matrices_dens(): the basis is normalised on a midpoint grid (before a fixed 1e-3 offset, which gave negative weights for data on a small scale), which changes the weights very slightly
vignettes 1-3 have been updated to work with automatic differentiation
tpm(), forward(), stateprobs(), viterbi() can now be used with inhomogeneous models: call their inhomogeneous counterparts internally
function MCreport() has been added for sampling
some functions have been renamed, e.g. stationary_cont() -> stationary_ct() (old versions still exist)
some internal cleanup
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