applicability_check() screens a fitted constant
copula-VAR for the floor/ceiling pile-up pathology, combining a
boundary-atom signal, a dynamics-collapse signal (fitted self-lags
against an OLS VAR(1) anchor and an optional normal-margin reference
fit), and slant/convergence flags into a
suitable/caution/unsuitable
verdict with a recommendation. Has a print method.
Convergence problems (divergences, elevated split-Rhat) block a
suitable verdict; the boundary atom requires a genuine tie
at the bound; and the dynamics-collapse anchor is sign-agnostic.sigma is supplied.sigma_trajectory() now reports skew-normal time-varying
scales on the residual-SD scale, matching simulation truth and the gamma
convention.log1m(), matching the
Clayton guard.phi selector.fitted()/predict() tests so they exercise
time-varying unit-level Phi paths and sigma_t intervals
away from t = 1.sigma_trajectory() scale
reporting.dcvar_sem(method = "indicator") gains
tv_phi and tv_sigma, using a new
sem_tv_mixed.stan engine with optional masked random walks
for latent VAR coefficients, log-scale scale paths, and time-varying
Gaussian-copula correlation. The naive score method now rejects TV SEM
options explicitly.dcvar_multilevel() gains tv_phi and
tv_sigma, using a new multilevel_tv_mixed.stan
engine. To reduce the main identifiability risk, time-varying Phi is
implemented as a shared population drift around unit-specific random
baselines rather than independent per-unit random walks.dcvar_sem_tv_fit
and dcvar_multilevel_tv_fit subclasses expose
phi_trajectory(), sigma_trajectory(),
rho_trajectory(), dependence_summary(),
coef(), and var_params() methods; multilevel
TV fits also use the per-unit-time Phi path in fitted() and
predict().dcvar_sem() (indicator and naive score methods) and
dcvar_multilevel() now accept homogeneous
"skew_normal" and "gamma" margins in addition
to "normal" and "exponential". These fits
route through the existing generic mixed-margin Stan engines
(sem_mixed.stan, sem_naive_mixed.stan, and
multilevel_mixed.stan) by passing the same family code for
both dimensions.coef(),
var_params(), diagnostics, and diagnostic plots understand
the mixed-engine parameter layout for these homogeneous fits, including
per-dimension shape_gam[d] outputs for gamma margins.dcvar_hmm() gains a switch argument that
lets the VAR(1) intercepts (mu), the VAR coefficients
(Phi, with
"ar"/"cross"/coefficient-name granularity),
and the residual scales become state-specific, in addition to the always
state-specific copula correlation. switch = "rho" (the
default) reproduces the previous regime-switching-correlation model
exactly. The copula correlation is the label-switching anchor
(ordered z_rho), so rho must
remain in switch whenever other components switch; states
are reported in increasing-correlation order.margins accepts a length-K list of per-state family
specifications, for example
margins = list(c("normal", "normal"), c("exponential", "gamma")),
with a matching skew_direction (a length-2 vector recycled
across states, or a length-K list). The list is consumed in
increasing-rho order, and a warning is emitted when
families differ across states so the ordering can be verified.inst/stan/hmm_switching.stan, selected
automatically; the default dcvar_hmm() keeps routing to the
specialised legacy files, so existing fits and posteriors are unchanged.
dcvar_stan_path("hmm") is unchanged; pass
stan_file = dcvar_stan_path("hmm_switching") to request the
engine explicitly.hmm_state_params() returns the per-state
intercepts, effective VAR(1) coefficient matrices, margin scales, and
family labels. coef(), var_params(),
print(), and summary() report state-indexed
parameters for switching fits; fitted() returns the
gamma-weighted one-step prediction. Marginal prediction intervals, PIT,
and posterior-predictive plots are not yet available for state-specific
fits and abort with a pointer to hmm_state_params().dcvar() with
tv_phi / tv_sigma) and the drift covariate
model (dcvar_covariate(drift = TRUE)) are now served by a
single generic Stan engine, inst/stan/dcvar_dynamic.stan.
It extends the time-varying mixed-margin model with a covariate
predictor on the Fisher-z correlation and the covariate model’s
residual-drift convention, unifying the two model families behind one
Fisher-z predictor
z_rho[t] = z_rho_init + X[t+1]' beta + drift[t].dcvar() and dcvar_covariate() keep
their signatures, defaults, S3 classes, and model strings, and the
posteriors are unchanged. The plain (non time-varying)
dcvar() and the no-drift
dcvar_covariate(drift = FALSE) model continue to use their
specialised Stan files unchanged, so no refit is needed. The covariate
model exposes its intercept as beta_0 exactly as before
(now a transformed-parameter alias of the sampled
z_rho_init).dcvar_stan_path() continues to return the Stan file
matching each exported prepare_*() data contract
(e.g. dcvar_stan_path("dcvar_covariate") still returns the
legacy covariate model). The unified engine is selected internally by
the fit wrappers; pass
stan_file = dcvar_stan_path("dcvar_dynamic") to request it
explicitly.dcvar() gains tv_phi and
tv_sigma flags. With tv_phi = TRUE the four
VAR(1) coefficients (the AR effects phi11/phi22 and the cross-lagged
effects phi12/phi21) evolve as independent non-centered random walks
around the constant baseline Phi; with
tv_sigma = TRUE the residual scales of normal and
skew-normal dimensions evolve as log-scale random walks around their
baselines. Combined with the always time-varying copula correlation
rho(t) and per-variable (mixed) margins, every model component except
the intercepts can now vary over time. Both flags default to
FALSE, which reproduces the previous behavior exactly (same
Stan models, same draws).tv_phi also accepts a character selector so that only a
subset of the VAR coefficients varies: "ar" (the
autoregressive effects phi11, phi22 – e.g. for changing emotional
inertia / critical slowing down), "cross" (the cross-lagged
effects phi12, phi21), or specific names such as
c("phi11", "phi22"). Only the selected coefficients get
random-walk parameters (the others stay at their constant baseline),
which improves identifiability on short series and lets you test a sharp
hypothesis. phi_trajectory() still returns all four
coefficients (the fixed ones as constant paths);
coef()/var_params() label tau_phi
by coefficient name.dcvar_tv_mixed.stan) for all margin specifications,
dispatching on per-dimension family codes; with both flags off in that
model the target density is identical term-by-term to
dcvar_mixed_ncp.stan, so the TV model exactly nests the
classic DC-VAR. New walk priors prior_tau_phi_rate (default
0.025) and prior_tau_sigma_rate (default 0.05) shrink
toward the constant model; see the new “Recommended workflow” section in
?dcvar for the model ladder.dcvar_tv_fit (inherits
dcvar_fit, so rho_trajectory(),
plot_rho(), and loo() work unchanged) with new
extractors phi_trajectory() and
sigma_trajectory() (flag-agnostic: constants are tiled over
time), plots plot_phi_trajectory() /
plot_sigma_trajectory(),
fitted()/predict() using the per-time
coefficients and scales, per-time plug-in PIT values, and per-time
spectral-radius monitoring in the Stan generated quantities.simulate_dcvar() gains optional
phi_trajectory and sigma_trajectory arguments
(matrix, list of per-coefficient vectors — the rho_*
generators can be reused — or constants). The marginal quantile
transform is now scale-aware, so exponential/gamma dimensions can be
simulated with any constant scale; regression tests pin the copula
orientation under time-varying scales for every
skew_direction combination.tv_sigma) apply to
all margin families. Normal and skew-normal dimensions
use a multiplicative log-scale random walk; exponential and gamma
dimensions use a soft-barrier transform
x = softplus_k(m_t + skew * eps) (sharpness
tv_sigma_k, default 8) so the scale m_t can
vary freely without the hard support boundary coupling it to the
residuals. The soft-barrier matches the exact shifted margin in the
interior and rounds the boundary smoothly (a small residual-mean bias in
the lower tail; larger tv_sigma_k tightens it at the cost
of stiffer geometry). simulate_dcvar() gains
tv_sigma_k to generate matching data for an exp/gamma
time-varying-scale fit.shape_gam is per-dimension
(mixed-model convention), unlike the shared scalar in the specialised
gamma models. dcvar_compare() warns when a TV fit is
compared against less heavily latent-conditioned fits.skew_direction = -1 exponential/gamma dimension as
u = 1 - F(x_shifted), but simulate_dcvar()
(and the shared mixed-margin helper used by
simulate_dcvar_multilevel() and
simulate_dcvar_sem(), plus the homogeneous exponential SEM
path) negated the quantile-transformed variable after applying
the copula. Data simulated with exactly one negative
skew_direction therefore implied copula correlation
-rho while true_params recorded
+rho, so simulate-then-fit studies silently recovered the
negated dependence trajectory. Simulations that used
c(1, 1) or c(-1, -1) are unaffected (the flips
cancel); any results generated with asymmetric skew directions should be
re-run.eps_rep posterior-predictive blocks in seven Stan
models had the mirror-image inversion bug and replicated negated
dependence on left-skewed dimensions; they now invert at the flipped
uniform.dependence_summary() for HMM fits now averages
Kendall’s tau over states per draw
(sum_k gamma[t,k] * (2/pi) asin(rho_state[k])) instead of
applying asin to the gamma-weighted mean rho, which
understated tau during regime transitions.hmm_states()$viterbi is now a genuine Viterbi (MAP)
decoding on posterior-mean emission/transition log-probabilities. The
previous most-frequent-sampled-path estimator degenerated to an
arbitrary (lexicographically first) single draw’s path whenever
parameter uncertainty made nearly all sampled paths unique.eps_rep) are now
drawn from the regime mixture (a state sampled from the smoothed
probabilities, then that state’s rho) instead of the gamma-weighted mean
rho..relative_bias() (used by
compute_rho_metrics() /
compute_param_metrics()) now normalizes the mean error by
the mean true value (by the mean absolute true value for mixed-sign
trajectories, which preserves the conventional sign for negative
parameters). The previous pointwise form exploded to ~1e12 % when any
true value was near zero (e.g. null-dependence conditions), silently
corrupting aggregated summaries. It returns NA with a
warning when all true values are near zero, and
aggregate_metrics() now tolerates that NA (its
summaries already used na.rm = TRUE).hmm_EG.stan now exposes sigma_exp (plus
b_gq and rate_exp) as saved generated
quantities. Previously they were brace-local, so summary(),
var_params(), coef(), and
plot_diagnostics() errored and pit_values()
silently returned all-NA for exponential-margin HMM fits.rho_trajectory(), fitted(), and
predict() downstream. All preparation functions now coerce
to a plain data frame up front.loo() and dcvar_compare() now support all
multilevel fits: multilevel_copula_var.stan stores
per-observation log_lik like its EG and mixed siblings, and
the margin-based whitelist was removed. dcvar_compare()
warns when an HMM fit (state-marginalized predictive density) is
compared against a dynamic fit (conditioned on the smoothed latent rho
trajectory), which can systematically favor the dynamic model.seed argument now makes fits reproducible: default
per-chain initial values are generated under a deterministic RNG
(previously they came from the unseeded global R RNG, so two fits with
the same seed differed).saveRDS() and an R restart:
posterior draws are eagerly read into the fit object after sampling
instead of being lazily re-read from CSVs in the session tempdir.interpret_rho_trajectory() no longer errors on Clayton
constant fits; it interprets the dependence via Kendall’s tau.-Inf (with NaN gradients) for
theta greater than about 34 during warmup.simulate_dcvar_sem(n_time = 1) no longer crashes with a
subscript error; prepare_multilevel_data() requires at
least 3 occasions per unit; .safe_cor() handles length-1
inputs; Y[cc, ] row removal keeps the matrix shape with a
single remaining row.print() for constant-fit summaries no longer swaps the
CI bounds when probs are not passed in ascending
order.draws() validates its format argument
instead of silently returning a draws_array.NA
(unknown) rather than 0 for fits without sampler diagnostics.plot_hmm_states() pins the state factor levels so
Viterbi point colors match the fill colors when the MAP path skips a
state; plot_trajectories() forwards ... only
to the scenario generators that accept each argument.prior_sigma_eps_rate with no normal margin;
prior_z_rho_sd with the Clayton copula;
prior_sigma_omega_rate and zero_init_eta with
dcvar_covariate(drift = FALSE)), and the recorded
priors list omits unused entries.interpret_rho_trajectory() classifies Clayton
dependence strength on the rho-equivalent scale
(sin(pi * tau / 2)), so the labels are comparable with the
Gaussian branch for equivalent dependence..Machine$integer.max no longer overflow and abort the
fit.int<lower=2, upper=2> D, rejecting D > 2 data that
the hard-wired bivariate copula code would silently mishandle.predict() intervals,
the raw-data scale of simulator true_params (fit with
standardize = FALSE for round-trip comparisons), the
lognormal scale prior for exponential/gamma multilevel margins, and
prior_rho_init_sd as the covariate-model dependence
intercept (beta_0) prior.skew_direction combination, seed reproducibility, tibble
input, time column validation, and edge-gap handling. The Clayton-normal
constant model and dcvar_covariate() (drift and no-drift)
now get real MCMC smoke fits in CI.summary() printouts for HMM and multilevel fits now
include the marginal scale/shape parameters
(e.g. sigma_exp, omega, delta,
sigma_gam, shape_gam) for non-normal and
per-variable (mixed) margins. Previously the HMM summary omitted all
margin scale parameters, and the multilevel summary dropped them for
mixed margins.simulate_breakpoint_data() now records the breakpoint
specification (type, plus breakpoint or
breakpoints) in true_params, so the documented
access no longer returns NULL.rho_decreasing() and rho_increasing() now
validate that rho_start and rho_end are single
finite values in [-1, 1], matching the other rho trajectory
generators.coef() / fitted() / data-preparation
contracts.b_gq) report the clamped lower bound
consistently across the exponential models (inference-neutral), and
corrected Stan prior-hyperparameter comments. The Clayton-normal
constant model’s sigma_eps prior is left unchanged and its
intentional divergence from the other constant models is now
documented.margins vector so each variable can use its own marginal
family, e.g. margins = c("normal", "exponential"). This
exploits the copula’s separation of the marginal distributions from the
dependence structure. Supported across dcvar_constant(),
dcvar(), dcvar_hmm(),
dcvar_multilevel(), and dcvar_sem() (both the
indicator and naive methods).constant_mixed.stan, dcvar_mixed_ncp.stan,
hmm_mixed.stan, multilevel_mixed.stan,
sem_mixed.stan, and sem_naive_mixed.stan.constant_mixed_clayton.stan), previously limited to normal
margins.simulate_dcvar(),
simulate_dcvar_multilevel(), and
simulate_dcvar_sem() accept the same per-variable
margins vector so mixed-family data can be generated (for
example for parameter-recovery studies).coef(), var_params(),
pit_values(), and the diagnostics/plots report each
dimension under its own family for mixed fits across all model
families.margins string is unchanged, and an
all-identical margins vector (such as
c("normal", "normal")) routes to the existing specialised
single-family model, so prior results, tests, and the gamma shared-shape
parameterisation are preserved exactly.margins vector instead.dcvar_constant(copula = "clayton").dcvar_multilevel().dcvar_sem(method = "naive") for normal and exponential
margins.dependence_summary() for Kendall’s tau summaries
across Gaussian and Clayton copula fits.loo() support across
model classes.*-test-local.log artifacts from source
builds.dcvar() and
dcvar_hmm().
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