rsdc_viterbi() no longer errors on a
single-observation input (the forward recursion indexed out of
bounds).rsdc_minvar()/rsdc_maxdiv() validate their
inputs before reconstructing the covariance: volatilities must be finite
and positive and correlations finite with modulus below one, so a
malformed column now raises an informative error naming the offending
row instead of silently producing a wrong-signed covariance matrix.rsdc_maxdiv() checks that Rsolnp::solnp()
actually converged (and that its weights are finite and sum to one)
rather than accepting whatever it returns. Both optimizers now count the
periods in which they fell back to equal weights, report the count as
$n_fallback, and warn once if it is positive; previously
the fallback was silent.rsdc_forecast_ahead() validates horizon,
and rsdc_forecast() validates
control$threshold, instead of failing later with a cryptic
message.vcov()/summary()/ confint() now
return NA/NULL with an explanatory warning
instead.vcov(type = "opg") no longer requires the Hessian-based
covariance: OPG needs only the per-observation scores, which are now
stored whenever compute_se = TRUE. Scores are still
withheld (and all covariance estimators refused) when a
"noX" transition row had to be projected onto the simplex,
since that point is not a stationary optimum.Depends is now R (>= 4.0): the package
compiles C++11 (from R 4.0 the default C++ standard) and declaring
CXX_STD explicitly would raise an R CMD check NOTE.rsdc_estimate() now rejects fewer than two observations
instead of returning a degenerate fit.src/Makevars/src/Makevars.win now link
BLAS/LAPACK explicitly
($(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS)), as required by
RcppArmadillo. Without it, R CMD INSTALL on Windows failed
at the link step with undefined references (dgemv_,
dgemm_, dsyev_, …); macOS linkers tolerate the
undefined symbols, which is why the omission was invisible on the
development machine.citation("RSDC") for the current reference. The package
ships the lightweight “Getting started” vignette
(rsdc-intro.Rmd).rsdc_estimate()’s
DEoptim stage now explores canonical partial correlations (Joe, 2006, J.
Multivariate Analysis 97(10), 2177-2189) instead of raw pairwise
correlations. Every point of the search box maps to a valid
positive-definite correlation matrix, so the out-of-the-box global
search is feasible at any K (the natural box has essentially no feasible
point from K ~ 5 on). For method = "noX" with N >= 3 the
transition head is searched as bounded per-row softmax logits (roughly
the historical [0.01, 0.99] box on the log-odds scale), which removes
the row-simplex penalty wall while keeping every regime reachable; the N
= 2 head and all natural-parameter results are unchanged. Within each
search, the top three mutually distant members of the final DE
population are refined by L-BFGS-B and the best refined point is kept
(the best DE point is not always in the best basin).control$n_starts >= 2,
a warning is issued when the best log-likelihood is reached by only one
of the independent searches (tolerance 2.5 log-points), signaling a
possibly non-global optimum.
control = list(n_starts = 4, cores = 4) is the recommended
protocol for multimodal surfaces (e.g. "noX" with N >=
3); the default remains a single search.rsdc_starts() is unchanged and remains a fast
deterministic complement: when both routes agree, the optimum is
cross-certified.cores > 1 on a plain single fit no
longer enables the internal parallelType of DEoptim;
parallelism lives across the independent searches of the multi-start
forms.ff5ind: daily value-weighted returns of
five Fama-French industry portfolios (Manuf, Enrgy, HiTec, Hlth, Utils;
Fama & French, 1997, J. Financial Economics 43(2), 153-193; data (c)
Fama and French, Kenneth R. French Data Library) with the MCCC Aggregate
index (2025 Sentometrics release, monthly, forward-filled across trading
days) and the CBOE VIX (via FRED, VIXCLS), aligned on 5155 U.S. trading
days, 2005-01-03 to 2025-06-30. Reproducible build in
data-raw/ff5ind.R.rsdc_starts(): data-driven warm starts for
high-dimensional problems. In moderate dimensions the DEoptim global
search works well, but for K >= 5 a random vector of pairwise
correlations is almost never positive definite, so its initial
population contains no feasible point. rsdc_starts() builds
feasible starting vectors from the empirical correlations of low/…/high
average-correlation sub-periods (positive definite by construction),
with n_starts shrinkage variants as protection against
local optima.rsdc_estimate() accepts an rsdc_starts
object via control$start: each start is refined by the
local optimizer (the global search is skipped), the highest-likelihood
fit is kept, and the returned object carries start_logliks
(spread across starts) and start_pars (per-start optima).
control$cores parallelizes both multi-start forms
(n_starts seeds or rsdc_starts warm starts);
for a plain single fit it is forwarded to the backends, where
cores > 1 enables parallel DEoptim
(parallelType = 1), as before.cores argument for rsdc_bootstrap()
and rsdc_corr_bands(): task-level parallelism via the base
parallel package (forked workers on Unix-alikes, a PSOCK
cluster on Windows). All random draws are generated up front from one
uninterrupted RNG stream and each parallelized task is deterministic
given its inputs, so results are identical for any number of cores; only
the wall time changes.set.seed() when a warm start skips the global search.
Previously every warm-started refit reset the global RNG stream, so
rsdc_bootstrap() — which interleaves simulation and
warm-started re-estimation — produced nearly identical replicates from
the second one on, drastically understating bootstrap standard errors
and intervals (also via vcov(type = "bootstrap") and
confint(type = "bootstrap")).rsdc_forecast() now returns a scalar
NA BIC (instead of a silent zero-length
numeric(0)) when final_params$log_likelihood
is missing.control$start) lying
outside the default L-BFGS-B search box — as produced by the
identifiability relabeling for N >= 3 (re-referenced
softmax betas, former row-complement transition entries) — is no longer
silently projected onto the box; the box is widened element-wise to keep
the supplied start feasible (affects multi-start refits and
rsdc_bootstrap() warm starts).inst/simulation/results/ are now build-ignored, and the
declared Rdpack import is used
(importFrom(Rdpack, reprompt)), clearing both
R CMD check NOTEs.rsdc_minvar()/rsdc_maxdiv(): when
value_cols is a character vector and y carries
column names, misaligned columns are now detected — reordered to
value_cols (with a warning) when the names match as a set,
otherwise a warning states that positional order is assumed. Previously
the names were silently overwritten.rsdc_forecast_ahead() now validates that
X_future has the same number of columns as the estimation
covariate matrix X.plot.rsdc_fit() error message corrected: stored regime
probabilities do not depend on compute_se.rsdc_corr_bands() documents the
truncation of invalid Gaussian draws near parameter bounds.requireNamespace("mvtnorm") guards
(mvtnorm is a hard dependency in
Imports).control$n_starts repeats the
global+local search from several seeds and keeps the highest-likelihood
fit, storing start_logliks. Since DEoptim is a
stochastic global search, this is primarily a stability diagnostic
across seeds (is the optimum reproducible?), not a replacement for
it.rsdc_forecast_ahead(): genuine multi-step-ahead
forecasts of the regime distribution and implied correlations,
propagating the terminal filtered state through the Markov chain
(X_future for the time-varying case).rsdc_corr_bands(): pointwise uncertainty bands for the
predicted correlation path, by drawing parameters from the asymptotic
sampling distribution and re-running the filter.rsdc_viterbi(): most likely (MAP) regime path via the
Viterbi algorithm.tidy(), glance(),
augment() and a ggplot2::autoplot() method for
rsdc_fit (ggplot2 is a soft dependency in
Suggests).rsdc_hamilton() is retained (and used
as the equivalence reference).control now also accepts
cores (parallel DEoptim via
parallelType = 1) and start (a warm-start
parameter vector that skips the global search and goes straight to local
refinement).rsdc_bootstrap() simulates from
the fitted model, re-estimates each replicate warm-started at the MLE,
and returns bootstrap standard errors / percentile intervals that
respect the parameter bounds.vcov()/confint()
gain a type argument — "hessian" (default),
"opg" (outer product of gradients), or
"sandwich" (QML). Per-observation scores are stored on the
fitted object.summary() now reports delta-method standard errors on
the natural scale of the transition probabilities, plus regime
diagnostics (stay probability, expected duration, ergodic
distribution).plot() method for fitted models
(smoothed/filtered regime probabilities).rsdc_estimate()/rsdc_hamilton() now reject
single-series input (K < 2) and a covariate matrix
X whose row count does not match the data;
rsdc_hamilton() validates that a user-supplied fixed
transition matrix P is row-stochastic (finite,
non-negative, rows summing to 1).confint() now uses the same safe square-root as
summary(), so non-positive variance estimates map to
NA instead of producing inconsistent NaN
intervals.noX model with
N >= 3, a transition row that the optimizer leaves
infeasible (free probabilities summing above 1) is still projected onto
the simplex, but the fit is now flagged as non-converged and its
standard errors are suppressed (the projected point is not a valid basis
for inference).rsdc_estimate() now returns an object of class
"rsdc_fit" with standard S3 methods: print,
summary, coef, logLik,
nobs, vcov, confint,
predict, and simulate.
AIC()/BIC() work out of the box via
logLik.vcov) and standard errors
(summary/confint), computed from the numerical
Hessian of the negative log-likelihood at the MLE.N >= 4 is now supported
for "noX" and "tvtp" (previously capped at
N = 3).control now forwards optimizer settings
(itermax, NP, parallelType,
steptol, maxit, and compute_se)
to DEoptim/optim, enabling faster runs."tvtp" covariate matrix X has
no intercept/constant column.inst/extdata
into build-ignored data-raw/ to shrink the package
tarball.mccc dataset: daily Media Climate Change Concerns
(Aggregate) index, forward-filled from monthly and aligned row-for-row
to greenbrown. Supplies the exogenous TVTP covariate used
in the vignette, so the empirical workflow is fully reproducible from
packaged data alone (no external Excel files).inst/extdata/mccc-monthly.csv and builder
data-raw/mccc.R.greenbrown documentation: date range is
2014-01-02 to 2022-12-30.
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