High-Performance Open-Source Archive
run_rolling_xi_acf,
run_rolling_xi_ccf) to strictly comply with CRAN server
policies (e.g., respecting MC_CORES and
OMP_THREAD_LIMIT). Replaced
parallel::detectCores() with
max(1L, parallelly::availableCores() - 1L) to ensure safe
execution without exceeding permitted limits or causing 0-core
crashes.future::plan(sequential)) via
setup.R across the entire testthat suite to
adhere to CRAN’s 2-core testing limit.xi_matrix() where variable names
(var_names) were inadvertently omitted from the output
object, resulting in empty variable lists during
print().xi_ccf()
to utilize the centralized internal helper
check_surrogate_count()."Error:" and
"Warning:" prefixes in custom message strings across the
package to ensure native R console formatting.sig_level validation in xi_matrix().max_iter argument in
run_rolling_xi_acf() and run_rolling_xi_ccf(),
allowing users to explicitly tune the maximum number of iterations for
the IAAFT/MIAAFT surrogate convergence.xi_acf().xi_ccf and xi_matrix. The Max-Statistic null
distribution is now strictly evaluated independently for Contemporaneous
(Lag 0) and Temporal (Lag > 0) dependencies. This resolves the “Lag-0
Masking Effect,” significantly improving the statistical power to detect
delayed causal propagation.xi_matrix to prevent arbitrary threshold inflation.%dofuture% with
explicit package loading.Window_ID tracking column in the outputs
of run_rolling_xi_acf and
run_rolling_xi_ccf.check_surrogate_count) to rigorously validate
user-provided n_surr against the dynamic size of FWER test
families.xi_ccf() now
explicitly separates causal directions (direction = "both",
"x_leads", "y_leads") and returns Tidy data
frames for easier downstream EDA.xi_matrix() Max-Statistic
empirical null distribution, restoring statistical power for detecting
true cross-edge pathways.xi_matrix() C++ calculations to
synchronize behavior with xi_ccf().extract_xi_acf(), extract_xi_ccf()) to
dynamically recompute exact FWER thresholds using preserved raw data
(data_raw).autoplot() methods for xi_ccf (vertical
faceting) and xi_matrix (diagonal variable labels) to
provide publication-ready, unified Tidyverse compatibility.xi_acf, xi_ccf, xi_matrix).autoplot
methods to produce publication-ready ggplot2 charts
utilizing base R expression() for native math
rendering.sig_level = 0.95 (confidence
level) inputs to significance levels, and systematically deprecated
older functions with proper warnings.
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