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Limit RcppParallel threads to 2 during tests for
CRAN compliance. Previously unconstrained parallelism caused a
CPU/elapsed time ratio of 22x on Debian, triggering a NOTE.
Share MCMC fit fixtures across plot and table tests instead of fitting independently in each test. Reduces total MCMC fits from ~32 to ~12.
Reduce MCMC iterations in structural tests from 3000 to 500. These tests check output structure (column names, dimensions, class), not convergence.
Mark expensive recovery/comparison tests with
skip_on_cran(): onset imputation parameter comparison (5000
iterations x 2 fits), NA covariate attack rate table, and SI summary
format check.
configure script for platform-specific LAPACK/BLAS
linking. On macOS, these libraries are provided by the R framework and
do not need explicit linking; on Linux,
$(LAPACK_LIBS) $(BLAS_LIBS) $(FLIBS) are required to
resolve symbols like dpotrf_.Replaced C-style variable length arrays (VLAs) with
std::vector<double> in C++ MCMC code to comply with
ISO C++ standard. Fixes CRAN check WARNING on Windows (GCC 14) and
installation failure on Debian Linux.
Removed obsolete CXX_STD = CXX11 from
Makevars/Makevars.win and SystemRequirements: C++11 from
DESCRIPTION. C++11 support has been removed in R-devel.
Added LAPACK/BLAS linking flags to src/Makevars
(previously only in Makevars.win), fixing
undefined symbol: dpotrf_ installation error on Debian
Linux.
Forest plot for covariate effects.
plot_covariates(fit) produces a publication-ready forest
plot grouped by Susceptibility and Infectiousness sections, with
reference level labels, alternating row shading, and a dashed null line
at RR = 1. Optional file argument saves a PDF with
auto-sized dimensions based on the number of covariate rows. Custom
variable and level labels can be supplied via the labels
argument.
Summary tables. Three new exported functions
return clean data frames: table_parameters(fit) (posterior
mean/median/CrI with ESS and acceptance),
table_covariates(fit) (covariate effects on the log and
exponentiated scale), and table_attack_rates(fit, by)
(secondary attack rates with Wilson CIs, optionally stratified by a
covariate).
Fixed table_attack_rates() NA handling: rows with
NA in the stratifying variable are now correctly excluded
from strata and counts (previously x == NA returned
NA rather than FALSE, poisoning subset
sizes).
Fixed .effective_sample_size() lag indexing: Geyer
(1992) pairing now correctly starts at lag 1 (index 2 of R’s
acf() output) rather than lag 0, which was inflating the
integrated autocorrelation time by ~2 and causing ESS to be
systematically underestimated by roughly half.
Optional serial interval estimation. Set
estimate_SI = TRUE in household_dynamics() to
jointly estimate the serial interval distribution as a Weibull(shape,
scale) alongside other model parameters. Two additional MCMC parameters
(si_shape, si_scale) are sampled. Priors:
shape ~ Uniform(0.1, 10), scale ~ Uniform(0.1, 20). The existing
serial_density() C++ function computes the discretized PMF
from the Weibull parameters at each MCMC iteration.
Missing onset time imputation. Infected
household contacts with missing (NA) onset times are now
automatically imputed during MCMC via Bayesian data augmentation,
drawing uniformly from the follow-up window and accepting/rejecting via
the full likelihood. An informational message reports the number and
percentage of missing onsets. Index case onset times must still be
non-missing.
SI defaults to bundled influenza serial
interval. The SI argument in
household_dynamics(), run_MCMC(), and
simulate_data() now defaults to NULL, which
loads the bundled flu SI from Tsang et al. (2014). Users no longer need
to call data(SI) and pass it explicitly.
simulate_data() now forces single-threaded execution to
avoid thread-unsafe R::runif calls in
parallelFor. This fixes correlated random draws that
attenuated covariate effects in simulated data.run_MCMC() now warns when with_rm = 1
(experimental random-effects branch with known per-household recording
limitation).~sex*age) are not supported with
missing covariate data.inf_factor and
sus_factor if it has missing values.household_dynamics() now returns an S3 object of
class hhdynamics_fit instead of a plain data frame. Use
summary(fit) to get the old-style parameter estimates
table.
Formula arguments (inf_factor,
sus_factor) now use R formula objects (~sex,
~age) instead of character strings ('~sex',
'~age'). Use NULL instead of '~'
when no covariates are needed.
hhdynamics_fit objects:
print(): brief model summary (data size, MCMC settings,
runtime)summary(): parameter estimates with credible
intervalscoef(): named vector of posterior meansfit$samples: posterior parameter samplesfit$log_likelihood: log-likelihood trace (for
convergence diagnostics)fit$acceptance: per-parameter acceptance ratesfit$update_accept: per-iteration infection-time update
acceptancefit$imputed_data: final imputed data matrixhousehold_dynamics() catches common
errors (missing columns, wrong formula types, bad MCMC settings) with
clear messages before the C++ MCMC runs.Fixed buffer overflow in C++ MCMC when household size exceeds 20.
The updateacceptrate matrix is now sized to
max_member instead of a hardcoded 20.
para_summary() no longer overwrites the median with
the mean, and no longer has plotting side effects
(layout(), par()).
run_MCMC() now uses message() instead
of print() for runtime output.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.