Changes in version 1.3.3 (2026-04-08)                  

Bug fixes

  - Fixed .effective_sample_size() error on constant MCMC chains. When a
    parameter has zero variance (chain never moved), acf() returns NaN,
    causing if (pair_sum < 0) to fail with "missing value where
    TRUE/FALSE needed." Now returns ESS = 1 for constant chains and
    handles any remaining NaN autocorrelation values defensively. Fixes
    CRAN check ERROR on r-devel-linux-x86_64-fedora-gcc.

                 Changes in version 1.3.2 (2026-03-26)                  

Test suite improvements

  - 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.

Build system

  - Use 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_.

                        Changes in version 1.3.1                        

Bug fixes

  - 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.

                        Changes in version 1.3.0                        

New features

  - 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).

Bug fixes

  - 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.

                        Changes in version 1.2.0                        

New features

  - 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.

Bug fixes

  - 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.

Other

  - Added testthat test suite (47 tests across 4 files).
  - run_MCMC() now warns when with_rm = 1 (experimental random-effects
    branch with known per-household recording limitation).

                        Changes in version 1.1.0                        

New features

  - Missing covariate imputation via Bayesian data augmentation. Factor
    covariates with missing values (NA) are now automatically imputed
    during MCMC, jointly with onset time imputation. The MCMC draws from
    the full conditional distribution at each iteration using a uniform
    categorical proposal over factor levels. No user action is required
    — simply pass data with NAs in factor covariates. An informational
    message reports the number and percentage of missing values per
    covariate.

Limitations

  - Only categorical (factor) covariates are imputed. Continuous
    covariates with missing values will still produce an error.
  - Interaction terms (~sex*age) are not supported with missing
    covariate data.
  - The same variable cannot appear in both inf_factor and sus_factor if
    it has missing values.

                        Changes in version 1.0.1                        

Breaking changes

  - 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.

New features

  - S3 methods for hhdynamics_fit objects:
    
      - print(): brief model summary (data size, MCMC settings, runtime)
      - summary(): parameter estimates with credible intervals
      - coef(): named vector of posterior means

  - Full MCMC output preserved in the fit object. Access via:
    
      - fit$samples: posterior parameter samples
      - fit$log_likelihood: log-likelihood trace (for convergence
        diagnostics)
      - fit$acceptance: per-parameter acceptance rates
      - fit$update_accept: per-iteration infection-time update
        acceptance
      - fit$imputed_data: final imputed data matrix

  - Input validation in household_dynamics() catches common errors
    (missing columns, wrong formula types, bad MCMC settings) with clear
    messages before the C++ MCMC runs.

Bug fixes

  - 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.

                         Changes in version 1.0                         

  - Initial release.