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Changelog

Source: NEWS.md

neuralsbi 0.4.0

  • The simulator can now run in parallel. Declare a future plan – library(future); plan(multisession) – and every function that calls a simulator (npe(), simulate_for_sbi(), npe_sequential(), sbc(), tarp(), posterior_predictive()) spreads the work across workers. There is no new argument to pass and no parallel variant to call: with no plan declared everything runs sequentially as before, and neuralsbi mentions the two lines above once per session (options(neuralsbi.parallel_hint = FALSE) to silence it). Each chunk of parameters draws from its own L’Ecuyer-CMRG stream, so a given set.seed() produces the same simulations sequentially and on any number of workers. See ?nsbi_parallel.

  • Long-running work now reports progress with an ETA – simulation and neural training alike, one progress step per simulation and per training epoch. With progressr installed, neuralsbi emits standard progressr updates, so progressr::handlers() and with_progress() control reporting; without it, a built-in bar needing no extra packages does the job. The training bar targets the epoch at which early stopping would fire and revises that target as the validation loss improves. See ?nsbi_progress.

  • npe(), simulate_for_sbi(), npe_sequential(), sbc(), tarp(), and posterior_predictive() gain a chunk_size argument controlling how many parameter rows go to the simulator per call. The default splits a run into about 64 chunks; because the split depends only on the number of simulations, results do not change with the number of workers. A simulator whose output must be produced in one call can set chunk_size to the full simulation budget.

  • future and progressr are Suggests, not dependencies; parallel (base R) moves into Imports. # neuralsbi 0.3.7

  • The test suite now skips its plotting tests when ggplot2/GGally are not installed, instead of failing. Both are Suggests, so R CMD check under _R_CHECK_FORCE_SUGGESTS_=false – the configuration CRAN uses on a machine without them – previously hit 5 errors from require_ggplot2(). The new skip_if_no_ggplot2()/skip_if_no_ggally() helpers mirror the skip_if_no_torch() contract already used for the neural tests, so the suite runs everywhere.

  • vignette("sir-time-varying-beta") reworks the epidemic model so that its posterior predictive tracks the observed case peaks instead of overshooting them. The introduction day and seed size are now inferred per state rather than fixed at two infections on 2020-01-21 (which left the epidemic’s phase to demographic noise: replicate simulations at one fixed parameter differed by a factor of several thousand at the peak bin), the ascertainment prior is pinned by spring-2020 seroprevalence (case counts alone identify only the product of ascertainment and incidence, and the unconstrained fit slides toward vast, barely-ascertained epidemics), and the regime-duration parameterization drops a coordinate that the simulator’s rescaling had left unidentified. The article reports the effective reproduction number Re(t) = beta(t) S(t) / (N gamma) computed from the simulator’s own susceptible trajectories, adds a prior-predictive check before training, and summarizes the posterior predictive by its median rather than its mean.

neuralsbi 0.3.6

neuralsbi 0.3.5

  • The SIR case study becomes a head-to-head comparison with the pomp package, vignette("sir-epidemic"). Both methods fit the same stochastic SIR epidemic: pomp via particle-filter MCMC (pmcmc), neuralsbi via neural posterior estimation. The vignette contrasts what each needs from the model — pomp a measurement density, neuralsbi only a simulator — overlays the two posteriors, scores their agreement with a C2ST, and confirms the neural fit with SBC. The comparison is precomputed, so pomp is needed only to regenerate the article, not to build or check the package.

neuralsbi 0.3.4

  • Plotting is now built on ggplot2 and GGally::ggpairs() instead of base graphics. pairplot(), plot_sbc(), plot_coverage(), plot_tarp(), and plot_posterior_predictive() keep their signatures (pairplot() gains an alpha argument) but now build and print a ggplot/ggmatrix object, returned invisibly for further customization. ggplot2 and GGally move to Suggests, following the same graceful-degradation pattern as torch: call any plotting function without them installed and you get an informative error, not a crash.
  • New vignette, vignette("intro-to-sbi"): a short beginner tutorial covering the three ingredients (prior, simulator, observation), amortized training, and a first calibration check, using a g-and-k distribution simulator whose likelihood has no closed form.

neuralsbi 0.3.2

  • README is now generated from README.Rmd, so the usage example runs at render time and its output cannot drift from the code. The example is a plain linear regression: the posterior recovers the ground-truth coefficients, which the reader can check against ordinary least squares.

neuralsbi 0.3.1

  • CRAN resubmission fixes. Routed the summary() and as.data.frame() methods into the single summaries help topic (they had drifted into separate .Rd files with duplicated \alias entries, which also produced duplicate HTML anchors). Wrapped theta_{<d} in \eqn{} in the made_masks docs so the Rd no longer drops braces. Dropped the bare “NPE” acronym from the DESCRIPTION to avoid the spurious misspelling note.

neuralsbi 0.3.0

  • Defaults now match Python sbi, so a workflow reads the same in both packages and results can be cross-checked. Changes to npe() defaults: the density estimator is now "maf" (was "mdn"); MDN mixture components default to 10 (was 5); NSF spline bins default to 10 (was 8); the training batch size is 200 (was 100). max_epochs is raised to 2000 as a guard cap that early stopping (patience = 20) normally reaches first, mirroring sbi’s effectively-unbounded epoch budget. lr, validation_fraction, patience, clip_grad_norm, n_transforms, and hidden already matched. Pass any of these explicitly to recover the previous behavior.

  • First CRAN submission. Dropped the development .9000 version suffix, removed the redundant Author/Maintainer fields (now derived from Authors@R), and tidied the package title.

  • Embedding networks (roadmap v0.4). embedding_mlp() builds a learned summary network that maps raw observations to a low-dimensional feature vector; pass it to npe(..., embedding_net = ) and the MDN, MAF, and NSF estimators condition on the features instead of the raw data, training the embedding jointly. The estimators still take raw x at the de_* boundary (dim_x is unchanged), so sampling and log_prob route through the embedding automatically. Ignored, with a warning, by linear_gaussian.

neuralsbi 0.2.4.9000 (development)

  • Vignettes now show real output. They are precomputed: each vignette’s evaluated source lives in vignettes/<name>.Rmd.orig, and vignettes/precompute.R bakes it into a static vignettes/<name>.Rmd (results, printed values, and figures inlined). CI and pkgdown re-render that static Markdown with no torch at build time, so the expensive neural training runs once, locally, instead of on every build. Re-run Rscript vignettes/precompute.R after editing any .Rmd.orig.
  • Two-moons calibration study (inst/benchmarks/two_moons_calibration.R): SBC, expected coverage, and TARP for a two-moons NSF fit, with figures written to docs/figures/ (roadmap milestone M2).

neuralsbi 0.2.3.9000

  • Package website built with pkgdown, deployed from CI to https://pedroliman.github.io/neuralsbi/.
  • Four vignettes that build on each other: getting started, choosing a density estimator, checking the posterior, and the SIR case study (which now also demonstrates npe_sequential()). Removed a truncated duplicate of the SIR vignette.
  • README rewritten to the standard terse form; authorship recorded in DESCRIPTION (Pedro Nascimento de Lima, with ORCID).

neuralsbi 0.2.2.9000

  • New npe_sequential(): multi-round NPE targeting a single observation via truncated-prior proposals (TSNPE, Deistler et al. 2022). Each round truncates the prior to the highest-probability region of the current posterior and retrains on all accumulated simulations; the standard NPE loss stays valid, so no importance correction is needed. Returns an nsbi_snpe fit that works with posterior(), sample(), and the diagnostics, but is only valid at the targeted x_obs. Verified against the analytic linear-Gaussian posterior.

neuralsbi 0.2.1.9000

  • New tarp() diagnostic and plot_tarp() (Lemos et al. 2023): a joint expected-coverage test using random reference points, complementing the per-parameter sbc() ranks. Detects posteriors with calibrated marginals but wrong correlation structure.
  • New plot_posterior_predictive(): marginal predictive histograms with the observation marked; returns the observation’s predictive quantiles.
  • Leakage correction is now under test: with a bounded prior, the renormalized log_prob() integrates to one over the support and returns -Inf outside it (test-posterior-normalization.R).
  • Fixed CI. R CMD check failed on three counts: the npe() example required libtorch (it now uses the torch-free linear_gaussian estimator and runs unconditionally), the hand-maintained npe.Rd/fit_mdn.Rd usage sections had drifted behind the code (missing n_restarts, clip_grad_norm, n_transforms, and the "maf"/"nsf" options), and CLAUDE.md was not in .Rbuildignore. The test-torch job also failed because torch 0.17 refuses a TORCH_HOME that does not exist; the workflow now creates it first.

neuralsbi 0.2.0.9000

  • Shared training engine for all neural estimators (train_conditional_de()): best-of-n restarts, learning-rate decay on plateau, gradient clipping, per-epoch loss history.
  • Masked Autoregressive Flow (density_estimator = "maf") and Neural Spline Flow ("nsf", autoregressive rational-quadratic splines) join the MDN and the closed-form linear_gaussian baseline.
  • Benchmark tasks (task_gaussian_linear(), task_two_moons(), task_slcp(), task_sir()) shared between tests and the inst/benchmarks/ head-to-head benchmark harness.
  • summary() methods, as.data.frame() tidy accessor, plot_coverage().
  • SIR applied case-study vignette.
  • CI: R CMD check plus a test-torch job with cached libtorch.

neuralsbi 0.1.0

  • First pilot release: priors, single-round amortized npe(), linear_gaussian and MDN estimators, posterior sampling with leakage correction, SBC, expected coverage, C2ST, posterior-predictive checks, pairplot(), plot_sbc().