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appac 4.0.3

• Added a package vignette (vignette("appac")) covering usage on PLOT_FID and the method: the multiplicative forward model, the PCA decomposition into correlated / uncorrelated / noise components, robust estimation of the common kappa, the NA-tolerant drift/daily-factor imputation, and the change-point detectors.

appac 4.0.2

• Change-point detection moved from ‘Rbeast’ to ‘strucchange’: get_changepoints() now dates episode level breakpoints with a deterministic structural-break model (OLS-MOSUM test + BIC-optimal breakpoints()), dropping the heavy ‘Rbeast’ dependency and the need for a random seed.
• New get_variance_changepoints(): detects precision (variance) breakpoints on the noise-energy signal — the second-moment counterpart of get_changepoints().
• New example dataset Synth_data: a compact, fully synthetic stress-test set with a known ground truth (attached as attr(., "truth")) — three samples, ten peaks, three episodes split by two planted level/variance breakpoints, with brown (AR(1)) heavy-tailed noise at a 1% repeatability — for unit tests and examples.
• appac() now imputes missing area cells: peaks with up to 30% NA are filled by low-rank reconstruction (svdImpute / EM) before the fit; whole missing injections (staggered dates) are handled by the cross-sample reconstruction.
• appac() validates minimum-size and degenerate input (at least 3 samples, 2 peaks and 20 injections per sample, and non-constant areas), failing with an explanatory error instead of a deep numeric one.
• show() and print() methods for the Appac, Compensation and Correction classes: a compact summary at the console (print() also lists per-sample goodness-of-fit) instead of dumping the full object.
• check_cols() gains a verbose argument (default FALSE) that reports which column, peak and sample names were renamed.
• debias_ct() shows a progress bar during the chi-square minimisation sweep.
• Documented the package limitations (see ?appac-package).

appac 4.0.1

First CRAN release.

• appac() runs the correction pipeline: it decomposes per-cylinder peak areas by principal components into a pressure-correlated component and per-peak drift, estimates the common pressure-sensitivity coefficient kappa with a heavy-tail-robust fit on a drift-reduced signal, and removes slow drift plus a daily factor. Corrects the response of standard, atmosphere-open detectors (FID, and more weakly TCD).
• check_cols() validates and canonicalises the input columns (role-keyed, so the order of the mapping does not matter).
• debias_ct() refines the per-peak centres by closed-form chi-square minimisation, for an optional de-biased second pass.
• goodness_of_fit() reports, per peak, the reduced chi-square of the corrected areas against a noise-floor estimate.
• get_changepoints() provides Bayesian episode/breakpoint detection on the PC2 drift signal (via ‘Rbeast’).
• plot_area_pressure(), plot_area_date(), plot_residuals() and plot_area_pressure_fit() visualise a fitted object (require the suggested ‘ggplot2’ / ‘patchwork’).
• Example dataset PLOT_FID: real FID injections from several control cylinders.
• Scope: APPAC is an a posteriori correction of already-measured areas. It has no forecasting ability and makes no prediction beyond the acquired data.

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.