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.