seqcomp 0.3.0

New Features

Added lambda_betting_agrapa() and lambda_betting_ons(), adaptive betting-fraction rules for the strong-null e-process (eprocess_betting()), adapting the aGRAPA and ONS-m algorithms of Waudby-Smith and Ramdas (2024) to Arnold et al. (2026)’s bounded strong-null setting. These adaptations are original to seqcomp, not given in either source paper.
Added build_agrapa_betting_array() and build_ons_betting_array() to construct the T x m x m lambda_param arrays these rules require for smcs_strong(), including a period argument for known periodic (e.g. day-of-week) structure.
Added a new vignette, “Choosing an Adaptive Betting Strategy: Temporal Dependence and Pathologies”, comparing naive, aGRAPA, ONS-m, and Arnold et al.’s quantile-specific betting rules across structural breaks, autocorrelated noise, near-degenerate bounds, volatility clustering, and closed-testing dilution.

compare_multiple_forecasts() is renamed to smcs_compare(). This function was never released on CRAN (only available on GitHub as part of unreleased 0.2.0 development), so this is not a breaking change for any CRAN user.

Removed the floor of 1 on the intrinsic time in cs_bernstein() after verifying it does not cause any numerical issues (as for validity, the floor was a conservative measure in the fist place, so nothing that affects validity).

Introduced Sequential Model Confidence Sets (SMCS) for multi-model evaluations, based on Arnold et al. (2026).
Added smcs_compare() as a high-level wrapper to sequentially compare 3 or more forecasters simultaneously.
Added smcs_strong() and smcs_weak() to construct confidence sets under the strong, uniformly weak, and weak null hypotheses.
Implemented predictable betting-style e-processes (eprocess_betting()) and adaptive betting fractions for quantile forecasts (lambda_betting_quantile()).
Added vovk_wang_merge() for highly efficient \(O(m \log m)\) closed-testing e-value merging.

Updated documentation and citations to reflect the official publication of Choe & Ramdas (2024) in Operations Research, 72(4).