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haze

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Velocity2.5

R package haze by dfsp-spirit — release notes from GitHub.

github.com/dfsp-spirit/haze ↗

Four dormant years end with a modernization pass and an off-by-one fix in the C++ core

neuroimagingmesh-processinginterpolationr-packagemaintenance
◆Current state
haze does nearest-neighbour smoothing and k-d tree interpolation on brain surface meshes. It sat untouched from April 2022 until July 2026, when a single release modernized it for current R versions and corrected an off-by-one error in the C++ code. It is not on CRAN and never will be — the package exceeds 50MB against CRAN's 5MB ceiling, a constraint its own initial release notes acknowledge.
◆Where it's heading
The July 2026 release arrived 56 minutes after its sibling regfusionr 0.3.0 from the same maintainer, which is the tell: this is a maintainer sweeping a set of related neuroimaging packages back into working order, not independent development on haze itself. haze is the dependency, regfusionr the consumer, and the substantive work sits on the regfusionr side. The off-by-one correction is the only change here that alters results.
◆Prediction
Expect haze to move only when a downstream dfsp-spirit package needs it to — its cadence is driven by the sibling packages, not by its own roadmap.

◆Recent moves

  1. 18d ago

    Version 0.3.0 -- Fixes and modernization

    A four-year gap closes with compatibility work for current R versions and an off-by-one correction in the C++ code — the latter quietly matters, since it sits in the smoothing path that downstream packages rely on. Distributed outside CRAN because of the package's size.

    View source ↗
  2. 4y ago

    v0.2.0 -- kdtrees

    The k-d tree functions arrive as a set: nearest-vertex lookup, plus linear and nearest-neighbour interpolation. This is what makes haze usable for mapping per-vertex data between subjects, and regfusionr took a dependency on exactly this version.

    View source ↗
  3. 4y ago

    v0.1.0: Initial release

    The founding release, with per-vertex smoothing over arbitrary k-ring neighbourhoods, sub-mesh extraction, and the k-d tree query functions. The notes are candid that CRAN distribution is off the table at over 50MB.

    View source ↗