overview,explicit: cite model-based recursive partitioning and partykit in cluster sections#92
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…it in cluster/partition sections Add model-based recursive partitioning (MOB; Zeileis, Hothorn, Hornik 2008) and the partykit toolkit (Hothorn & Zeileis 2015) as data-driven ways to learn a partition of context space: - 03.overview 'Conditional and Clustered Models': MOB as a supervised alternative to manual conditions and unsupervised clustering for forming groups. - 06.explicit 'Piecewise-Constant and Partition-Based Models': MOB as a tree-structured route that learns split boundaries from parameter instability, complementing TV/fused-lasso changepoints; partykit as the implementation. - manual-references.json: manual entry for partykit (JMLR, no DOI). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Summary
Cites two related works on data-driven partitioning, as requested, in the cluster/partition model sections:
MOB fits a parametric model, tests its coefficients for instability across candidate splitting variables, and recursively splits on the most unstable one. This makes it a supervised, data-driven way to learn a partition of context space, which is exactly the cluster/partition-model framing in these sections. partykit is its R implementation (with conditional-inference trees and parametric-leaf model-based trees).
Changes
content/03.overview.md— "Conditional and Clustered Models": one sentence presenting MOB as a supervised alternative to manually defined conditions and unsupervised clustering for forming groups.content/06.explicit.md— "Piecewise-Constant and Partition-Based Models": two sentences positioning MOB as a tree-structured route that learns split boundaries from parameter instability (complementing the existing total-variation / fused-lasso changepoint mention), with partykit as the implementation.content/manual-references.json— manual entryhothorn2015partykit(JMLR, no DOI).Builds on the VCVS section merged in #91. Ran the avoid-ai-writing check on both additions (clean).
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