Package: chaidr 0.1.0.9000

chaidr: CHAID and Exhaustive CHAID Decision Trees

An implementation of the CHAID (Chi-squared Automatic Interaction Detection) decision tree algorithm of Kass (1980) <doi:10.2307/2986296> and the Exhaustive CHAID variant of Biggs, de Ville, and Suen (1991) <doi:10.1080/02664769100000005>, as specified in the 'IBM SPSS' Statistics Algorithms documentation. Supports nominal, ordinal (with floating missing category), and continuous predictors, and nominal, ordinal, and continuous response variables using Pearson chi-squared, Goodman row-effects, and one-way ANOVA F tests respectively. Includes prediction, rule extraction, gains and lift analysis, validation on holdout data, and visualization via base graphics, 'Graphviz' DOT, 'plotly', and conversion to 'partykit' objects.

Authors:Osamu Morimoto [aut, cre, cph]

chaidr_0.1.0.9000.tar.gz
chaidr_0.1.0.9000.zip(r-4.7-any)chaidr_0.1.0.9000.zip(r-4.6-any)chaidr_0.1.0.9000.zip(r-4.5-any)
chaidr_0.1.0.9000.tgz(r-4.6-any)chaidr_0.1.0.9000.tgz(r-4.5-any)
chaidr_0.1.0.9000.tar.gz(r-4.7-any)chaidr_0.1.0.9000.tar.gz(r-4.6-any)
chaidr_0.1.0.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
chaidr/json (API)

# Install 'chaidr' in R:
install.packages('chaidr', repos = c('https://morimotoosamu.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/morimotoosamu/chaidr/issues

Pkgdown/docs site:https://morimotoosamu.github.io

On CRAN:

Conda:

4.30 score 1 stars 8 scripts 563 downloads 11 exports 0 dependencies

Last updated from:ee3556c8ba. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-develOK148
source / vignettesOK183
linux-releaseOK128
macos-releaseOK201
macos-oldrelOK223
windows-develOK103
windows-releaseOK135
windows-oldrelOK97
wasm-releaseOK103

Exports:chaidchaid_as_partychaid_controlchaid_dotchaid_gainschaid_graphvizchaid_importancechaid_plotlychaid_ruleschaid_tablechaid_validate

Dependencies:

chaidr detailed tutorial (Japanese)
1. クイックスタート | 2. アルゴリズムの概要 | 3. API リファレンス | 3.1 chaid(formula, data, weights, freq, method, control) | 3.2 chaid_control() — 全パラメータ | 3.3 S3 メソッド | 4. 応用機能 | 4.1 標準 CHAID と Exhaustive CHAID の比較 | 機構の違い | 実例1: Titanic — Exhaustive が細かい構造を見つける | 実例2: penguins — Exhaustive が簡潔な構造を選ぶ | なぜ結果が変わるのか — Bonferroni 乗数 B の構造差 | 計算コストの実測 | Exhaustive の副作用: 高カーディナリティ変数への偏り | 使い分け指針 | 4.2 連続目的変数(回帰木) | 非線形(U字型)関係の1階層検出 | 4.3 重み | 4.4 欠損値(Floating カテゴリ) | 4.5 予測変数間の多重比較補正(adjust_across) | 4.6 一般的な二分木(CART / rpart)との比較 | 4.7 レポーティング機能(テーブル・ルール・重要度・ゲイン・検証) | 4.8 誤分類コスト行列(costs) | 4.9 順序型目的変数(Goodman row effects model) | 5. 可視化 | 5.1 組み込み plot()(base graphics、依存なし) | 5.2 partykit 連携 | 5.3 ggparty(ggplot2 系の描画) | 回帰木の場合: 積み上げ棒ではなく箱ひげ図 | 5.4 Graphviz(DiagrammeR、出版品質) | 5.5 Plotly(インタラクティブ) | 6. 制限事項と注意点 | 参考文献

Last update: 2026-07-29
Started: 2026-07-29

Introduction to chaidr
Quick start | The algorithm in brief | Exhaustive CHAID | Reporting | Visualization | Further reading | References

Last update: 2026-07-29
Started: 2026-07-29