arabic-survey-analyzer 0.1.0

Smart Arabic survey analysis: Likert, reliability, validity, inferential tests, open-text analysis and automatic Arabic Word/Excel reports

ArabicSurveyAnalyzer — محلل الاستبيانات العربية

مكتبة Python ذكية لتحليل الاستبيانات باللغة العربية: من ملف Excel/CSV إلى تقرير عربي أكاديمي كامل (Word + Excel + JSON + رسوم بيانية) في سطور قليلة.

A smart Python library for Arabic-language survey analysis: from a raw Excel/CSV file to a complete academic Arabic report (Word + Excel + JSON + publication-quality charts) in a few lines of code.

Author: Dr Merwan Roudane — https://github.com/merwanroudane/surveyarabic

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Table of contents — المحتويات

1. Features — المزايا
2. Installation — التثبيت
3. Input data format — صيغة البيانات
4. Quick start — البداية السريعة
5. SurveyAnalyzer — full API reference
6. Results objects — بنية النتائج
7. Outputs — المخرجات
8. Likert scales — مقاييس ليكرت
9. Low-level module API
10. Charts — الرسوم البيانية
11. MATLAB color palettes — الألوان
12. AI / LLM integration — الذكاء الاصطناعي
13. Streamlit web app — واجهة الويب
14. Demo & tests — المثال والاختبارات
15. Methodological & ethical notes — ملاحظات منهجية

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1. Features — المزايا

الوحدة Module	ما تقدمه What you get
التحليل الوصفي Descriptive	sample overview, frequencies & percentages, mean, SD, median, mode, min/max, item ranking, automatic Arabic agreement-level interpretation
مقاييس Likert	3/5/7-point Arabic presets, custom mappings, Arabic-text → numeric conversion, diverging stacked bar chart
الثبات Reliability	Cronbach's alpha, McDonald's omega (ω), corrected item-total correlation, alpha-if-item-deleted, Composite Reliability (CR), AVE
الصدق Validity	item-dimension correlations, KMO, Bartlett's sphericity, EFA (varimax, Kaiser criterion), Fornell–Larcker, HTMT
الاختبارات الاستدلالية Inferential	t-test / Mann-Whitney, ANOVA / Kruskal-Wallis (automatic normality-based selection), effect sizes (Cohen's d, η², ε², rank-biserial), chi-square + Cramér's V, Pearson/Spearman/Kendall, OLS regression
جودة البيانات Data quality	straightlining, low variance, missingness, Mahalanobis multivariate outliers, weak/duplicate open answers — review report only, no automatic deletion
الإجابات المفتوحة Open text	Arabic normalization, keyword extraction, lexicon-based sentiment (with negation), representative quotes, auto Arabic summary paragraph, optional LLM hook
المخرجات Outputs	full RTL Arabic Word report, styled RTL Excel workbook, raw JSON, PNG charts (Parula palette)
الواجهة UI	Streamlit web application

2. Installation — التثبيت

git clone https://github.com/merwanroudane/surveyarabic
cd surveyarabic

pip install -e .          # core library
pip install -e .[app]     # + streamlit & plotly (web interface)
pip install -e .[ai]      # + anthropic SDK (optional LLM analysis)

Core dependencies (installed automatically): pandas, numpy, scipy, statsmodels, factor_analyzer, matplotlib, openpyxl, python-docx, arabic-reshaper, python-bidi.

Compatible with pandas 3.x (new str dtype) and scikit-learn 1.8 (a built-in shim fixes the factor_analyzer ↔ sklearn incompatibility).

3. Input data format — صيغة البيانات

One row per respondent, one column per question. Accepted files: .xlsx, .xls, .xlsm, .csv (UTF-8 or Windows-1256) — or pass a ready pandas.DataFrame directly.

الجنس	العمر	Q1	Q2	…	Q10	ملاحظات
ذكر	25-35	موافق	موافق بشدة	…	محايد	الخدمة ممتازة…
أنثى	36-45	محايد	موافق	…	موافق	أعاني من بطء…

Likert answers may be Arabic text (موافق بشدة, …) or numbers (1–5); both are handled. Text matching is normalization-tolerant (غير موافق جداً ≡ غير موافق جدا, hamza/ta-marbuta variants unified).

4. Quick start — البداية السريعة

from arabic_survey_analyzer import SurveyAnalyzer

analyzer = SurveyAnalyzer("survey.xlsx",
                          title="تقرير تحليل استبيان جودة الخدمة",
                          researcher="د. مروان رودان")

analyzer.define_likert_scale("5-point")

analyzer.set_dimensions({
    "جودة الخدمة": ["Q1", "Q2", "Q3", "Q4"],
    "الرضا":       ["Q5", "Q6", "Q7"],
    "الثقة":       ["Q8", "Q9", "Q10"],
})

analyzer.set_demographics(["الجنس", "العمر", "المستوى التعليمي"])

analyzer.run_all(group_vars=["الجنس"],
                 text_cols=["ملاحظات"],
                 regression_dependent="الرضا")

paths = analyzer.export_all("output")
# output/تقرير_الاستبيان.docx  +  نتائج_الاستبيان.xlsx  +  results.json  +  charts/*.png

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5. SurveyAnalyzer — full API reference

from arabic_survey_analyzer import SurveyAnalyzer

5.1 Constructor

SurveyAnalyzer(source, sheet_name=0, title="تقرير تحليل الاستبيان", researcher="")

Parameter	Type	Default	Description
source	str path or DataFrame	—	.xlsx/.xls/.csv file path, or a pandas DataFrame
sheet_name	int/str	0	Excel sheet to read
title	str	تقرير تحليل الاستبيان	Title shown on the Word report cover
researcher	str	""	Researcher name shown on the cover

On load: column names stripped, fully-empty rows/columns dropped, text cells trimmed, empty strings → missing.

5.2 Configuration methods

All configuration methods return self, so they can be chained.

define_likert_scale(scale_type="5-point", mapping=None)

Parameter	Type	Description
scale_type	str	"3-point", "5-point" or "7-point" — sets scale bounds and default Arabic label presets
mapping	dict or None	optional custom {label: number} map; labels are Arabic-normalized before matching

# preset
analyzer.define_likert_scale("5-point")

# fully custom labels
analyzer.define_likert_scale("5-point", mapping={
    "أرفض بشدة": 1, "أرفض": 2, "لا أدري": 3, "أوافق": 4, "أوافق بشدة": 5,
})

set_dimensions(dimensions)

analyzer.set_dimensions({
    "جودة الخدمة": ["Q1", "Q2", "Q3", "Q4"],
    "الرضا":       ["Q5", "Q6", "Q7"],
})

{dimension_name: [item_columns]}. Raises KeyError listing any column that does not exist in the data.

set_demographics(columns)

analyzer.set_demographics(["الجنس", "العمر", "المستوى التعليمي", "سنوات الخبرة"])

Columns to describe with frequency tables + pie/bar charts. Unknown columns are silently skipped.

set_text_columns(columns)

analyzer.set_text_columns(["ملاحظات", "اقتراحات"])

Open-ended question columns for qualitative analysis.

5.3 Analysis methods

Each run_* method returns its result table(s) and also stores them in analyzer.results / analyzer.tables (see §6).

run_descriptive()

items_table, dims_table = analyzer.run_descriptive()

Produces: sample overview, demographic frequency tables, per-item statistics (count, mean, SD, median, mode, min, max, rank, agreement level), per-dimension statistics, Likert category percentages, and the auto-written Arabic paragraph analyzer.paragraphs["dimensions"].

run_reliability()

rel_table = analyzer.run_reliability()

Per dimension + overall: Cronbach's α, McDonald's ω, CR, AVE, Arabic interpretation. Item-total tables per dimension are stored in analyzer.results["reliability"]["item_total"].

run_validity(n_factors=None)

Parameter	Type	Description
n_factors	int or None	number of EFA factors; None → Kaiser criterion (eigenvalues > 1)

val = analyzer.run_validity()        # dict, see §6
val = analyzer.run_validity(n_factors=3)

Produces: item-dimension correlations, KMO + Bartlett table, EFA loadings

• explained variance + eigenvalues (scree), Fornell–Larcker matrix, HTMT matrix. If EFA fails (e.g. singular matrix) the error string is stored in val["efa_error"] and the rest still completes.

run_group_tests(group_var, force=None)

Parameter	Type	Description
group_var	str	grouping column (e.g. "الجنس")
force	None / "parametric" / "nonparametric"	override the automatic Shapiro-Wilk-based test choice

t = analyzer.run_group_tests("الجنس")                       # auto choice
t = analyzer.run_group_tests("العمر", force="nonparametric") # always KW/MW

Test selection logic:

Groups	Normal (Shapiro p ≥ .05 in all groups)	Not normal
2	Welch t-test + Cohen's d	Mann-Whitney U + rank-biserial r
3+	one-way ANOVA + η²	Kruskal-Wallis + ε²

Can be called repeatedly with different group_vars; each adds a table and an Arabic paragraph.

run_correlations(method="pearson")

r = analyzer.run_correlations()              # Pearson
r = analyzer.run_correlations("spearman")    # or "kendall"

Correlation + p-value matrices between dimension scores (analyzer.results["correlation"]["r"] / ["p"]).

run_regression(dependent, predictors=None)

Parameter	Type	Description
dependent	str	dimension name used as the outcome
predictors	list[str] or None	predictor dimensions; None → all other dimensions

coef, summary = analyzer.run_regression("الرضا")
coef, summary = analyzer.run_regression("الرضا", predictors=["جودة الخدمة"])

OLS on respondent-level dimension scores; returns the coefficient table (B, SE, t, p, significance) and the model summary (R², adj-R², F, p).

run_data_quality()

summary = analyzer.run_data_quality()

Flags per respondent: straightlining (≥ 90 % identical answers), low variance (SD < 0.5), high missingness (> 20 %), Mahalanobis outliers (χ², p < .001), weak/duplicate open answers. Detailed per-respondent table in analyzer.results["quality"]["detail"]. Nothing is deleted.

run_text_analysis(columns=None, llm_callable=None)

(alias: run_ai_open_text_analysis — proposal-compatible name)

Parameter	Type	Description
columns	list[str] or None	open-text columns; None → those from set_text_columns
llm_callable	f(prompt)->str or None	optional LLM for deep thematic analysis (§12)

out = analyzer.run_text_analysis(["ملاحظات"])
out["ملاحظات"]["keywords"]            # DataFrame: keyword / count / %
out["ملاحظات"]["sentiment_summary"]   # DataFrame: إيجابي/محايد/سلبي + %
out["ملاحظات"]["quotes"]              # list of representative quotes
out["ملاحظات"]["paragraph"]           # auto Arabic summary paragraph

run_all(group_vars=None, text_cols=None, regression_dependent=None)

Runs the full pipeline in the right order: descriptive → data quality → reliability → validity → group tests (each var) → correlations → regression (optional) → text analysis (optional).

analyzer.run_all(group_vars=["الجنس", "المستوى التعليمي"],
                 text_cols=["ملاحظات"],
                 regression_dependent="الرضا")

5.4 Export methods

export_charts(out_dir="charts")

Renders every chart available for the analyses already run; returns {section: [png paths]} and caches it in analyzer.charts.

export_excel(path="نتائج_الاستبيان.xlsx")

All result tables → one styled workbook (navy headers, zebra rows, borders, auto column widths, RTL sheet view, frozen header row).

export_report(path="تقرير_الاستبيان.docx", charts_dir="charts")

Full Arabic academic Word report (see §7.1). Charts are rendered first if not already cached.

export_json(path="results.json")

Entire results dict serialized to UTF-8 JSON (DataFrames → record lists) — your machine-readable audit trail.

export_all(out_dir="output")

Charts + Excel + Word + JSON into one folder. Returns {"excel": ..., "word": ..., "json": ..., "charts": ...} paths.

5.5 Attributes

Attribute	Type	Content
analyzer.df	DataFrame	cleaned raw data
analyzer.num_df	DataFrame	data with Likert items converted to numbers
analyzer.scores	DataFrame	respondent-level mean score per dimension
analyzer.items	list[str]	all item columns (property)
analyzer.results	dict	every analysis result (§6)
analyzer.tables	dict[str, DataFrame]	flat table registry → Excel sheets
analyzer.paragraphs	dict	auto-generated Arabic narrative
analyzer.charts	dict	section → list of PNG paths

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6. Results objects — بنية النتائج

analyzer.results keys after run_all:

results
├── descriptive
│   ├── overview          DataFrame  — participants / variables / missing
│   ├── demographics      {var: DataFrame}  — frequencies + %
│   ├── items             DataFrame  — per-item stats + rank + level
│   ├── dimensions        DataFrame  — per-dimension stats + rank + level
│   └── percentages       DataFrame  — % per Likert category per item
├── quality
│   ├── detail            DataFrame  — per-respondent flags
│   └── summary           DataFrame  — counts + % per indicator
├── reliability
│   ├── summary           DataFrame  — α, ω, CR, AVE + interpretation
│   └── item_total        {dim: DataFrame}  — r(item, total), α-if-deleted
├── validity
│   ├── item_dimension    DataFrame  — item ↔ dimension r + p
│   ├── kmo_bartlett      DataFrame
│   ├── efa_loadings      DataFrame  — loadings + communalities
│   ├── efa_variance      DataFrame  — eigenvalue / % / cumulative %
│   ├── eigenvalues       list       — for the scree plot
│   ├── fornell_larcker   DataFrame  — √AVE diagonal vs correlations
│   └── htmt              DataFrame
├── group_tests           {group_var: DataFrame}  — test, stat, p, effect size
├── correlation           {"r": DataFrame, "p": DataFrame}
├── regression            {"coefficients": DataFrame, "summary": DataFrame}
└── text                  {column: {keywords, sentiment_detail,
                                    sentiment_summary, quotes,
                                    paragraph, llm_analysis}}

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7. Outputs — المخرجات

7.1 Word report (RTL) — التقرير العربي

True right-to-left layout (OOXML <w:bidi/> paragraphs, <w:bidiVisual/> tables), Traditional Arabic body font, navy-styled tables, embedded charts. Sections:

1. صفحة عنوان — cover page
2. مقدمة — introduction
3. وصف العينة — sample description (+ demographic charts)
4. جودة البيانات — data-quality summary
5. التحليل الوصفي للفقرات (+ Likert & means charts)
6. تحليل المحاور (+ bar & radar charts) مع فقرة نتائج آلية
7. الثبات (+ α/ω chart) مع فقرة تفسيرية
8. الصدق: KMO/بارتليت، EFA، فورنيل-لاركر، HTMT (+ scree)
9. الاختبارات الاستدلالية (+ boxplots) مع فقرات تفسيرية
10. الارتباط (+ heatmap) والانحدار
11. تحليل الإجابات المفتوحة (+ keywords & sentiment charts، اقتباسات)
12. ملخص النتائج والتوصيات — auto-generated recommendations

7.2 Excel workbook — ملف الجداول

One sheet per table (~26 sheets for the full pipeline): نظرة عامة، توزيع كل متغير ديموغرافي، إحصاءات الفقرات/المحاور، جودة البيانات (ملخص + تفصيلي)، الثبات، ارتباط الفقرات لكل محور، صدق الاتساق الداخلي، KMO وبارتليت، التشبعات العاملية، التباين المفسر، فورنيل-لاركر، HTMT، الفروق حسب كل متغير، مصفوفة الارتباط، الانحدار، الكلمات المفتاحية والمشاعر لكل سؤال مفتوح.

7.3 JSON — سجل التدقيق

Everything in results as UTF-8 JSON for reproducibility, audit, or downstream apps.

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8. Likert scales — مقاييس ليكرت

Presets — الإعدادات الجاهزة

scale_type	Labels (value)
"3-point"	غير موافق (1) محايد (2) موافق (3)
"5-point"	غير موافق بشدة/جداً (1) غير موافق (2) محايد (3) موافق (4) موافق بشدة/جداً (5)
"7-point"	غير موافق بشدة (1) … محايد (4) … موافق بشدة (7)

Agreement levels — مستويات الموافقة

Equal-width thirds of the scale range. For a 5-point scale:

Mean range	Level
1.00 – 2.33	منخفض
2.34 – 3.67	متوسط
3.68 – 5.00	مرتفع

from arabic_survey_analyzer import agreement_level
agreement_level(4.12)                          # 'مرتفع'   (5-point default)
agreement_level(4.12, scale_min=1, scale_max=7) # 7-point scale

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9. Low-level module API

Every stage is usable standalone with plain DataFrames.

9.1 Reading data

from arabic_survey_analyzer import read_survey
df = read_survey("survey.xlsx")            # or .csv (UTF-8 / Windows-1256)
df = read_survey(existing_dataframe)       # pass-through + cleaning

9.2 Likert conversion

from arabic_survey_analyzer.likert import build_mapping, to_numeric

mapping = build_mapping("5-point")                       # preset
mapping = build_mapping(mapping={"أوافق": 4, ...})       # custom
num_df  = to_numeric(df, items=["Q1", "Q2"], mapping=mapping)

9.3 Descriptives

from arabic_survey_analyzer.descriptive import (
    sample_overview, frequency_table, demographics_tables,
    item_statistics, dimension_statistics, dimension_scores,
    likert_percentages)

item_statistics(num_df, ["Q1", "Q2"], scale_min=1, scale_max=5)
dimension_statistics(num_df, {"محور أ": ["Q1", "Q2"]})
scores = dimension_scores(num_df, dimensions)   # n × dims, for tests/SEM

9.4 Reliability

from arabic_survey_analyzer import cronbach_alpha, mcdonald_omega
from arabic_survey_analyzer.reliability import (
    item_total_statistics, composite_reliability_ave, reliability_table)

cronbach_alpha(num_df[["Q1", "Q2", "Q3"]])      # float
mcdonald_omega(num_df[["Q1", "Q2", "Q3"]])      # float (1-factor ω total)
item_total_statistics(num_df[["Q1", "Q2", "Q3"]])  # DataFrame
cr, ave = composite_reliability_ave(num_df[["Q1", "Q2", "Q3"]])
reliability_table(num_df, dimensions)           # full Arabic table

9.5 Validity

from arabic_survey_analyzer.validity import (
    item_dimension_correlations, kmo_bartlett, efa,
    fornell_larcker, htmt_matrix)

table, kmo_value, bartlett_p = kmo_bartlett(num_df, items)
loadings, variance, eigenvalues = efa(num_df, items)            # Kaiser
loadings, variance, eigenvalues = efa(num_df, items, n_factors=3,
                                      rotation="varimax")       # fixed k
fornell_larcker(num_df, dimensions)
htmt_matrix(num_df, dimensions)

9.6 Inferential tests

from arabic_survey_analyzer.inferential import (
    compare_groups, chi_square_table, correlation_matrix, linear_regression)

compare_groups(num_df, df["الجنس"], scores)                  # auto test
compare_groups(num_df, df["العمر"], scores, force="parametric")
res, crosstab = chi_square_table(df, "الجنس", "المستوى التعليمي")
r_mat, p_mat  = correlation_matrix(scores, method="spearman")
coef, summary = linear_regression(scores, "الرضا", ["جودة الخدمة", "الثقة"])

9.7 Data quality

from arabic_survey_analyzer.data_quality import (
    straightlining, low_variance, missing_per_respondent,
    mahalanobis_outliers, weak_text_answers, quality_report)

detail, summary = quality_report(df, num_df, items, text_cols=["ملاحظات"])
straightlining(num_df, items, threshold=0.9)
mahalanobis_outliers(num_df, items, alpha=0.001)

9.8 Text analysis

from arabic_survey_analyzer.text_analysis import (
    clean_answers, keyword_frequencies, sentiment_score, sentiment_analysis,
    representative_quotes, analyze_open_text)
from arabic_survey_analyzer.textutils import normalize_ar, ar

keyword_frequencies(df["ملاحظات"], top_n=20)
sentiment_score("الخدمة ممتازة والموظفون متعاونون")    # 1.0
detail, summary = sentiment_analysis(df["ملاحظات"])
normalize_ar("إستبيانٌ")     # 'استبيان' (hamza/diacritics unified)
ar("جودة الخدمة")            # reshaped+bidi string for matplotlib labels

---

10. Charts — الرسوم البيانية

All charts: Arabic-safe text (arabic-reshaper + python-bidi, Tahoma), 150 dpi PNG, Parula palette by default. Each function saves a PNG and returns its path.

from arabic_survey_analyzer import visualization as viz

Function	Chart
viz.likert_diverging_chart(pct_df, out_dir)	diverging stacked bars (Heiberger–Robbins) per item
viz.item_means_chart(item_table, out_dir, scale_max=5)	horizontal item means + SD error bars
viz.dimension_means_chart(dim_table, out_dir, scale_max=5)	dimension means + SD
viz.dimensions_radar_chart(dim_table, out_dir, scale_max=5)	radar profile (needs ≥ 3 dims)
viz.correlation_heatmap(r_mat, out_dir, colorscale="Parula")	annotated heatmap
viz.demographic_chart(freq_table, var_name, out_dir)	pie (≤ 5 categories) or bars
viz.group_boxplot(scores, df["الجنس"], "الرضا", out_dir)	boxplots by group
viz.scree_plot(eigenvalues, out_dir)	scree + Kaiser line
viz.reliability_chart(rel_table, out_dir)	α vs ω bars + 0.70 threshold
viz.keywords_chart(kw_df, col, out_dir)	top-15 keyword bars
viz.sentiment_chart(sent_summary, col, out_dir)	sentiment pie
viz.interactive_dimension_chart(dim_table)	plotly interactive bar (returns Figure)

path = viz.dimension_means_chart(dims_table, "charts", scale_max=5)
fig  = viz.interactive_dimension_chart(dims_table, colorscale="Parula")
fig.show()

---

11. MATLAB color palettes — الألوان

parula_colors(n) reproduces MATLAB R2014b Parula from the official 64 RGB stops; Parula is the default everywhere.

from arabic_survey_analyzer import (parula_colors, matlab_jet_colors,
                                    turbo_colors, bluered_colors,
                                    sinha_colors, resolve_colorscale)

parula_colors(8)            # ['#352a87', '#2058b0', ...]  8 hex colours
matlab_jet_colors(16)       # classic MATLAB Jet
turbo_colors(16)            # Google Turbo
bluered_colors(16)          # blue-white-red diverging
sinha_colors(16)            # navy-teal-green-gold-red ramp

resolve_colorscale("Parula")        # plotly colorscale [[0.0,'#352a87'],...]
from arabic_survey_analyzer.colors import get_cmap
cmap = get_cmap("Parula")           # matplotlib colormap object

---

12. AI / LLM integration — الذكاء الاصطناعي

The library never requires an API. Any function f(prompt: str) -> str works as llm_callable; two convenience builders are provided.

Local model (full privacy — recommended for sensitive data)

from arabic_survey_analyzer.ai_analysis import make_openai_compatible_callable

llm = make_openai_compatible_callable("http://localhost:11434/v1",
                                      model="qwen2.5")        # Ollama
analyzer.run_text_analysis(["ملاحظات"], llm_callable=llm)

Anthropic API

from arabic_survey_analyzer.ai_analysis import make_anthropic_callable

llm = make_anthropic_callable(model="claude-sonnet-4-6")  # uses ANTHROPIC_API_KEY
analyzer.run_text_analysis(["ملاحظات"], llm_callable=llm)

Custom function

def my_llm(prompt: str) -> str:
    ...  # anything: requests.post to your server, a pipeline, etc.
    return generated_text

analyzer.run_text_analysis(["ملاحظات"], llm_callable=my_llm)

The LLM output appears under "تحليل الذكاء الاصطناعي" in the Word report and in results["text"][col]["llm_analysis"]. LLM failures never break the pipeline — the error message is stored instead.

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13. Streamlit web app — واجهة الويب

streamlit run arabic_survey_analyzer/streamlit_app.py

Workflow: upload file → preview → pick Likert scale, demographics, open questions and dimensions in the sidebar → run → browse 6 result tabs (الوصفي / المحاور / الثبات والصدق / الفروق / الإجابات المفتوحة / جودة البيانات) → download Word / Excel / JSON.

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14. Demo & tests — المثال والاختبارات

python examples/generate_sample_data.py   # synthetic Arabic survey (n=220)
python examples/run_demo.py               # full pipeline -> examples/output/
python -m pytest tests -q                 # 8 unit tests

The synthetic data has person-level latent traits per dimension, so the demo produces realistic psychometrics (α ≈ 0.81–0.84, KMO ≈ 0.74).

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15. Methodological & ethical notes — ملاحظات منهجية

• مخرجات الذكاء الاصطناعي احتمالية وليست حكماً نهائياً، ولا تعوض الحكم العلمي للباحث.
• لا يُحذف أي مشارك آلياً؛ وحدة جودة البيانات تنتج تقرير مراجعة فقط.
• استخدم نموذجاً محلياً عند تحليل بيانات حساسة، ولا تُرسل بيانات شخصية لأي خدمة خارجية دون موافقة.
• كل خطوة تحليلية موثقة في results.json (سجل تدقيق Audit trail).
• ω يحسب من نموذج عامل واحد (minres)؛ عند فشل التقدير يستخدم احتياطي PCA.
• اختيار الاختبار المعلمي/اللامعلمي يعتمد على Shapiro-Wilk داخل كل مجموعة (عينة ≤ 500)، ويمكن فرضه يدوياً عبر force=.

License

MIT © Dr Merwan Roudane