C2REGbase 0.1.1

A minimal linear regression pipeline providing six functions for data inspection, type adjustment, listwise cleaning, OLS fitting, parameter export, and diagnostics.

📘 README.md (End-User Version — Clean & Professional)

C2REGbase

C2REGbase provides a simple and structured workflow for running linear regression using six clear steps.
It is designed for analysts who want a clean, consistent, and reproducible regression process without the complexity of full statistical frameworks.

The library performs:

1. Variable inspection
2. Type adjustments
3. Missing-value summary and listwise deletion
4. Ordinary Least Squares (OLS) regression
5. Export of parameter estimates in a dataset-friendly format
6. Diagnostics including standard errors, t-values, p-values, and confidence intervals

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🔧 Installation

Install from PyPI:

pip install C2REGbase

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🚀 Quick Example

from C2REGbase import (
    inspect_variables,
    adjust_variable_types,
    summarize_missing_listwise,
    fit_ols,
    export_outest,
    compute_diagnostics
)
import pandas as pd
import numpy as np

# Sample dataset
df = pd.DataFrame({
    "bweight": np.random.normal(3000, 600, size=200),
    "matage": np.random.randint(18, 40, size=200),
    "ht": np.random.choice(["yes", "no"], size=200),
    "sex": np.random.choice(["male", "female"], size=200)
})

# Step 1: Inspect structure
inspect_variables(df)

# Step 2: Adjust variable types
df = adjust_variable_types(df, {"ht": "category", "sex": "category"})

# Step 3: Missing-value summary & listwise deletion
df_clean, mv_summary = summarize_missing_listwise(
    df, ["bweight", "matage", "ht", "sex"]
)

# Step 4: Fit OLS model
results, stats = fit_ols(
    df_clean,
    dependent="bweight",
    independents=["matage", "ht", "sex"]
)

# Step 5: Export parameter estimates (OUTEST-style)
outest_df = export_outest(results, dependent="bweight")

# Step 6: Compute diagnostics (stderr, t, p-value, CI)
diag_df = compute_diagnostics(results, alpha=0.05)

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📘 Notes

• C2REGbase does not perform automatic transformations (e.g., log, squared, interaction terms). Users should create any derived variables manually in their DataFrame before fitting.

• Categorical variables are automatically dummy-encoded during regression.

• The output includes:

  • ANOVA
  • RMSE
  • R² and Adjusted R²
  • Coefficient table
  • OUTEST-like export
  • Diagnostic table with confidence intervals

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📄 License

C2REGbase is open-source under the MIT License.

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