NNS examples

The numbered package vignettes are the canonical NNS example curriculum. They define the statistical narrative, section order, datasets, and intended interpretation for companion implementations. The Python package follows this sequence and treats R as the source of truth.

Canonical package vignettes

# Topic R vignette Python companion
01 Overview Overview 01_overview.py
02 Partial Moments Partial Moments 02_partial_moments.py
03 Correlation and Dependence Correlation and Dependence 03_correlation_and_dependence.py
04 Normalization and Rescaling Normalization and Rescaling 04_normalization_and_rescaling.py
05 Sampling and Simulation Sampling and Simulation 05_sampling_and_simulation.py
06 Comparing Distributions Comparing Distributions 06_comparing_distributions.py
07 Clustering and Regression Clustering and Regression 07_clustering_and_regression.py
08 Classification Classification 08_classification.py
09 Forecasting Forecasting 09_forecasting.py

Canonical changes should be made in the R vignette first. A companion port may use language-appropriate syntax and containers, but should preserve the same statistical demonstration and interpretation.

Applied studies and extended examples

The following material applies NNS to specific problems, comparisons, and research questions. These examples are useful applications, but they do not replace the numbered package curriculum.

Basic statistics

  1. Partial Moment Equivalences
  2. Bayes' Theorem
  3. CDFs and ANOVA
  4. Bias and Confidence Intervals
  5. Partial Moments Estimation Error

Regression

  1. Overview
  2. Curve Fitting
  3. Nonparametric Regression Using Clusters
  4. Clustering and Curve Fitting By Line Segments
  5. Regression Residuals
  6. Multiple Imputation
  7. Logistic Regression Binary Classification
  8. Boston Housing

Machine learning

  1. Partitional Estimation Using Partial Moments
  2. NNS Regression in Machine Learning
  3. Classification Using NNS Clustering Analysis
  4. NNS vs. xgboost
  5. Time-Series Classification
  6. Time-Series Classification II
  7. Spiral Matching Example
  8. MNIST

Time-series forecasting

  1. Overview
  2. NNS vs. KERAS
  3. NNS vs. prophet
  4. Tides
  5. NNS vs. N-HiTS
  6. NNS Time-Series Prediction Interval Benchmark

Econometrics

  1. Econometrics Critiques and Solutions
  2. VAR Alternative
  3. Nowcasting
  4. Causal Analysis
  5. Federal Reserve Causal Analysis
  6. Causal Inference

References

The applied examples are demonstrations rather than exhaustive proofs. See the book and the research papers for the underlying arguments.