Traditional SPC assumes: - Observations are independent - No correlation between consecutive measurements - Random sampling from a stable process Reality in many processes: - ✗ Chemical reactors have thermal inertia - ✗ Manufacturing lines have tool wear - ✗ Measurements taken close in time are correlated - ✗ This is called autocorrelation

Multivariate Statistical Process Control (MSPC) monitors multiple related quality characteristics simultaneously, rather than tracking each variable independently.

Comprehensive SPC Implementation with R, GT Tables, and Plotly

Optimal Channel selection using ROI and mROI

This document presents a comprehensive sentiment analysis framework for analyzing text data across time series and cross-sectional dimensions. The analysis uses the `tidytext` package for text processing and sentiment scoring, combined with interactive visualizations using `plotly` and professionally formatted tables using `gt`.

Implement the SAP logic for PK and PD by creating subject-level ADaM datasets (for example, ADPPK and ADPPD) that compute PK parameters and PD responses as prespecified in the SAP.

Eliminating lots of code

TS Forecasting Using Cross Validation and Ensamble Methods