Multivariable control systems design and optimization addresses the task of regulating multiple interdependent process variables within a single framework. Unlike single‐loop controllers, ...

When model-based multivariable control made its debut in the 1980s, it was expected that process models, once acquired through a plant step test, would be durable and long lived. However, this ...

In this article, as in industry, advanced process control (APC) refers primarily to multi-variable control. Multivariable control means adjusting multiple single-loop controllers in unison, to meet ...

This R package (version: 0.1.0) implements the MRBMA method (Zuber et al., 2020), a Bayesian multivariable Mendelian randomization (MR) approach that uses Bayesian model averaging (BMA) to identify ...

This R package (version: 0.1.0) implements the MRBMA method (Zuber et al., 2020), a Bayesian multivariable Mendelian randomization (MR) approach that uses Bayesian model averaging (BMA) to identify ...