Multivariate Latent Variable Methods (LVM)
- Theory, algorithms and parameter estimation solutions for reduced rank projection and regression methods (PLS,PCA)
- Application of multi-block and multi-path methods to complex data structures.
- Optimization solutions with embedded latent variable models for assisted process operations.
- Monitoring, troubleshooting, fault-detection control and optimization of process systems with LVM.
algorithms and methods for process analytical technology
- Lean development and low cost of ownership methods for PAT (EIOT).
- Self-sustaining methodologies (structured adaptive techniques).
- Data fusion.
- Integration of PAT metrics into state estimation, reconciliation and non-linear control solutions
statistics and fundamental modeling
- Model Based Experimental Design for non-linear differential algebraic systems. i) Model Discrimination, ii) Model Parametrization, iii) Model Exploration.
- Uncertainty handling and analysis for indirect measurements (PAT) incorporated into fundamental models.
hybrid modeling systems
- Process monitoring, state-estimation and fault detection using fundamentally derived and empirically derived models.
- Application of optimization methods to all of the above.
pyPhi - A Python package for Multivariate Analysis
pyphi is a python based package for multivariate analysis.
Version 1.0 includes: Principal Components Analysis, Projection to Latent Structures, LWPLS, Savitzy-Golay derivative transform, Standard Normal Variate transform. PCA and PLS routines support to missing data.
pyphi_plots is a package with a variety of plotting tools for models created with pyphi
Dependencies: numpy, scipy, pandas, datetime, bokeh , matplotlib
The packages can be downloaded from: