: She has compared and enhanced techniques like AMOPLS and AComDim , extending them to unbalanced experimental designs using Generalized Linear Model (GLM) versions of matrix decomposition.

: She has authored accessible guides on Linear Regression, ANOVA, and Linear Mixed Models tailored specifically for chemists and life-science researchers. 4. Application Domains

Manon Martin is a prominent researcher at the , specializing in biostatistics and the analysis of high-dimensional "omics" data. Her work primarily focuses on developing statistical frameworks and software to interpret complex experimental designs in fields like metabolomics and peptidomics.

: Martin has significantly advanced the ASCA (ANOVA-Simultaneous Component Analysis) family of methods. Her work on LiMM-PCA combines Linear Mixed Models (LMM) with Principal Component Analysis (PCA) to handle advanced designs with random effects and quantitative variables.

While her focus is statistical, her work is applied across diverse scientific areas:

: An R package designed for the linear modeling of high-dimensional designed data based on the ASCA/APCA family.

Below is a structured "paper" summarizing the core pillars of her scientific contributions and research focus.