- π University Teaching (PUC): Lecturer of the undergraduate course Statistical Applications (Aplicaciones EstadΓsticas) at the Pontificia Universidad CatΓ³lica de Chile, training students in statistical inference, experimental design, linear models, and applied data analysis in R.
- πΎ PhD Research & Multi-Omics: PhD Candidate in Plant Biotechnology (PUC), focused on abiotic stress tolerance (heat and drought) in crops through the integration of Ionomics (
$K^+/Na^+$ , micronutrients), Transcriptomics (RNA-seq /DESeq2/WGCNA), and Metabolomics (antioxidant profiling, compatible solutes, proline, MDA). - π¦ Agricultural Microbiology & Biocontrol: Quantitative modeling of plant-beneficial microbe interactions: antagonistic dynamics of Trichoderma spp. (mycoparasitism, volatile organic compounds VOCs, dual-culture inhibition), Plant Growth-Promoting Rhizobacteria (PGPR: Bacillus, Pseudomonas), dual inoculation/consortia, biomineral solubilization (
$P$ ,$K$ , siderophores), and microbe-mediated abiotic stress mitigation. - π² Bayesian Statistics & MCMC Inference: Advanced Bayesian hierarchical modeling with
brmsandStan, calibration of informative and regularizing priors, MCMC convergence diagnostics ($\hat{R}$ , ESS, trace plots), leave-one-out cross-validation (LOO-CV / WAIC), and Posterior Predictive Checks. - πΈοΈ Structural Equation Modeling (Piecewise SEM): Disentangling direct and indirect effects in multilayer physiological networks, integrating hierarchical mixed submodels and directional separation (d-sep) tests.
- π» Scientific Software Development: Creator and author of the R packages
agriDesignR(v0.2.1, assisted experimental design engine, 2D micro-layouts, and hierarchical mixed models) andeasyModels(v0.4.2, automated and reproducible biostatistics), designed to translate cutting-edge methodological workflows to the university classroom and high-impact scientific research.
π± agriDesignR (v0.2.1) β Assisted Experimental Design Engine & Mixed ModelsIntelligent decision engine for agricultural and biological sciences. Connects the complete workflow: Pre-Trial Planning (greenhouse/field gradient diagnostics, replication calculator, 2D bench layouts, and sowing CSV templates), Multi-Engine Mixed Modeling (Kenward-Roger LMM, GLMM, non-parametric tests), Automated Diagnostic Remediation (Box-Cox, varIdent, Gamma/NB GLMM), Relative Blocking Efficiency (Cochran & Cox), Simple Effects in 2- & 3-Way Factorials, Publication-Ready Figures (Nature style), and an Interactive Web App (
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π¦ easyModels (v0.4.2)R package for applied and reproducible biostatistics. Features a unified S3 architecture ( |
π¦ Plant-Microbiome-BiocontrolPlatform for quantitative modeling and experimental analysis of plant-microbe interactions: Trichoderma antagonistic kinetics, plant growth-promoting rhizobacteria (PGPR: Bacillus, Pseudomonas), dual consortia, mineral solubilization ( |
π BioAgro-StatsStructured 8-module repository for advanced biostatistics: from assumption auditing, experimental designs, and non-linear dose-response curves (log-logistic DRC) to Bayesian modeling with |
πΎ Triticum Tale WebScientific web portal and interactive Data Storytelling platform built with Quarto/Plotly to visually communicate multi-omics and physiological stress responses in Triticum durum under heat and drought stress. |
| Repository | Specialty & Focus | Direct Link |
|---|---|---|
| π± agriDesignR | Assisted Experimental Design Engine, Mixed Models & Shiny GUI | View Repository β |
| π¦ easyModels | R package for automated biostatistics and mixed models | View Repository β |
| π¦ Plant-Microbiome-Biocontrol | Trichoderma, PGPR, endophytes, biocontrol & abiotic stress mitigation | View Repository β |
| π BioAgro-Stats | Statistical modules for agronomy, DRC, GAMs & Machine Learning | View Repository β |
| πΎ Triticum-tale-web | Interactive Quarto/Plotly platform for crop stress responses | View Repository β |
| π Experimental-Design-Lab | RCBD, Split-Plot, Latin Squares, Alpha-Lattice & Power Analysis | View Repository β |
| π Statistical-Modeling-Mastery | Progressive modeling roadmap: from LM/GLM to DRC, GAMs & Bayesian | View Repository β |
| πΎ Plant-MultiOmics-Framework | Ionomics, RNA-seq Transcriptomics, Metabolomics & sPLS-DA | View Repository β |
| π² Bayesian-Biostats | Bayesian Modeling with brms/Stan, MCMC & Piecewise SEM |
View Repository β |



