Ecological forecasting using Dynamic Generalized Additive Models with R 📦's {mvgam} and {brms}
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Updated
Jun 11, 2024 - HTML
Ecological forecasting using Dynamic Generalized Additive Models with R 📦's {mvgam} and {brms}
PGR generalised linear modelling course
R scripts and data sets that can be used to reproduce the results presented in the paper "Accounting for shared covariates in semi-parametric Bayesian additive regression trees".
Bayesian modelling of DNA methylation heterogeneity at single-cell resolution
The project can be split into different sub-projects (easy difficulty: replication of the published meta-analysis for evidence of gender bias in hiring decisions; medium for newer modelling). Requires skills in R and will require some learning on Bayesian modelling.
The project can be split into different sub-projects (easy-medium difficulty: meta-analysis of COVID vaccines; medium-high difficulty: estimating excess mortality due to COVID). Requires skills in R and will require some learning on Bayesian modelling.
Norm Constrained Generalised Linear Model using numpy, numba and scipy.
Data analysis of Air BnB data in Barcelona and London using a subset of data investigated by Gyódi, K., & Nawaro, Ł. Generalised Linear Models have been fitted to examine weekend price differences and how price changes between the two cities.
Data and R code accompanying article 10.3354/meps14261 in Marine Ecology Progress Series.
R package hosted on CRAN: Generalised linear models calculated by splitting data into smaller chunks (Author: Thomas Lumley)
Modeling Mortgage Customer Retention with Binary Logistic Regression
Experiments for Binarsity: a penalization for one-hot encoded features
Some solutions to Wood's Generalised Additive Models book.
Core Functionality for Simulating Quantities of Interest from Generalised Linear Models
my second year poster for the Graduate School Poster Day 2016
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