The JAGS Module
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Updated
Oct 10, 2024 - R
The JAGS Module
This code is in reference to the paper: Kundu, D. and Das, K., 2024. "A quantile-regression approach to bivariate longitudinal joint modeling." Journal of Statistical Research 58(1) , pp : 111-130 , doi: 10.3329/jsr.v58i1.75417
{mvgam} R 📦 to fit Dynamic Bayesian Generalized Additive Models for time series analysis and forecasting
The aim of the project is to analyze the CO2 emissions dataset using Bayesian learning and Monte Carlo Simulations tools in R using JAGS. After a preliminary analysis we answer to some statistical questions, implementing different models. Time-series and clustering analysis are also considered
Bayesian analysis + tidy data + geoms (R package)
A repo for "Precipitation Scaling With Temperature in the Northeast US: Variations by Weather Regime, Season, and Precipitation Intensity"
A repo for "Extreme Precipitation-Temperature Scaling in California: The Role of Atmospheric Rivers"
Reproducible Bayesian data analysis pipelines with targets and JAGS
An R Package for Hierarchical Bayesian Analysis of North American Breeding Bird Survey Data
A repository to store the docker container for the practicals at VIBASS 7
Spatial analysis and modelling from traffic accidents in New Zealand.
A Bayesian hierarchical model that quantifies long-term annual land surface phenology from sparse time series of vegetation indices.
Joint Analysis and Imputation of generalized linear models and linear mixed models with missing values
MCMC inference on Switching Linear Dynamical Systems, using Stan and JAGS
Repository for example Hierarchical Drift Diffusion Model (HDDM) code using JAGS in Python. These scripts provide useful examples for using JAGS with pyjags, the JAGS Wiener module, mixture modeling in JAGS, and Bayesian diagnostics in Python.
Repository for example Hierarchical Drift Diffusion Model (HDDM) code using JAGS in R. These scripts provide useful examples for using JAGS with R2jags, the JAGS Wiener module, mixture modeling in JAGS, and Bayesian diagnostics in R.
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