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anovatype_pspline_2012

In this respository the code of the paper "A P-spline ANOVA type model in space-time disease mapping (2012). Stochastic Environmental Research and Risk Assessment, 26(6), 835-845" is provided

bigdm

R package for scalable Bayesian disease mapping models for high-dimensional data

biometrical_journal_2022

Using mortality to predict incidence for rare and lethal cancers in very small areas. Biometrical Journal 2022

bookchapter_stmmodels

R code to fit the models and reproduce the results described in Adin et al. (2023)

comparing-r-inla-and-nimble

This repository contains the R code to fit the models described in Urdangarin et al. (2022)

confounding_article

R code to fit spatio-temporal models described in Adin et al. (2023)

covid-19

Mapas descriptivos y predicciones de COVID-19 por Comunidades Autónomas en España

curso_rsat_aet

curso_rsat_upna

Curso sobre rsat presentado en la Universidad Pública de Navarra

dbsc_article

Supplementary material and R code described in Santafé et al. (2021)

dbsc_rr_article

R code to fit the models and reproduce the results described in Adin et al. (2022)

discontinuities_and_clusters_article

Motivating applications and R code described in Adin et al. (2019, SMMR)

dowry_jrssa_article

Supplementary material and code of the paper by Vicente al. al. (2020, JRSSA)

estimating_locp_cancer_mortality_rates

R code to fit age- and gender-specific shared component models described in Retegui et al. (2021)

flexible_psplines_article

R-INLA code to fit the Bayesian P-splines models described in Goicoa et al. (2019)

forecastinganova_psplines_2015

On predicting mortality counts using ANOVA-type P- spline models

identifiability_constraints_article

R-INLA code to fit spatio-temporal CAR models described in Goicoa et al. (2018)

inla_groupcv

R code to fit the models and reproduce the results described in Adin et al. (2023)

itoiz_article

R Code

lacpd

Locally adaptive change-point detection (LACPD)

lacpd_article

lxg

R code to reproduce the computational analysis of the paper: "Logistic regression versus XGBoost for detecting burned areas using satellite images"

mmodels_serra_article

R code to fit spatio-temporal M-Models described in Vicente et al. (2020, SERRA)

mtf-industrial-process

Data and R code to reproduce the results described in "An Adaptive Learning Approach to Multivariate Time Forecasting in Industrial Processes" (Miguelez et al., 2024)

multivariate_confounding

multivariate_spatio_temporal_p_spline

pluvnav

project_cancer_incidence

A new approach to project cancer incidence in regions without population-based cancer registry using mortality

remc_confounding_article

R code to fit the models and reproduce the results described in Urdangarin et al. (2023)

rgistools

Tools for Downloading, Customizing, and Processing Time Series of Satellite Images from Landsat, MODIS, and Sentinel