Cover Image

GLOSSA: a user-friendly R Shiny application for Bayesian machine learning analysis of marine species distribution

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Mestre-Tomás, J., Fuster-Alonso, A., Bellido, J. M., Coll, M.
Format:	Preprint
Published:	2025
Subjects:	Methodology
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

GLOSSA: A user-friendly R Shiny application for Bayesian machine learning analysis of marine species distribution
by: Mestre-Tomás, Jorge, et al.
Published: (2025)

Shiny-MAGEC: A Bayesian R Shiny Application for Meta-analysis of Censored Adverse Events
by: Zhou, Zihan, et al.
Published: (2025)

OrigamiPlot: An R Package and Shiny Web App Enhanced Visualizations for Multivariate Data
by: Lu, Yiwen, et al.
Published: (2024)

OCCUR Shiny application: A user‐friendly guide for curating species occurrence records
by: Cristina Ronquillo, et al.
Published: (2024)

Assessing variable importance in survival analysis using machine learning
by: Wolock, Charles J., et al.
Published: (2023)

A scalable Bayesian double machine learning framework for high dimensional causal estimation, with application to racial disproportionality assessment
by: Luo, Yu, et al.
Published: (2025)

Measurement and comparison of distributional shift with applications to ecology, economics, and image analysis
by: Locey, Kenneth J., et al.
Published: (2024)

A Bayesian shrinkage estimator for transfer learning
by: Abba, Mohamed A., et al.
Published: (2024)

Bayesian kernel machine regression for heteroscedastic health outcome data
by: Smith, Melissa J., et al.
Published: (2025)

Spatiotemporal double machine learning to estimate the impact of Cambodian land concessions on deforestation
by: Arifin, Anika, et al.
Published: (2026)

Beyond algorithm hyperparameters: on preprocessing hyperparameters and associated pitfalls in machine learning applications
by: Sauer, Christina, et al.
Published: (2024)

Bayesian feature selection in joint models with application to a cardiovascular disease cohort study
by: Islam, Mirajul, et al.
Published: (2024)

A framework for leveraging machine learning tools to estimate personalized survival curves
by: Wolock, Charles J., et al.
Published: (2022)

Objective Bayesian inference for the Dhillon distribution
by: Ramos, Pedro Luiz, et al.
Published: (2025)

Optimality-based reward learning with applications to toxicology
by: Weisenthal, Samuel J., et al.
Published: (2024)

Bayesian estimation of a multivariate TAR model when the noise process distribution belongs to the class of Gaussian variance mixtures
by: Vanegas, L. H., et al.
Published: (2025)

Dir-SPGLM: A Bayesian semiparametric GLM with data-driven reference distribution
by: Alam, Entejar, et al.
Published: (2024)

Causal machine learning for high-dimensional mediation analysis using interventional effects mapped to a target trial
by: Chen, Tong, et al.
Published: (2025)

Multilevel network meta-regression for general likelihoods: synthesis of individual and aggregate data with applications to survival analysis
by: Phillippo, David M., et al.
Published: (2024)

Differential contributions of machine learning and statistical analysis to language and cognitive sciences
by: Sun, Kun, et al.
Published: (2024)

Double machine learning to estimate the effects of multiple treatments and their interactions
by: Xiang, Qingyan, et al.
Published: (2025)

A generalized Bayesian approach to multiple changepoint analysis
by: Wang, Yuhui, et al.
Published: (2026)

kDGLM: a R package for Bayesian analysis of Generalized Dynamic Linear Models
by: Santos Jr., Silvaneo V. dos, et al.
Published: (2024)

Bayesian spatial functional data clustering: applications in disease surveillance
by: Zhong, Ruiman, et al.
Published: (2024)

Fast variational Bayesian inference for correlated survival data: an application to invasive mechanical ventilation duration analysis
by: Xian, Chengqian, et al.
Published: (2024)

A machine learning approach based on survival analysis for IBNR frequencies in non-life reserving
by: Hiabu, Munir, et al.
Published: (2023)

Flexible cost-penalized Bayesian model selection: developing inclusion paths with an application to diagnosis of heart disease
by: Porter, Erica M., et al.
Published: (2023)

Fast Bayesian inference in a class of sparse linear mixed effects models
by: Spyropoulou, M-Z., et al.
Published: (2024)

DiPPER: A Bayesian approach to differential prevalence analysis with applications in microbiome studies
by: Pelto, Juho, et al.
Published: (2026)

A Bayesian semi-parametric approach to causal mediation for longitudinal mediators and time-to-event outcomes with application to a cardiovascular disease cohort study
by: Bhandari, Saurabh, et al.
Published: (2024)

On the order statistics from the XLindley distribution and associated inference with an application to fatigue data
by: Akhter, Zuber, et al.
Published: (2025)

Bayesian multilevel compositional data analysis: introduction, evaluation, and application
by: Le, Flora, et al.
Published: (2024)

Dynamic factor analysis for sparse and irregular longitudinal data: an application to metabolite measurements in a COVID-19 study
by: Cai, Jiachen, et al.
Published: (2024)

On the use of cross-fitting in causal machine learning with correlated units
by: Balkus, Salvador V., et al.
Published: (2026)

Hierarchical Bayesian spectral analysis of multiple stationary time series
by: Lee, Rebecca, et al.
Published: (2025)

CMiNet: An R package and user‐friendly Shiny App for constructing consensus microbiome networks
by: Rosa Aghdam, et al.
Published: (2025)

hdbayes: An R Package for Bayesian Analysis of Generalized Linear Models Using Historical Data
by: Alt, Ethan M., et al.
Published: (2025)

Spatial Gaussian fields for complex areas with application to marine megafauna conservation
by: Heyl, Martina Le-Bert, et al.
Published: (2025)

Bayesian changepoint detection via logistic regression and the topological analysis of image series
by: Thomas, Andrew M., et al.
Published: (2024)

Opaque prior distributions in Bayesian latent variable models
by: Merkle, Edgar C., et al.
Published: (2023)