R-universe search: needs:inlabru

Showing 6 of total 6 results (show query)

philipmostert

PointedSDMs:Fit Models Derived from Point Processes to Species Distributions using 'inlabru'

Integrated species distribution modeling is a rising field in quantitative ecology thanks to significant rises in the quantity of data available, increases in computational speed and the proven benefits of using such models. Despite this, the general software to help ecologists construct such models in an easy-to-use framework is lacking. We therefore introduce the R package 'PointedSDMs': which provides the tools to help ecologists set up integrated models and perform inference on them. There are also functions within the package to help run spatial cross-validation for model selection, as well as generic plotting and predicting functions. An introduction to these methods is discussed in Issac, Jarzyna, Keil, Dambly, Boersch-Supan, Browning, Freeman, Golding, Guillera-Arroita, Henrys, Jarvis, Lahoz-Monfort, Pagel, Pescott, Schmucki, Simmonds and O’Hara (2020) <doi:10.1016/j.tree.2019.08.006>.

Maintained by Philip Mostert. Last updated 2 days ago.

26 stars 8.59 score 50 scripts 1 dependents

philipmostert

intSDM:Reproducible Integrated Species Distribution Models Across Norway using 'INLA'

Integration of disparate datasets is needed in order to make efficient use of all available data and thereby address the issues currently threatening biodiversity. Data integration is a powerful modeling framework which allows us to combine these datasets together into a single model, yet retain the strengths of each individual dataset. We therefore introduce the package, 'intSDM': an R package designed to help ecologists develop a reproducible workflow of integrated species distribution models, using data both provided from the user as well as data obtained freely online. An introduction to data integration methods is discussed in Issac, Jarzyna, Keil, Dambly, Boersch-Supan, Browning, Freeman, Golding, Guillera-Arroita, Henrys, Jarvis, Lahoz-Monfort, Pagel, Pescott, Schmucki, Simmonds and O’Hara (2020) <doi:10.1016/j.tree.2019.08.006>.

Maintained by Philip Mostert. Last updated 3 months ago.

5 stars 6.26 score 12 scripts

sujit-sahu

bmstdr:Bayesian Modeling of Spatio-Temporal Data with R

Fits, validates and compares a number of Bayesian models for spatial and space time point referenced and areal unit data. Model fitting is done using several packages: 'rstan', 'INLA', 'spBayes', 'spTimer', 'spTDyn', 'CARBayes' and 'CARBayesST'. Model comparison is performed using the DIC and WAIC, and K-fold cross-validation where the user is free to select their own subset of data rows for validation. Sahu (2022) <doi:10.1201/9780429318443> describes the methods in detail.

Maintained by Sujit K. Sahu. Last updated 18 hours ago.

bayesian modelling spatio-temporal-data cpp

16 stars 5.28 score 12 scripts

rdinnager

phyf:Phylogenetic Flow Objects for Easy Manipulation and Modelling of Data on Phylogenetic Trees and Graphs

The {phyf} package implements a tibble and vctrs based object for storing phylogenetic trees along with data. It is fast and flexible and directly produces data structures useful for phylogenetic modelling in the {fibre} package.

Maintained by Russell Dinnage. Last updated 7 months ago.

1 stars 4.20 score 53 scripts 1 dependents

njtierney

yahtsee:Yet Another Hierachical Time Series Extension and Expansion

An opinionated approach to building hierarchical time series models in R using INLA and inlabru.

Maintained by Nicholas Tierney. Last updated 3 years ago.

2 stars 3.00 score 8 scripts

rdinnager

fibre:Fast Evolutionary Trait Modelling on Phylogenies using Branch Regression Models

Implements Phylogenetic Branch Regression models which allow for flexible and versatile models of evolution along a phylogeny. The model can be used to detect shifts in rates of evolution along branches. The model uses a continuous and linear model structure and so can be easily combined with other non-phylogenetic statistical structures, as long as they are implemented using the R package INLA. One major uses of this are to condition on phylogeny in a standard regression between two traits, thus 'accounting' for phylogenetic structure in the response variable, similar to how pgls is used but allowing for a more flexible phylogenetic model. This also allows the phylogenetic model to be combined with the spatial models that INLA excels at (and with comparable flexibility to those spatial models).

Maintained by Russell Dinnage. Last updated 4 months ago.

3 stars 2.71 score 34 scripts