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inlabru-org

inlabru:Bayesian Latent Gaussian Modelling using INLA and Extensions

Facilitates spatial and general latent Gaussian modeling using integrated nested Laplace approximation via the INLA package (<https://www.r-inla.org>). Additionally, extends the GAM-like model class to more general nonlinear predictor expressions, and implements a log Gaussian Cox process likelihood for modeling univariate and spatial point processes based on ecological survey data. Model components are specified with general inputs and mapping methods to the latent variables, and the predictors are specified via general R expressions, with separate expressions for each observation likelihood model in multi-likelihood models. A prediction method based on fast Monte Carlo sampling allows posterior prediction of general expressions of the latent variables. Ecology-focused introduction in Bachl, Lindgren, Borchers, and Illian (2019) <doi:10.1111/2041-210X.13168>.

Maintained by Finn Lindgren. Last updated 21 hours ago.

96 stars 12.60 score 832 scripts 6 dependents

pbs-assess

sdmTMB:Spatial and Spatiotemporal SPDE-Based GLMMs with 'TMB'

Implements spatial and spatiotemporal GLMMs (Generalized Linear Mixed Effect Models) using 'TMB', 'fmesher', and the SPDE (Stochastic Partial Differential Equation) Gaussian Markov random field approximation to Gaussian random fields. One common application is for spatially explicit species distribution models (SDMs). See Anderson et al. (2024) <doi:10.1101/2022.03.24.485545>.

Maintained by Sean C. Anderson. Last updated 3 days ago.

ecologyglmmspatial-analysisspecies-distribution-modellingtmbcpp

205 stars 11.04 score 848 scripts 1 dependents

andrewzm

FRK:Fixed Rank Kriging

A tool for spatial/spatio-temporal modelling and prediction with large datasets. The approach models the field, and hence the covariance function, using a set of basis functions. This fixed-rank basis-function representation facilitates the modelling of big data, and the method naturally allows for non-stationary, anisotropic covariance functions. Discretisation of the spatial domain into so-called basic areal units (BAUs) facilitates the use of observations with varying support (i.e., both point-referenced and areal supports, potentially simultaneously), and prediction over arbitrary user-specified regions. `FRK` also supports inference over various manifolds, including the 2D plane and 3D sphere, and it provides helper functions to model, fit, predict, and plot with relative ease. Version 2.0.0 and above also supports the modelling of non-Gaussian data (e.g., Poisson, binomial, negative-binomial, gamma, and inverse-Gaussian) by employing a generalised linear mixed model (GLMM) framework. Zammit-Mangion and Cressie <doi:10.18637/jss.v098.i04> describe `FRK` in a Gaussian setting, and detail its use of basis functions and BAUs, while Sainsbury-Dale, Zammit-Mangion, and Cressie <doi:10.18637/jss.v108.i10> describe `FRK` in a non-Gaussian setting; two vignettes are available that summarise these papers and provide additional examples.

Maintained by Andrew Zammit-Mangion. Last updated 6 months ago.

cpp

71 stars 8.70 score 188 scripts 1 dependents

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

davidbolin

rSPDE:Rational Approximations of Fractional Stochastic Partial Differential Equations

Functions that compute rational approximations of fractional elliptic stochastic partial differential equations. The package also contains functions for common statistical usage of these approximations. The main references for rSPDE are Bolin, Simas and Xiong (2023) <doi:10.1080/10618600.2023.2231051> for the covariance-based method and Bolin and Kirchner (2020) <doi:10.1080/10618600.2019.1665537> for the operator-based rational approximation. These can be generated by the citation function in R.

Maintained by David Bolin. Last updated 11 days ago.

11 stars 7.65 score 188 scripts 3 dependents

eliaskrainski

INLAspacetime:Spatial and Spatio-Temporal Models using 'INLA'

Prepare objects to implement models over spatial and spacetime domains with the 'INLA' package (<https://www.r-inla.org>). These objects contain data to for the 'cgeneric' interface in 'INLA', enabling fast parallel computations. We implemented the spatial barrier model, see Bakka et. al. (2019) <doi:10.1016/j.spasta.2019.01.002>, and some of the spatio-temporal models proposed in Lindgren et. al. (2023) <https://www.idescat.cat/sort/sort481/48.1.1.Lindgren-etal.pdf>. Details are provided in the available vignettes and from the URL bellow.

Maintained by Elias Teixeira Krainski. Last updated 18 days ago.

openblas

4 stars 7.05 score 56 scripts

vast-lib

tinyVAST:Multivariate Spatio-Temporal Models using Structural Equations

Fits a wide variety of multivariate spatio-temporal models with simultaneous and lagged interactions among variables (including vector autoregressive spatio-temporal ('VAST') dynamics) for areal, continuous, or network spatial domains. It includes time-variable, space-variable, and space-time-variable interactions using dynamic structural equation models ('DSEM') as expressive interface, and the 'mgcv' package to specify splines via the formula interface. See Thorson et al. (2024) <doi:10.48550/arXiv.2401.10193> for more details.

Maintained by James T. Thorson. Last updated 11 days ago.

vector-autoregressive-spatio-temporal-modelcpp

14 stars 6.83 score

davidbolin

excursions:Excursion Sets and Contour Credibility Regions for Random Fields

Functions that compute probabilistic excursion sets, contour credibility regions, contour avoiding regions, and simultaneous confidence bands for latent Gaussian random processes and fields. The package also contains functions that calculate these quantities for models estimated with the INLA package. The main references for excursions are Bolin and Lindgren (2015) <doi:10.1111/rssb.12055>, Bolin and Lindgren (2017) <doi:10.1080/10618600.2016.1228537>, and Bolin and Lindgren (2018) <doi:10.18637/jss.v086.i05>. These can be generated by the citation function in R.

Maintained by David Bolin. Last updated 14 days ago.

gslcppopenmp

3 stars 6.64 score 40 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

davidbolin

MetricGraph:Random Fields on Metric Graphs

Facilitates creation and manipulation of metric graphs, such as street or river networks. Further facilitates operations and visualizations of data on metric graphs, and the creation of a large class of random fields and stochastic partial differential equations on such spaces. These random fields can be used for simulation, prediction and inference. In particular, linear mixed effects models including random field components can be fitted to data based on computationally efficient sparse matrix representations. Interfaces to the R packages 'INLA' and 'inlabru' are also provided, which facilitate working with Bayesian statistical models on metric graphs. The main references for the methods are Bolin, Simas and Wallin (2024) <doi:10.3150/23-BEJ1647>, Bolin, Kovacs, Kumar and Simas (2023) <doi:10.1090/mcom/3929> and Bolin, Simas and Wallin (2023) <doi:10.48550/arXiv.2304.03190> and <doi:10.48550/arXiv.2304.10372>.

Maintained by David Bolin. Last updated 4 days ago.

cpp

14 stars 6.19 score 275 scripts

cmjt

stelfi:Hawkes and Log-Gaussian Cox Point Processes Using Template Model Builder

Fit Hawkes and log-Gaussian Cox process models with extensions. Introduced in Hawkes (1971) <doi:10.2307/2334319> a Hawkes process is a self-exciting temporal point process where the occurrence of an event immediately increases the chance of another. We extend this to consider self-inhibiting process and a non-homogeneous background rate. A log-Gaussian Cox process is a Poisson point process where the log-intensity is given by a Gaussian random field. We extend this to a joint likelihood formulation fitting a marked log-Gaussian Cox model. In addition, the package offers functionality to fit self-exciting spatiotemporal point processes. Models are fitted via maximum likelihood using 'TMB' (Template Model Builder). Where included 1) random fields are assumed to be Gaussian and are integrated over using the Laplace approximation and 2) a stochastic partial differential equation model, introduced by Lindgren, Rue, and Lindström. (2011) <doi:10.1111/j.1467-9868.2011.00777.x>, is defined for the field(s).

Maintained by Charlotte M. Jones-Todd. Last updated 6 months ago.

cppopenmp

8 stars 5.90 score 4 scripts

mandymejia

BayesfMRI:Spatial Bayesian Methods for Task Functional MRI Studies

Performs a spatial Bayesian general linear model (GLM) for task functional magnetic resonance imaging (fMRI) data on the cortical surface. Additional models include group analysis and inference to detect thresholded areas of activation. Includes direct support for the 'CIFTI' neuroimaging file format. For more information see A. F. Mejia, Y. R. Yue, D. Bolin, F. Lindgren, M. A. Lindquist (2020) <doi:10.1080/01621459.2019.1611582> and D. Spencer, Y. R. Yue, D. Bolin, S. Ryan, A. F. Mejia (2022) <doi:10.1016/j.neuroimage.2022.118908>.

Maintained by Amanda Mejia. Last updated 24 days ago.

cpp

26 stars 5.77 score 19 scripts

andrea-havron

clustTMB:Spatio-Temporal Finite Mixture Model using 'TMB'

Fits a spatio-temporal finite mixture model using 'TMB'. Covariate, spatial and temporal random effects can be incorporated into the gating formula using multinomial logistic regression, the expert formula using a generalized linear mixed model framework, or both.

Maintained by Andrea M. Havron. Last updated 6 months ago.

cpp

4 stars 5.38 score 9 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 17 hours ago.

bayesianmodellingspatio-temporal-datacpp

16 stars 5.28 score 12 scripts

timcdlucas

disaggregation:Disaggregation Modelling

Fits disaggregation regression models using 'TMB' ('Template Model Builder'). When the response data are aggregated to polygon level but the predictor variables are at a higher resolution, these models can be useful. Regression models with spatial random fields. The package is described in detail in Nandi et al. (2023) <doi:10.18637/jss.v106.i11>.

Maintained by Tim Lucas. Last updated 5 months ago.

cpp

2 stars 4.60 score 9 scripts

inbo

inlatools:Diagnostic Tools for INLA Models

Several functions which can be useful to choose sensible priors and diagnose the fitted model.

Maintained by Thierry Onkelinx. Last updated 6 months ago.

bayesian-statisticsgplv3inlamixed-modelsmodel-checkingmodel-validation

4 stars 4.41 score 43 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

andrewzm

IDE:Integro-Difference Equation Spatio-Temporal Models

The Integro-Difference Equation model is a linear, dynamical model used to model phenomena that evolve in space and in time; see, for example, Cressie and Wikle (2011, ISBN:978-0-471-69274-4) or Dewar et al. (2009) <doi:10.1109/TSP.2008.2005091>. At the heart of the model is the kernel, which dictates how the process evolves from one time point to the next. Both process and parameter reduction are used to facilitate computation, and spatially-varying kernels are allowed. Data used to estimate the parameters are assumed to be readings of the process corrupted by Gaussian measurement error. Parameters are fitted by maximum likelihood, and estimation is carried out using an evolution algorithm.

Maintained by Andrew Zammit-Mangion. Last updated 3 years ago.

1 stars 3.70 score 9 scripts

inbo

multimput:Using Multiple Imputation to Address Missing Data

Accompanying package for the paper: Working with population totals in the presence of missing data comparing imputation methods in terms of bias and precision. Published in 2017 in the Journal of Ornithology volume 158 page 603–615 (<doi:10.1007/s10336-016-1404-9>).

Maintained by Thierry Onkelinx. Last updated 1 months ago.

imputationimputation-model

1 stars 3.62 score 14 scripts 1 dependents

inbo

n2kanalysis:Generic Functions to Analyse Data from the 'Natura 2000' Monitoring

All generic functions and classes for the analysis for the 'Natura 2000' monitoring. The classes contain all required data and definitions to fit the model without the need to access other sources. Potentially they might need access to one or more parent objects. An aggregation object might for example need the result of an imputation object. The actual definition of the analysis, using these generic function and classes, is defined in dedictated analysis R packages for every monitoring scheme. For example 'abvanalysis' and 'watervogelanalysis'.

Maintained by Thierry Onkelinx. Last updated 2 months ago.

analysismonitoringnatura2000

1 stars 3.18 score 7 scripts

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

giabaio

survHEinla:Survival Analysis in Health Economic Evaluation using INLA

A module to complement the backbone structure of the package survHE and expand its functionality to run survival models under a Bayesian approach (based on Integrated Nested Laplace Approximation; the underlying 'INLA' package is available for download at <https://inla.r-inla-download.org/R/stable/>). <doi:10.18637/jss.v095.i14>.

Maintained by Gianluca Baio. Last updated 29 days ago.

bayesian-inferencecost-effectiveness-analysishealth-economic-evaluationintegrated-nested-laplace-approximationsurvival-analysisuncertaintyopenjdk

4 stars 2.78 score

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

cran

BSSoverSpace:Blind Source Separation for Multivariate Spatial Data using Eigen Analysis

Provides functions for blind source separation over multivariate spatial data, and useful statistics for evaluating performance of estimation on mixing matrix. 'BSSoverSpace' is based on an eigen analysis of a positive definite matrix defined in terms of multiple normalized spatial local covariance matrices, and thus can handle moderately high-dimensional random fields. This package is an implementation of the method described in Zhang, Hao and Yao (2022)<arXiv:2201.02023>.

Maintained by Sixing Hao. Last updated 2 years ago.

2.00 score

cran

GeoAdjust:Accounting for Random Displacements of True GPS Coordinates of Data

The purpose is to account for the random displacements (jittering) of true survey household cluster center coordinates in geostatistical analyses of Demographic and Health Surveys program (DHS) data. Adjustment for jittering can be implemented either in the spatial random effect, or in the raster/distance based covariates, or in both. Detailed information about the methods behind the package functionality can be found in two preprints. Umut Altay, John Paige, Andrea Riebler, Geir-Arne Fuglstad (2022) <arXiv:2202.11035v2>. Umut Altay, John Paige, Andrea Riebler, Geir-Arne Fuglstad (2022) <arXiv:2211.07442v1>.

Maintained by Umut Altay. Last updated 2 years ago.

cpp

1.70 score

inbo

ladybird:Analysis of Ladybird Occurrence Data

Analysis of ladybird occurrence data from Belgium, the Netherlands and the UK since 1990.

Maintained by Thierry Onkelinx. Last updated 4 years ago.

1.70 score 3 scripts

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