Modeling lake conductivity in the contiguous United States using spatial indexing for big spatial data.

Conductivity is an important indicator of the health of aquatic ecosystems. We model large amounts of lake conductivity data collected as part of the United States Environmental Protection Agency's National Lakes Assessment using spatial indexing, a flexible and efficient approach to fitting spatial statistical models to big data sets. Spatial indexing is capable of accommodating various spatial covariance structures as well as features like random effects, geometric anisotropy, partition factors, and non-Euclidean topologies.

SSN2: The next generation of spatial stream network modeling in R.

The SSN2 package provides tools for spatial statistical modeling, parameter estimation, and prediction on stream (river) networks. SSN2 is the successor to the SSN package (Ver Hoef, Peterson, Clifford, & Shah, 2014), which was archived alongside broader changes in the -spatial ecosystem (Nowosad, 2023) that included 1) the retirement of rgdal (Bivand, Keitt, & Rowlingson, 2021), rgeos (Bivand & Rundel, 2020), and maptools (Bivand & Lewin-Koh, 2021) and 2) the lack of active development of sp (Bivand, Pebesma, & Gómez-Rubio, 2013). SSN2 maintains compatibility with the input data file structures used by the SSN package but leverages modern -spatial tools like sf (Pebesma, 2018).

Indexing and partitioning the spatial linear model for large data sets.

We consider four main goals when fitting spatial linear models: 1) estimating covariance parameters, 2) estimating fixed effects, 3) kriging (making point predictions), and 4) block-kriging (predicting the average value over a region). Each of these goals can present different challenges when analyzing large spatial data sets. Current research uses a variety of methods, including spatial basis functions (reduced rank), covariance tapering, etc, to achieve these goals.

An Application of Spatio-temporal Modeling to Finite Population Abundance Prediction.

Spatio-temporal models can be used to analyze data collected at various spatial locations throughout multiple time points. However, even with a finite number of spatial locations, there may be a lack of resources to collect data from every spatial location at every time point. We develop a spatio-temporal finite-population block kriging (ST-FPBK) method to predict a quantity of interest, such as a mean or total, across a finite number of spatial locations.

sptotal: an R package for predicting totals and weighted sums from spatial data.

In ecological or environmental surveys, it is often desired to predict the mean or total of a variable in some finite region. However, because of time and money constraints, sampling the entire region is often unfeasible. The purpose of the sptotal R package is to provide software that gives a prediction for a quantity of interest, such as a total, and an associated standard error for the prediction.

Iron Sulfate and Phosphorous Acid Affect Turfgrass Surface pH and Microdochium Patch Severity on Annual Bluegrass.

Microdochium patch is a turfgrass disease caused by the fungal pathogen Iron sulfate heptahydrate (FeSO•7HO) and phosphorous acid (HPO) applications have previously been shown to suppress Microdochium patch on annual bluegrass putting greens when applied alone, although either disease suppression was inadequate or turfgrass quality was reduced from the applications. A field experiment was conducted in Corvallis, Oregon, U.S.

spmodel: Spatial statistical modeling and prediction in [Formula: see text].

spmodel is an [Formula: see text] package used to fit, summarize, and predict for a variety spatial statistical models applied to point-referenced or areal (lattice) data. Parameters are estimated using various methods, including likelihood-based optimization and weighted least squares based on variograms. Additional modeling features include anisotropy, non-spatial random effects, partition factors, big data approaches, and more.

A comparison of design-based and model-based approaches for finite population spatial sampling and inference.

The design-based and model-based approaches to frequentist statistical inference rest on fundamentally different foundations. In the design-based approach, inference relies on random sampling. In the model-based approach, inference relies on distributional assumptions.

Marginal inference for hierarchical generalized linear mixed models with patterned covariance matrices using the Laplace approximation.

We develop hierarchical models and methods in a fully parametric approach to generalized linear mixed models for any patterned covariance matrix. The Laplace approximation is used to marginally estimate covariance parameters by integrating over all fixed and latent random effects. The Laplace approximation relies on Newton-Raphson updates, which also leads to predictions for the latent random effects.