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Author

Matt Higham

Observed name: Higham, Matt
ORCID: 0009-0006-4217-625X
Observed ORCID: 0009-0006-4217-625X
OpenAlex ID: A5040064230
spmodel: Spatial statistical modeling and prediction in R
2023 · Michael Dumelle, Matt Higham, et al. · PLoS ONE · 34
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A comparison of design‐based and model‐based approaches for finite population spatial sampling and inference
2022 · Michael Dumelle, Matt Higham, et al. · Methods in Ecology and Evolution · 30
Species density models from opportunistic citizen science data
2021 · Jay M. Ver Hoef, Devin Johnson, et al. · Methods in Ecology and Evolution · 19
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Patterns of Coarse Woody Debris in Hardwood Forests across a Chronosequence of Ash Mortality Due to the Emerald Ash Borer (Agrilus planipennis)
2017 · Matt Higham, Brian M. Hoven, et al. · Natural Areas Journal · 12
Adjusting a finite population block kriging estimator for imperfect detection
2020 · Matt Higham, Jay Ver Hoef, et al. · Environmetrics · 9
Indexing and partitioning the spatial linear model for large data sets
2023 · Jay M. Ver Hoef, Michael Dumelle, et al. · PLoS ONE · 6
sptotal: Predicting Totals and Weighted Sums from Spatial Data
2020 · Matt Higham, Jay Ver Hoef, et al. · 4
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sptotal: an R package for predicting totals and weighted sums from spatial data
2023 · Matt Higham, Jay Ver Hoef, et al. · The Journal of Open Source Software · 3
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Modeling lake conductivity in the contiguous United States using spatial indexing for big spatial data
2023 · Michael Dumelle, Jay M. Ver Hoef, et al. · Spatial Statistics · 2
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An Application of Spatio-Temporal Modeling to Finite Population Abundance Prediction
2023 · Matt Higham, Michael Dumelle, et al. · Journal of Agricultural Biological and Environmental Statistics
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Indexing and Partitioning the Spatial Linear Model for Large Data Sets
2023 · Hoef, Jay M. Ver, Dumelle, Michael, et al. · arXiv (Cornell University)
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spmodel: Spatial Statistical Modeling and Prediction
2022 · Michael Dumelle, Matt Higham, et al.
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Author response for "A comparison of design-based and model-based approaches for finite population spatial sampling and inference"
2022 · Dumelle, Michael, Higham, Matt, et al.
Author response for "A comparison of design-based and model-based approaches for finite population spatial sampling and inference"
2022 · Dumelle, Michael, Higham, Matt, et al.
Author response for "Species density models from opportunistic citizen science data"
2021 · Jay M. Ver Hoef, Devin Johnson, et al.
Predicting Totals and Weighted Sums from Spatial Data [R package sptotal version 0.1.0]
2020 · Matt Higham, Ver Hoef Jay, et al.
Works count: 19
Citation count: 120
H-index: 6
I10-index: 4
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