The count of open source software packages hosted by the Comprehensive R Archive
Network (CRAN) using key spatial data handling packages has now passed 1,000 …

Many spatially resolved transcriptomic technologies do not have single-cell resolution but
measure the average gene expression for each spot from a mixture of cells of potentially …

Spatial Data Science introduces fundamental aspects of spatial data that every data scientist
should know before they start working with spatial data. These aspects include how …

Geospatial health data are essential to inform public health and policy. These data can be
used to quantify disease burden, understand geographic and temporal patterns, identify risk …

Background: Preliminary evidence has shown inequities in coronavirus disease 2019
(COVID-19)–related cases and deaths in the United States. Objective: To explore the …

Geocomputation with R is for people who want to analyze, visualize and model geographic
data with open source software. It is based on R, a statistical programming language that …

Panel data econometrics is obviously one of the main fields in the profession, but most of the
models used are difficult to estimate with R. plm is a package for R which intends to make …

Tree diversity has been shown to promote a broad range of ecosystem functions in forests.
However, how important these effects are in driving ecosystem multifunctionality in natural …

Recently, community ecologists are focusing on the relative importance of local
environmental factors and proxies to dispersal limitation to explain spatial variation in …

Aim Spatial autocorrelation is a frequent phenomenon in ecological data and can affect
estimates of model coefficients and inference from statistical models. Here, we test the …