Abstract Machine learning and artificial intelligence (ML/AI), previously considered black box
approaches, are becoming more interpretable, as a result of the recent advances in …

We present a statistical simulator, scDesign3, to generate realistic single-cell and spatial
omics data, including various cell states, experimental designs and feature modalities, by …

As cities warm and the need for climate adaptation strategies increases, a more detailed
understanding of the cooling effects of land cover across a continuum of spatial scales will …

ASReml-R is a statistical package that fits linear mixed models using Residual Maximum
Likelihood (REML) in the R environment. This package uses the same computational kernel …

In the absence of annual laminations, time series generated from lake sediments or other
similar stratigraphic sequences are irregularly spaced in time, which complicates formal …

Spatial process models for analyzing geostatistical data entail computations that become
prohibitive as the number of spatial locations become large. This article develops a class of …

Gaussian Process (GP) models provide a very flexible nonparametric approach to modeling
location‐and‐time indexed datasets. However, the storage and computational requirements …

The first edition of this book has established itself as one of the leading references on
generalized additive models (GAMs), and the only book on the topic to be introductory in …

Structured additive regression models are perhaps the most commonly used class of models
in statistical applications. It includes, among others,(generalized) linear …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …