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# CITATION file created with {cffr} R package
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cff-version: 1.2.0
message: 'To cite package "SpTe2M" in publications use:'
type: software
license: GPL-3.0-or-later
title: 'SpTe2M: Nonparametric Modeling and Monitoring of Spatio-Temporal Data'
version: 1.0.3
doi: 10.32614/CRAN.package.SpTe2M
abstract: Spatio-temporal data have become increasingly popular in many research fields.
  Such data often have complex structures that are difficult to describe and estimate.
  This package provides reliable tools for modeling complicated spatio-temporal data.
  It also includes tools of online process monitoring to detect possible change-points
  in a spatio-temporal process over time. More specifically, the package implements
  the spatio-temporal mean estimation procedure described in Yang and Qiu (2018) <https://doi.org/10.1002/sim.7622>,
  the spatio-temporal covariance estimation procedure discussed in Yang and Qiu (2019)
  <https://doi.org/10.1002/sim.8315>, the three-step method for the joint estimation
  of spatio-temporal mean and covariance functions suggested by Yang and Qiu (2022)
  <https://doi.org/10.1007/s10463-021-00787-2>, the spatio-temporal disease surveillance
  method discussed in Qiu and Yang (2021) <https://doi.org/10.1002/sim.9150> that
  can accommodate the covariate effect, the spatial-LASSO-based process monitoring
  method proposed by Qiu and Yang (2023) <https://doi.org/10.1080/00224065.2022.2081104>,
  and the online spatio-temporal disease surveillance method described in Yang and
  Qiu (2020) <https://doi.org/10.1080/24725854.2019.1696496>.
authors:
- family-names: Yang
  given-names: Kai
  email: kayang@mcw.edu
repository: https://yklmy1994121.r-universe.dev
commit: aa2c6666d8cbd78588988fdfd73134682eac28e2
date-released: '2023-09-29'
contact:
- family-names: Yang
  given-names: Kai
  email: kayang@mcw.edu