From 1ca4e6c247ffad6e7b22bd23815bed261593e830 Mon Sep 17 00:00:00 2001 From: Sean Anderson Date: Wed, 28 Feb 2024 13:30:05 -0800 Subject: [PATCH] Update preprint citation [skip ci] --- README.Rmd | 2 +- README.md | 10 +++++----- header.md | 2 +- inst/CITATION | 5 +++-- 4 files changed, 10 insertions(+), 9 deletions(-) diff --git a/README.Rmd b/README.Rmd index 6f17a5958..16c7e7007 100644 --- a/README.Rmd +++ b/README.Rmd @@ -77,7 +77,7 @@ citation("sdmTMB") ``` Anderson, S.C., E.J. Ward, P.A. English, L.A.K. Barnett., J.T. Thorson. -2022. sdmTMB: an R package for fast, flexible, and +2024. sdmTMB: an R package for fast, flexible, and user-friendly generalized linear mixed effects models with spatial and spatiotemporal random fields. bioRxiv 2022.03.24.485545; doi: diff --git a/README.md b/README.md index dcd29a176..be2b68a61 100644 --- a/README.md +++ b/README.md @@ -14,7 +14,7 @@ sdmTMB is an R package that fits spatial and spatiotemporal GLMMs (Generalized Linear Mixed Effects Models) using Template Model Builder ([TMB](https://github.com/kaskr/adcomp)), [R-INLA](https://www.r-inla.org/), and Gaussian Markov random fields. One common application is for species distribution models (SDMs). See the [documentation site](https://pbs-assess.github.io/sdmTMB/) and a preprint: -Anderson, S.C., E.J. Ward, P.A. English, L.A.K. Barnett, J.T. Thorson. 2022. sdmTMB: an R package for fast, flexible, and user-friendly generalized linear mixed effects models with spatial and spatiotemporal random fields. bioRxiv 2022.03.24.485545; doi: https://doi.org/10.1101/2022.03.24.485545 +Anderson, S.C., E.J. Ward, P.A. English, L.A.K. Barnett, J.T. Thorson. 2024. sdmTMB: an R package for fast, flexible, and user-friendly generalized linear mixed effects models with spatial and spatiotemporal random fields. bioRxiv 2022.03.24.485545; doi: https://doi.org/10.1101/2022.03.24.485545 ## Table of contents @@ -135,10 +135,10 @@ To cite sdmTMB in publications use: citation("sdmTMB") ``` -Anderson, S.C., E.J. Ward, P.A. English, L.A.K. Barnett. 2022. sdmTMB: -an R package for fast, flexible, and user-friendly generalized linear -mixed effects models with spatial and spatiotemporal random fields. -bioRxiv 2022.03.24.485545; doi: +Anderson, S.C., E.J. Ward, P.A. English, L.A.K. Barnett., J.T. Thorson. +2024. sdmTMB: an R package for fast, flexible, and user-friendly +generalized linear mixed effects models with spatial and spatiotemporal +random fields. bioRxiv 2022.03.24.485545; doi: A list of (known) publications that use sdmTMB can be found diff --git a/header.md b/header.md index 664465e04..e7b365325 100644 --- a/header.md +++ b/header.md @@ -13,6 +13,6 @@ sdmTMB is an R package that fits spatial and spatiotemporal GLMMs (Generalized Linear Mixed Effects Models) using Template Model Builder ([TMB](https://github.com/kaskr/adcomp)), [R-INLA](https://www.r-inla.org/), and Gaussian Markov random fields. One common application is for species distribution models (SDMs). See the [documentation site](https://pbs-assess.github.io/sdmTMB/) and a preprint: -Anderson, S.C., E.J. Ward, P.A. English, L.A.K. Barnett. 2022. sdmTMB: an R package for fast, flexible, and user-friendly generalized linear mixed effects models with spatial and spatiotemporal random fields. bioRxiv 2022.03.24.485545; doi: https://doi.org/10.1101/2022.03.24.485545 +Anderson, S.C., E.J. Ward, P.A. English, L.A.K. Barnett, J.T. Thorson. 2024. sdmTMB: an R package for fast, flexible, and user-friendly generalized linear mixed effects models with spatial and spatiotemporal random fields. bioRxiv 2022.03.24.485545; doi: https://doi.org/10.1101/2022.03.24.485545 ## Table of contents diff --git a/inst/CITATION b/inst/CITATION index 1a78858ea..bac5a7eab 100644 --- a/inst/CITATION +++ b/inst/CITATION @@ -7,9 +7,10 @@ bibentry( person(c("Sean", "C."), "Anderson"), person(c("Eric", "J."), "Ward"), person(c("Philina", "A."), "English"), - person(c("Lewis", "A.", "K."), "Barnett") + person(c("Lewis", "A.", "K."), "Barnett"), + person(c("James", "T."), "Thorson") ), - year = "2022", + year = "2024", journal = "bioRxiv", volume = "2022.03.24.485545", doi = "10.1101/2022.03.24.485545"