From 40002b603e74e206c943aeaf3ac12a1b133595bc Mon Sep 17 00:00:00 2001 From: Diego H Date: Tue, 20 Feb 2024 22:00:23 +0100 Subject: [PATCH] Handle DOI and corner case for VGAM --- R/cff_parse_citation.R | 2 ++ R/utils-read-description.R | 3 +++ tests/testthat/_snaps/encoding.md | 27 +++++++++++++------------ tests/testthat/_snaps/merge_desc_cit.md | 27 +++++++++++++------------ 4 files changed, 33 insertions(+), 26 deletions(-) diff --git a/R/cff_parse_citation.R b/R/cff_parse_citation.R index c7fccc55..5a4ab9d1 100644 --- a/R/cff_parse_citation.R +++ b/R/cff_parse_citation.R @@ -100,6 +100,8 @@ cff_parse_citation <- function(bib) { # Parse BibTeX fields ---- parsed_fields <- parse_bibtex_fields(parse_cit) + # VGAM: title is a vector + parsed_fields$title <- clean_str(parsed_fields$title) ## Handle collection types ---- parsed_fields <- add_bibtex_coltype(parsed_fields) diff --git a/R/utils-read-description.R b/R/utils-read-description.R index d26b4e51..1d9935f1 100644 --- a/R/utils-read-description.R +++ b/R/utils-read-description.R @@ -8,6 +8,9 @@ parse_desc_abstract <- function(pkg) { abstract <- clean_str(abstract) abstract <- unname(abstract) + # Convert doi to url + abstract <- gsub(". + negative binomial GLR-CUSUM method of Höhle and Paul (2008) . A novel CUSUM approach combining logistic and multinomial logistic modeling is also included. The package contains several real-world data sets, the ability to simulate outbreak data, and to visualize the results of the monitoring in a temporal, spatial or spatio-temporal fashion. A recent overview of the available monitoring procedures - is given by Salmon et al. (2016) . For the retrospective - analysis of epidemic spread, the package provides three endemic-epidemic modeling - frameworks with tools for visualization, likelihood inference, and simulation. hhh4() - estimates models for (multivariate) count time series following Paul and Held (2011) - and Meyer and Held (2014) . twinSIR() - models the susceptible-infectious-recovered (SIR) event history of a fixed population, - e.g, epidemics across farms or networks, as a multivariate point process as proposed - by Höhle (2009) . twinstim() estimates self-exciting - point process models for a spatio-temporal point pattern of infective events, e.g., - time-stamped geo-referenced surveillance data, as proposed by Meyer et al. (2012) - . A recent overview of the implemented space-time - modeling frameworks for epidemic phenomena is given by Meyer et al. (2017) . + is given by Salmon et al. (2016) . For the + retrospective analysis of epidemic spread, the package provides three endemic-epidemic + modeling frameworks with tools for visualization, likelihood inference, and simulation. + hhh4() estimates models for (multivariate) count time series following Paul and + Held (2011) and Meyer and Held (2014) . + twinSIR() models the susceptible-infectious-recovered (SIR) event history of a fixed + population, e.g, epidemics across farms or networks, as a multivariate point process + as proposed by Höhle (2009) . twinstim() + estimates self-exciting point process models for a spatio-temporal point pattern + of infective events, e.g., time-stamped geo-referenced surveillance data, as proposed + by Meyer et al. (2012) . A recent + overview of the implemented space-time modeling frameworks for epidemic phenomena + is given by Meyer et al. (2017) . authors: - family-names: Höhle given-names: Michael diff --git a/tests/testthat/_snaps/merge_desc_cit.md b/tests/testthat/_snaps/merge_desc_cit.md index 4476fa32..28dc3cec 100644 --- a/tests/testthat/_snaps/merge_desc_cit.md +++ b/tests/testthat/_snaps/merge_desc_cit.md @@ -806,23 +806,24 @@ diseases, but applications could just as well originate from environmetrics, reliability engineering, econometrics, or social sciences. The package implements many typical outbreak detection procedures such as the (improved) Farrington algorithm, or the - negative binomial GLR-CUSUM method of Höhle and Paul (2008) . + negative binomial GLR-CUSUM method of Höhle and Paul (2008) . A novel CUSUM approach combining logistic and multinomial logistic modeling is also included. The package contains several real-world data sets, the ability to simulate outbreak data, and to visualize the results of the monitoring in a temporal, spatial or spatio-temporal fashion. A recent overview of the available monitoring procedures - is given by Salmon et al. (2016) . For the retrospective - analysis of epidemic spread, the package provides three endemic-epidemic modeling - frameworks with tools for visualization, likelihood inference, and simulation. hhh4() - estimates models for (multivariate) count time series following Paul and Held (2011) - and Meyer and Held (2014) . twinSIR() - models the susceptible-infectious-recovered (SIR) event history of a fixed population, - e.g, epidemics across farms or networks, as a multivariate point process as proposed - by Höhle (2009) . twinstim() estimates self-exciting - point process models for a spatio-temporal point pattern of infective events, e.g., - time-stamped geo-referenced surveillance data, as proposed by Meyer et al. (2012) - . A recent overview of the implemented space-time - modeling frameworks for epidemic phenomena is given by Meyer et al. (2017) . + is given by Salmon et al. (2016) . For the + retrospective analysis of epidemic spread, the package provides three endemic-epidemic + modeling frameworks with tools for visualization, likelihood inference, and simulation. + hhh4() estimates models for (multivariate) count time series following Paul and + Held (2011) and Meyer and Held (2014) . + twinSIR() models the susceptible-infectious-recovered (SIR) event history of a fixed + population, e.g, epidemics across farms or networks, as a multivariate point process + as proposed by Höhle (2009) . twinstim() + estimates self-exciting point process models for a spatio-temporal point pattern + of infective events, e.g., time-stamped geo-referenced surveillance data, as proposed + by Meyer et al. (2012) . A recent + overview of the implemented space-time modeling frameworks for epidemic phenomena + is given by Meyer et al. (2017) . repository: https://CRAN.R-project.org/package=surveillance url: https://surveillance.R-Forge.R-project.org/ date-released: '2021-03-30'