Handle DOI and corner case for VGAM

Author: dieghernan

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)

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("<doi:", "<https://doi.org/", abstract, fixed = TRUE)
+
   abstract
 }
 

tests/testthat/_snaps/encoding.md

@@ -17,23 +17,24 @@
         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) <doi:10.1016/j.csda.2008.02.015>.
+        negative binomial GLR-CUSUM method of Höhle and Paul (2008) <https://doi.org/10.1016/j.csda.2008.02.015>.
         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) <doi:10.18637/jss.v070.i10>. 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)
-        <doi:10.1002/sim.4177> and Meyer and Held (2014) <doi:10.1214/14-AOAS743>. 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) <doi:10.1002/bimj.200900050>. 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)
-        <doi:10.1111/j.1541-0420.2011.01684.x>. A recent overview of the implemented space-time
-        modeling frameworks for epidemic phenomena is given by Meyer et al. (2017) <doi:10.18637/jss.v077.i11>.
+        is given by Salmon et al. (2016) <https://doi.org/10.18637/jss.v070.i10>. 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) <https://doi.org/10.1002/sim.4177> and Meyer and Held (2014) <https://doi.org/10.1214/14-AOAS743>.
+        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) <https://doi.org/10.1002/bimj.200900050>. 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) <https://doi.org/10.1111/j.1541-0420.2011.01684.x>. A recent
+        overview of the implemented space-time modeling frameworks for epidemic phenomena
+        is given by Meyer et al. (2017) <https://doi.org/10.18637/jss.v077.i11>.
       authors:
       - family-names: Höhle
         given-names: Michael

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) <doi:10.1016/j.csda.2008.02.015>.
+        negative binomial GLR-CUSUM method of Höhle and Paul (2008) <https://doi.org/10.1016/j.csda.2008.02.015>.
         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) <doi:10.18637/jss.v070.i10>. 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)
-        <doi:10.1002/sim.4177> and Meyer and Held (2014) <doi:10.1214/14-AOAS743>. 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) <doi:10.1002/bimj.200900050>. 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)
-        <doi:10.1111/j.1541-0420.2011.01684.x>. A recent overview of the implemented space-time
-        modeling frameworks for epidemic phenomena is given by Meyer et al. (2017) <doi:10.18637/jss.v077.i11>.
+        is given by Salmon et al. (2016) <https://doi.org/10.18637/jss.v070.i10>. 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) <https://doi.org/10.1002/sim.4177> and Meyer and Held (2014) <https://doi.org/10.1214/14-AOAS743>.
+        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) <https://doi.org/10.1002/bimj.200900050>. 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) <https://doi.org/10.1111/j.1541-0420.2011.01684.x>. A recent
+        overview of the implemented space-time modeling frameworks for epidemic phenomena
+        is given by Meyer et al. (2017) <https://doi.org/10.18637/jss.v077.i11>.
       repository: https://CRAN.R-project.org/package=surveillance
       url: https://surveillance.R-Forge.R-project.org/
       date-released: '2021-03-30'