cff_read.md
In cffr: Generate Citation File Format ('cff') Metadata for R Packages

Test errors on cff_read

Code
  cff_read(c("abcde", "b"))
Condition
  Error in `cff_read()`:
  ! Use a single value, `path` has length 2

Code
  cff_read("abcde")
Condition
  Error in `file_exist_abort()`:
  ! 'abcde' doesn't exist. Check the '.' directory

cff_read citation.cff

Code
  cff_read_cff_citation("a")
Condition
  Error in `file_exist_abort()`:
  ! 'a' doesn't exist. Check the '.' directory

cff_read DESCRIPTION

Code
  cff_read_description("a")
Condition
  Error in `file_exist_abort()`:
  ! 'a' doesn't exist. Check the '.' directory

cff_read bib

Code
  cff_read_bib("a")
Condition
  Error in `file_exist_abort()`:
  ! 'a' doesn't exist. Check the '.' directory

Code
  d
Output
  type: article
  title: 'cffr: Generate Citation File Format Metadata for R Packages'
  authors:
  - family-names: Hernangómez
    given-names: Diego
  year: '2021'
  journal: Journal of Open Source Software
  publisher:
    name: The Open Journal
  volume: '6'
  issue: '67'
  doi: 10.21105/joss.03900
  url: https://doi.org/10.21105/joss.03900
  copyright: All rights reserved
  notes: 'Publisher: The Open Journal'
  start: '3900'

cff_read citation messages

Code
  cff_read_citation("a")
Condition
  Error in `file_exist_abort()`:
  ! 'a' doesn't exist. Check the '.' directory

Code
  s <- cff_read(f, meta = "aa")
Message
  ! `meta` should be "NULL" or a <packageDescription> object not a string. Using `meta = NULL`

Creating cff from packages encoded in latin1

Code
  cffobj
Output
  cff-version: 1.2.0
  message: 'To cite package "surveillance" in publications use:'
  type: software
  license: GPL-2.0-only
  title: 'surveillance: Temporal and Spatio-Temporal Modeling and Monitoring of Epidemic
    Phenomena'
  version: 1.19.1
  doi: 10.32614/CRAN.package.surveillance
  abstract: Statistical methods for the modeling and monitoring of time series of counts,
    proportions and categorical data, as well as for the modeling of continuous-time
    point processes of epidemic phenomena. The monitoring methods focus on aberration
    detection in count data time series from public health surveillance of communicable
    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) <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) <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
    email: hoehle@math.su.se
    orcid: https://orcid.org/0000-0002-0423-6702
  - family-names: Meyer
    given-names: Sebastian
    email: seb.meyer@fau.de
    orcid: https://orcid.org/0000-0002-1791-9449
  - family-names: Paul
    given-names: Michaela
  repository: https://CRAN.R-project.org/package=surveillance
  url: https://surveillance.R-Forge.R-project.org/
  date-released: '2021-03-30'
  contact:
  - family-names: Meyer
    given-names: Sebastian
    email: seb.meyer@fau.de
    orcid: https://orcid.org/0000-0002-1791-9449
  references:
  - type: article
    title: 'Monitoring Count Time Series in R: Aberration Detection in Public Health
      Surveillance'
    authors:
    - family-names: Salmon
      given-names: Maëlle
    - family-names: Schumacher
      given-names: Dirk
    - family-names: Höhle
      given-names: Michael
    journal: Journal of Statistical Software
    year: '2016'
    volume: '70'
    issue: '10'
    doi: 10.18637/jss.v070.i10
    start: '1'
    end: '35'
  - type: article
    title: Spatio-Temporal Analysis of Epidemic Phenomena Using the R Package surveillance
    authors:
    - family-names: Meyer
      given-names: Sebastian
    - family-names: Held
      given-names: Leonhard
    - family-names: Höhle
      given-names: Michael
    journal: Journal of Statistical Software
    year: '2017'
    volume: '77'
    issue: '11'
    doi: 10.18637/jss.v077.i11
    start: '1'
    end: '55'