Update the .lintr configuration to match with that in packagetemplate

.lintr

@@ -7,6 +7,10 @@ linters: linters_with_tags(
     todo_comment_linter = NULL,
     function_argument_linter = NULL,
     cyclocomp_linter = NULL,
-    # Use minimum R declared in DESCRIPTION or fall back to current R version
+    # Use minimum R declared in DESCRIPTION or fall back to current R version.
+    # Install etdev package from https://github.com/epiverse-trace/etdev
     backport_linter(if (length(x <- etdev::extract_min_r_version())) x else getRversion())
-    )
+  )
+exclusions: list(
+    "tests/testthat.R" = list(unused_import_linter = Inf)
+  )

DESCRIPTION

@@ -1,18 +1,18 @@
 Package: bpmodels
-Title: Analysing Transmission Chain Statistics using Branching Process
+Title: Simulating and Analysing Transmission Chain Statistics using Branching Process
     Models
 Version: 0.3.0
 Authors@R: c(
-    person("Sebastian", "Funk", , "sebastian.funk@lshtm.ac.uk", role = c("aut", "cph"),
-           comment = c(ORCID = "https://orcid.org/0000-0002-2842-3406")),
+    person("James M.", "Azam", , "james.azam@lshtm.ac.uk", role = c("aut", "cre"),
+           comment = c(ORCID = "https://orcid.org/0000-0001-5782-7330")),
     person("Zhian N.", "Kamvar", , "[email protected]", role = "ctb",
            comment = c(ORCID = "https://orcid.org/0000-0003-1458-7108")),
     person("Flavio", "Finger", , "[email protected]", role = "aut",
            comment = c(ORCID = "https://orcid.org/0000-0002-8613-5170")),
-    person("James M.", "Azam", , "james.azam@lshtm.ac.uk", role = c("aut", "cre"),
-           comment = c(ORCID = "https://orcid.org/0000-0001-5782-7330"))
+    person("Sebastian", "Funk", , "sebastian.funk@lshtm.ac.uk", role = c("aut", "cph"),
+           comment = c(ORCID = "https://orcid.org/0000-0002-2842-3406"))
   )
-Description: Provides methods to analyse and simulate the size and length
+Description: Provides methods to simulate and analyse the size and length
     of branching processes with an arbitrary offspring distribution. These
     can be used, for example, to analyse the distribution of chain sizes
     or length of infectious disease outbreaks, as discussed in Farrington

README.Rmd

@@ -13,7 +13,7 @@ knitr::opts_chunk$set(
 )
 ```
 
-# _bpmodels_: Methods for analysing the size and length of transmission chains from branching process models
+# _bpmodels_: Methods for simulating and analysing the size and length of transmission chains from branching process models
 
 <!-- badges: start -->
 ![GitHub R package version](https://img.shields.io/github/r-package/v/epiverse-trace/bpmodels)
@@ -67,7 +67,8 @@ Here is a quick example of estimating the loglikelihood of an observed chain:
 # example of observed chain sizes
 chain_sizes <- c(1, 2, 3, 4) 
 # estimate loglikelihood of the observed chain sizes
-chain_ll_eg <- chain_ll(chain_sizes, "pois", "size", lambda = 0.5)
+chain_ll_eg <- chain_ll(x = chain_sizes, offspring = "pois", 
+                        stat = "size", lambda = 0.5)
 chain_ll_eg
 ```
 
@@ -94,9 +95,9 @@ offspring with mean, $\text{lambda} = 0.5$, and serial interval of $3$:
 ```{r}
 set.seed(1234)
 
-chain_sim_susc_eg <- chain_sim_susc(pop = 1000, "pois",
+chain_sim_susc_eg <- chain_sim_susc(pop = 1000, offspring = "pois",
                                     mn_offspring = 0.5,
-                                    serial = function(x) 3
+                                    serial = function(x) {3}
                                     )
 
 head(chain_sim_susc_eg)

README.md

@@ -1,5 +1,5 @@
 
-# *bpmodels*: Methods for analysing the size and length of transmission chains from branching process models
+# *bpmodels*: Methods for simulating and analysing the size and length of transmission chains from branching process models
 
 <!-- badges: start -->
 
@@ -57,7 +57,8 @@ chain:
 # example of observed chain sizes
 chain_sizes <- c(1, 2, 3, 4) 
 # estimate loglikelihood of the observed chain sizes
-chain_ll_eg <- chain_ll(chain_sizes, "pois", "size", lambda = 0.5)
+chain_ll_eg <- chain_ll(x = chain_sizes, offspring = "pois", 
+                        stat = "size", lambda = 0.5)
 chain_ll_eg
 #> [1] -7.772589
 ```
@@ -94,9 +95,9 @@ offspring with mean, $\text{lambda} = 0.5$, and serial interval of $3$:
 ``` r
 set.seed(1234)
 
-chain_sim_susc_eg <- chain_sim_susc(pop = 1000, "pois",
+chain_sim_susc_eg <- chain_sim_susc(pop = 1000, offspring = "pois",
                                     mn_offspring = 0.5,
-                                    serial = function(x) 3
+                                    serial = function(x) {3}
                                     )
 
 head(chain_sim_susc_eg)