Minor improvements to vignettes

R/offspring_distributions.R

@@ -1,4 +1,4 @@
-#' Density of the poisson-lognormal compound distribution
+#' Density of the Poisson-lognormal compound distribution
 #'
 #' @details
 #' The function is vectorised so a vector of quantiles can be input and the
@@ -9,7 +9,7 @@
 #' @param sdlog A `number` for the standard deviation of the distribution on
 #'   the log scale.
 #'
-#' @return A `numeric` vector of the density of the poisson-lognormal
+#' @return A `numeric` vector of the density of the Poisson-lognormal
 #'   distribution.
 #' @export
 #'
@@ -34,7 +34,7 @@ dpoislnorm <- Vectorize(function(x, meanlog, sdlog) {
 })
 
 
-#' Cumulative distribution function of the poisson-lognormal compound
+#' Cumulative distribution function of the Poisson-lognormal compound
 #' distribution
 #'
 #' @details
@@ -63,7 +63,7 @@ ppoislnorm <- Vectorize(function(q, meanlog, sdlog) {
   sum(dpoislnorm(x = seq(0, q, by = 1), meanlog = meanlog, sdlog = sdlog))
 })
 
-#' Density of the poisson-Weibull compound distribution
+#' Density of the Poisson-Weibull compound distribution
 #'
 #' @details
 #' The function is vectorised so a vector of quantiles can be input and the
@@ -73,7 +73,7 @@ ppoislnorm <- Vectorize(function(q, meanlog, sdlog) {
 #' @param shape A `number` for the shape parameter of the distribution.
 #' @param scale A `number` for the scale parameter of the distribution.
 #'
-#' @return A `numeric` vector of the density of the poisson-Weibull
+#' @return A `numeric` vector of the density of the Poisson-Weibull
 #'   distribution.
 #' @export
 #'
@@ -100,7 +100,7 @@ dpoisweibull <- Vectorize(function(x, shape, scale) {
   out
 })
 
-#' Cumulative distribution function of the poisson-Weibull compound
+#' Cumulative distribution function of the Poisson-Weibull compound
 #' distribution
 #'
 #' @details

R/proportion_transmission.R

@@ -40,7 +40,7 @@
 #' parameter \eqn{k}, which encodes all variation in infectious histories of
 #' individuals, including properties of the host and pathogen and environmental
 #' circumstances." The value of \eqn{k} corresponds to the shape parameters of
-#' the gamma distribution which encodes the variation in the gamma-poisson
+#' the gamma distribution which encodes the variation in the gamma-Poisson
 #' mixture a.k.a. the negative binomial.
 #'
 #' For `method = t_20`, we define the upper proportion of infectiousness,

README.Rmd

@@ -145,11 +145,11 @@ dynamics do not change).
 
 ## Package vignettes
 
-More details on how to use `{superspreading}` can be found in the [online documentation as package vignettes](https://epiverse-trace.github.io/superspreading/), under "Articles".
+More details on how to use `{superspreading}` can be found in the [online documentation as package vignettes](https://epiverse-trace.github.io/superspreading/), under [Articles](https://epiverse-trace.github.io/superspreading/articles/).
 
 ### Visualisation and plotting functionality
 
-`{superspreading}` does not provide plotting functions, instead we provide example code chunks in the package's vignettes that can be used as a templates upon which data visualisations can be modified. We recommend users copy and edit the examples for their own purposes. (This is permitted under the package's MIT license). By default code chunks for plotting are folded, in order to unfold them and see the code simply click the code button at the top left of the plot.
+`{superspreading}` does not provide plotting functions, instead we provide example code chunks in the package's vignettes that can be used as a templates upon which data visualisations can be modified. We recommend users copy and edit the examples for their own purposes. (This is permitted under the package's MIT license).
 
 ## Help
 
@@ -161,7 +161,7 @@ Contributions to `{superspreading}` are welcomed. Please follow the [package con
 
 ## Code of Conduct
 
-Please note that the {superspreading} project is released with a
+Please note that the `{superspreading}` project is released with a
 [Contributor Code of Conduct](https://github.com/epiverse-trace/.github/blob/main/CODE_OF_CONDUCT.md).
 By contributing to this project, you agree to abide by its terms.
 

README.md

@@ -183,17 +183,15 @@ inevitably cease assuming transmission dynamics do not change).
 More details on how to use `{superspreading}` can be found in the
 [online documentation as package
 vignettes](https://epiverse-trace.github.io/superspreading/), under
-“Articles”.
+[Articles](https://epiverse-trace.github.io/superspreading/articles/).
 
 ### Visualisation and plotting functionality
 
 `{superspreading}` does not provide plotting functions, instead we
 provide example code chunks in the package’s vignettes that can be used
 as a templates upon which data visualisations can be modified. We
 recommend users copy and edit the examples for their own purposes. (This
-is permitted under the package’s MIT license). By default code chunks
-for plotting are folded, in order to unfold them and see the code simply
-click the code button at the top left of the plot.
+is permitted under the package’s MIT license).
 
 ## Help
 
@@ -208,7 +206,7 @@ guide](https://github.com/epiverse-trace/.github/blob/main/CONTRIBUTING.md).
 
 ## Code of Conduct
 
-Please note that the {superspreading} project is released with a
+Please note that the `{superspreading}` project is released with a
 [Contributor Code of
 Conduct](https://github.com/epiverse-trace/.github/blob/main/CODE_OF_CONDUCT.md).
 By contributing to this project, you agree to abide by its terms.

inst/WORDLIST

@@ -38,6 +38,7 @@ Fitdistrplus
 frac
 Getz
 ggplot
+ggtext
 Gostic
 Hanne
 Heleze
@@ -70,6 +71,7 @@ MDN
 MERS
 Meuwissen
 mpox
+nCoV
 N’Faly
 Natsal
 neq

vignettes/design-principles.Rmd

@@ -20,7 +20,7 @@ This document is primarily intended to be read by those interested in understand
 
 ## Scope
 
-The {superspreading} package aims to provide a range of summary metrics to characterise individual-level variation in disease transmission and its impact on the growth or decline of an epidemic. These include calculating the probability an outbreak becomes an epidemic (`probability_epidemic()`), or conversely goes extinct (`probability_extinct()`), the probability an outbreak can be contained (`probability_contain()`), the proportion of cases in cluster of a given size (`proportion_cluster_size()`), and the proportion of cases that cause a proportion of transmission (`proportion_transmission()`).
+The {superspreading} package aims to provide a range of summary metrics to characterise individual-level variation in disease transmission and its impact on the growth or decline of an epidemic. These include calculating the probability an outbreak becomes an epidemic (`probability_epidemic()`), or conversely goes extinct (`probability_extinct()`), the probability an outbreak can be contained (`probability_contain()`), the probability of a pathogen evolving and emerging, the proportion of cases in cluster of a given size (`proportion_cluster_size()`), and the proportion of cases that cause a proportion of transmission (`proportion_transmission()`).
 
 The other aspect of the package is to provide probability density functions and cumulative distribution functions to compute the likelihood for distribution models to estimate heterogeneity in individual-level disease transmission that are not available in R (i.e. base R). At present we include two models: Poisson-lognormal (`dpoislnorm()` & `ppoislnorm()`) and Poisson-Weibull (`dpoisweibull()` & `ppoisweibull()`) distributions.
 
@@ -38,7 +38,7 @@ The distribution functions return a vector of `numeric`s of equal length to the
 
 - The distribution functions are vectorised (i.e. wrapped in `Vectorize()`). This enables them to be used identically to base R distribution functions.
 
-- Native interoperability with `<epiparameter>` objects, from the {epiparameter} package is enabled for `probability_*()` and `proportion_*()` via the `offspring_dist` argument. This allows user to pass in a single object and the parameters required by the {superspreading} function will be extracted, if these are not available within the `<epiparameter>` object the function returns an informative error. The `offspring_dist` argument is after `...` to ensure users specify the argument in full and not accidentally provide data to this argument.
+- Native interoperability with `<epiparameter>` objects, from the {epiparameter} package is enabled for `probability_*()` and `proportion_*()` via the `offspring_dist` argument. This allows user to pass in a single object and the parameters required by the {superspreading} function will be extracted, if these are not available within the `<epiparameter>` object the function returns an informative error. The `offspring_dist` argument is after `...` to ensure users specify the argument in full and not accidentally provide data to this argument. The exception is `probability_emergence()` which does not have an `offspring_dist` argument and thus no interoperability with {epiparameter} due to the function using two reproduction numbers so handling multiple `<epiparameter>` objects was judged to add too much complexity for the benefit.
 
 - Internal functions have a dot (`.`) prefix, exported functions do not.
 
@@ -50,10 +50,11 @@ The aim is to restrict the number of dependencies to a minimal required set for
 
 * {stats}
 * [{checkmate}](https://CRAN.R-project.org/package=checkmate)
+* [{rlang}](https://CRAN.R-project.org/package=rlang)
 
-{stats} is distributed with the R language so is viewed as a lightweight dependency, that should already be installed on a user's machine if they have R. {checkmate} is an input checking package widely used across Epiverse-TRACE packages.
+{stats} is distributed with the R language so is viewed as a lightweight dependency, that should already be installed on a user's machine if they have R. {checkmate} is an input checking package widely used across Epiverse-TRACE packages. {rlang} is used to accept [dynamic dots](https://rlang.r-lib.org/reference/dyn-dots.html) in various {superspreading} functions. 
 
-Suggested dependencies (not including package documentation ({knitr}, {rmarkdown}), testing ({spelling} and {testthat}), and plotting ({ggplot2})) are: [{epiparameter}](https://github.com/epiverse-trace/epiparameter), used to easily access epidemiological parameters from the package's library, and [{fitdistrplus}](https://CRAN.R-project.org/package=fitdistrplus), used for model fitting methods.
+Suggested dependencies are used for package documentation, including building vignettes ([{knitr}]( https://CRAN.R-project.org/package=knitr), [{rmarkdown}](https://CRAN.R-project.org/package=rmarkdown)), data wrangling ([{dplyr}](https://CRAN.R-project.org/package=dplyr), [{purrr}](https://CRAN.R-project.org/package=purrr)), plotting ([{ggplot2}](https://CRAN.R-project.org/package=ggplot2), [{scales}](https://CRAN.R-project.org/package=scales), [{ggtext}](https://CRAN.R-project.org/package=ggtext)), loading epidemiological parameters from [{epiparameter}](https://CRAN.R-project.org/package=epiparameter), and model fitting with [{fitdistrplus}](https://CRAN.R-project.org/package=fitdistrplus). There are also suggested dependencies for testing and spell checking ([{testthat}](https://CRAN.R-project.org/package=testthat), [{spelling}](https://CRAN.R-project.org/package=spelling)).
 
 ## Contribute