Update Related projects section in README

R/probability_contain.R

@@ -18,7 +18,7 @@
 #'
 #' @details
 #' When using `simulate = TRUE`, the default arguments to simulate the
-#' transmission chains with [chain_sim()] are `1e5` replicates,
+#' transmission chains with [.chain_sim()] are `1e5` replicates,
 #' a negative binomial (`nbinom`) offspring distribution, parameterised with
 #' `R` (and `pop_control` if > 0) and `k`.
 #'
@@ -40,7 +40,7 @@
 #' enables setting the `case_threshold`, `outbreak_time` and `generation_time`
 #' arguments.
 #' @param ... <[`dynamic-dots`][rlang::dyn-dots]> Named elements to replace
-#' default arguments in [chain_sim()]. See details.
+#' default arguments in [.chain_sim()]. See details.
 #' @param case_threshold A number for the threshold of the number of cases below
 #' which the epidemic is considered contained. `case_threshold` is only used
 #' when `simulate = TRUE`.

R/utils.R

@@ -335,7 +335,7 @@ solve_for_u <- function(prop, R, k) {
     if (!is.function(generation_time)) {
       stop(sprintf("%s %s",
                    "The `generation_time` argument must be a function",
-                   "(see details in ?chain_sim)."
+                   "(see details in ?.chain_sim)."
       )
       )
     }

README.Rmd

@@ -159,12 +159,10 @@ citation("superspreading")
 
 This project has some overlap with other R packages:
 
-- [`{bpmodels}`](https://github.com/epiverse-trace/bpmodels) is another Epiverse-TRACE
-R package that analyses transmission chain data to infer the transmission process for either
-the size or length of transmission chains. Two main differences between the packages
-are: 1) `{superspreading}` has more functions to compute metrics that characterise
-outbreaks and superspreading events (e.g. `probability_epidemic()` & 
-`probability_extinct()`); 2) `{bpmodels}` can simulate a branching process 
-(`chain_sim()`) with a specified process (e.g. negative binomial).
+- [`{epichains}`](https://github.com/epiverse-trace/epichains) is another Epiverse-TRACE R package that analyses transmission chain data to infer the likelihood for either the size or length of an outbreak cluster, or simulate transmission chains. It is based on the, now retired, [`{bpmodels}`](https://github.com/epiforecasts/bpmodels) package. 
+
+    Two main differences between `{superspreading}` and `{epichains}` are: 1) `{superspreading}` has functions to compute metrics that characterise outbreaks and superspreading events (e.g. `probability_epidemic()`, `probability_extinct()`, `proportion_cluster_size()` & `proportion_transmission()`); whereas `{epichains}` has functions to calculate the likelihood of a transmission chain size and length. 2) `{epichains}` exports functions to simulate a single-type branching process (`simulate_chains()` & `simulate_chain_stats()`).
+
+- [`{modelSSE}`](https://CRAN.R-project.org/package=modelSSE) has a similar scope to `{superspreading}`, it contains functions to infer offspring distribution parameters. It exports several infectious disease outbreak datasets (see `data(package = "modelSSE")`). Both `{superspreading}` and `{modelSSE}` export functions to calculate the proportion of transmission using different methods. It also imports the [`{Delaporte}`](https://CRAN.R-project.org/package=Delaporte) package to model the offspring distribution as a Delaporte distribution.
 
 ## References

README.md

@@ -218,15 +218,32 @@ citation("superspreading")
 
 This project has some overlap with other R packages:
 
-- [`{bpmodels}`](https://github.com/epiverse-trace/bpmodels) is another
-  Epiverse-TRACE R package that analyses transmission chain data to
-  infer the transmission process for either the size or length of
-  transmission chains. Two main differences between the packages are: 1)
-  `{superspreading}` has more functions to compute metrics that
+- [`{epichains}`](https://github.com/epiverse-trace/epichains) is
+  another Epiverse-TRACE R package that analyses transmission chain data
+  to infer the likelihood for either the size or length of an outbreak
+  cluster, or simulate transmission chains. It is based on the, now
+  retired, [`{bpmodels}`](https://github.com/epiforecasts/bpmodels)
+  package.
+
+  Two main differences between `{superspreading}` and `{epichains}`
+  are: 1) `{superspreading}` has functions to compute metrics that
   characterise outbreaks and superspreading events
-  (e.g. `probability_epidemic()` & `probability_extinct()`); 2)
-  `{bpmodels}` can simulate a branching process (`chain_sim()`) with a
-  specified process (e.g. negative binomial).
+  (e.g. `probability_epidemic()`, `probability_extinct()`,
+  `proportion_cluster_size()` & `proportion_transmission()`); whereas
+  `{epichains}` has functions to calculate the likelihood of a
+  transmission chain size and length. 2) `{epichains}` exports functions
+  to simulate a single-type branching process (`simulate_chains()` &
+  `simulate_chain_stats()`).
+
+- [`{modelSSE}`](https://CRAN.R-project.org/package=modelSSE) has a
+  similar scope to `{superspreading}`, it contains functions to infer
+  offspring distribution parameters. It exports several infectious
+  disease outbreak datasets (see `data(package = "modelSSE")`). Both
+  `{superspreading}` and `{modelSSE}` export functions to calculate the
+  proportion of transmission using different methods. It also imports
+  the [`{Delaporte}`](https://CRAN.R-project.org/package=Delaporte)
+  package to model the offspring distribution as a Delaporte
+  distribution.
 
 ## References
 

inst/WORDLIST

@@ -15,6 +15,7 @@ Codecov
 Conall
 CoV
 COVID
+Delaporte
 Delignette
 dplyr
 Driessche