Release v0.1.1 to CRAN

.Rbuildignore

@@ -15,3 +15,4 @@
 ^data-raw$
 ^CITATION\.cff$
 ^_pkgdown\.yml$
+^CRAN-SUBMISSION$

.github/settings.yml

@@ -0,0 +1,22 @@
+repository:
+  # https://probot.github.io/apps/settings/
+  allow_merge_commit: false
+  allow_rebase_merge: true
+  allow_squash_merge: true
+  default_branch: main
+  delete_branch_on_merge: true
+  # has_discussions: false
+  has_issues: true
+  # has_projects: false
+  # has_wiki: false
+  # private: false
+branches:
+  - name: main
+    # https://docs.github.com/en/rest/reference/repos#update-branch-protection
+    protection:
+      required_pull_request_reviews:
+        required_approving_review_count: 0 # (1-6; optionally 0)
+        dismiss_stale_reviews: true
+        require_code_owner_reviews: false
+      required_status_checks:
+        strict: true

.github/workflows/lint-changed-files.yaml

@@ -34,6 +34,7 @@ jobs:
             any::gh
             any::lintr
             any::purrr
+            any::cyclocomp
             epiverse-trace/etdev
           needs: check
 

.lintr

@@ -1,5 +1,6 @@
 linters: all_linters(
     packages = c("lintr", "etdev"),
+    pipe_consistency_linter(pipe = "%>%"),
     object_name_linter = NULL,
     implicit_integer_linter = NULL,
     extraction_operator_linter = NULL,

CRAN-SUBMISSION

@@ -0,0 +1,3 @@
+Version: 0.1.1
+Date: 2024-10-09 11:55:04 UTC
+SHA: cefe3fcfbce1b012b86cae03cf55b3ec90fff852

DESCRIPTION

@@ -1,11 +1,11 @@
 Package: epichains
 Title: Simulating and Analysing Transmission Chain Statistics Using
     Branching Process Models
-Version: 0.1.0.9000
+Version: 0.1.1
 Authors@R: c(
     person("James M.", "Azam", , "[email protected]", role = c("aut", "cre", "cph"),
            comment = c(ORCID = "0000-0001-5782-7330")),
-    person("Sebastian", "Funk", , "[email protected]", role = "aut",
+    person("Sebastian", "Funk", , "[email protected]", role = c("aut", "cph"),
            comment = c(ORCID = "0000-0002-2842-3406")),
     person("Flavio", "Finger", , "[email protected]", role = "aut",
            comment = c(ORCID = "0000-0002-8613-5170")),
@@ -54,4 +54,4 @@ Encoding: UTF-8
 Language: en-GB
 LazyData: true
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2.9000

NEWS.md

@@ -1,4 +1,6 @@
-# epichains (development version)
+# epichains 0.1.1
+
+This is the first CRAN release of `{epichains}`.
 
 ## Package
 
@@ -8,7 +10,7 @@
 
 We are excited to announce the first minor release of `{epichains}`.
 
-`{epichains}` re-implements [{bpmodels}](https://github.com/epiverse-trace/bpmodels), focusing on a unified simulation framework using branching processes to simulate transmission chains data. The framework incorporates susceptible depletion and pre-existing immunity and provides dedicated data structures for handling and analysing transmission chains in both tabular and vector formats. The goal is to provide seamless interoperability with other packages within the Epiverse-TRACE Initiative and the broader epidemiological tool ecosystem.
+`{epichains}` re-implements [{bpmodels}](https://github.com/epiforecasts/bpmodels/), focusing on a unified simulation framework using branching processes to simulate transmission chains data. The framework incorporates susceptible depletion and pre-existing immunity and provides dedicated data structures for handling and analysing transmission chains in both tabular and vector formats. The goal is to provide seamless interoperability with other packages within the Epiverse-TRACE Initiative and the broader epidemiological tool ecosystem.
 
 ## New Features
 

R/borel.R

@@ -4,17 +4,22 @@
 #' @param mu A non-negative number for the poisson mean.
 #' @param log Logical; if TRUE, probabilities p are given as log(p).
 #' @return A numeric vector of the borel probability density.
-#' @author Sebastian Funk
+#' @author Sebastian Funk James M. Azam
 #' @export
 #' @examples
 #' set.seed(32)
 #' dborel(1:5, 1)
 dborel <- function(x, mu, log = FALSE) {
   checkmate::assert_numeric(
-    x, lower = 1, upper = Inf
+    x,
+    lower = 1,
+    upper = Inf
   )
   checkmate::assert_number(
-    mu, lower = 0, finite = TRUE, na.ok = FALSE
+    mu,
+    lower = 0,
+    finite = TRUE,
+    na.ok = FALSE
   )
 
   ld <- -mu * x + (x - 1) * log(mu * x) - lgamma(x + 1)
@@ -34,17 +39,23 @@ dborel <- function(x, mu, log = FALSE) {
 #' `mu >= 1`, the simulation could proceed unendingly. This parameter is used
 #' to prevent this.
 #' @return A numeric vector of random numbers.
-#' @author Sebastian Funk
+#' @author Sebastian Funk James M. Azam
 #' @export
 #' @examples
 #' set.seed(32)
 #' rborel(5, 1)
 rborel <- function(n, mu, censor_at = Inf) {
   checkmate::assert_number(
-    n, lower = 1, finite = TRUE, na.ok = FALSE
+    n,
+    lower = 1,
+    finite = TRUE,
+    na.ok = FALSE
   )
   checkmate::assert_number(
-    mu, lower = 0, finite = TRUE, na.ok = FALSE
+    mu,
+    lower = 0,
+    finite = TRUE,
+    na.ok = FALSE
   )
   # Run simulations
   out <- simulate_chain_stats(
@@ -69,7 +80,7 @@ rborel <- function(n, mu, censor_at = Inf) {
 #'   0 and 1.
 #' @param mu Mean; A positive number.
 #' @return Numeric vector of random numbers
-#' @author Sebastian Funk
+#' @author Sebastian Funk James M. Azam
 #' @export
 #' @examples
 #' set.seed(32)
@@ -81,16 +92,22 @@ rgborel <- function(n, size, prob, mu, censor_at = Inf) {
   # its "correct implementation" for documentation/clarity purposes, as well as
   # for simulations.
   checkmate::assert_number(
-    size, finite = TRUE, lower = 0
+    size,
+    finite = TRUE,
+    lower = 0
   )
   if (!missing(prob)) {
     checkmate::assert_number(
-      prob, lower = 0, upper = 1
+      prob,
+      lower = 0,
+      upper = 1
     )
   }
   if (!missing(mu)) {
     checkmate::assert_number(
-      mu, finite = TRUE, lower = 0
+      mu,
+      finite = TRUE,
+      lower = 0
     )
   }
   if (!missing(prob)) {
@@ -101,6 +118,7 @@ rgborel <- function(n, size, prob, mu, censor_at = Inf) {
   x <- rgamma(n, shape = size, rate = size / mu)
   # then, sample from borel
   return(vapply(
-    x, rborel, n = 1, censor_at = censor_at, FUN.VALUE = numeric(1)
+    x, rborel,
+    n = 1, censor_at = censor_at, FUN.VALUE = numeric(1)
   ))
 }

R/checks.R

@@ -72,9 +72,9 @@
   # check that stat_threshold is an integer or Inf.
   checkmate::assert(
     checkmate::anyInfinite(stat_threshold),
-      checkmate::check_integerish(
-        stat_threshold,
-        lower = 1
+    checkmate::check_integerish(
+      stat_threshold,
+      lower = 1
     ),
     combine = "or"
   )
@@ -102,7 +102,7 @@
   if (!is.null(generation_time)) {
     .check_generation_time_valid(generation_time)
   } else if (tf_specified) {
-      stop("If `tf` is specified, `generation_time` must be specified too.")
+    stop("If `tf` is specified, `generation_time` must be specified too.")
   }
   checkmate::assert_number(
     tf,

R/epichains.R

@@ -16,11 +16,11 @@
 #' @author James M. Azam
 #' @keywords internal
 .new_epichains <- function(sim_df,
-                               n_chains,
-                               statistic,
-                               offspring_dist,
-                               stat_threshold,
-                               track_pop) {
+                           n_chains,
+                           statistic,
+                           offspring_dist,
+                           stat_threshold,
+                           track_pop) {
   # Assemble the elements of the object
   obj <- sim_df
   class(obj) <- c("epichains", class(obj))
@@ -55,11 +55,11 @@
 #' @author James M. Azam
 #' @keywords internal
 .epichains <- function(sim_df,
-                      n_chains,
-                      offspring_dist,
-                      track_pop,
-                      statistic = c("size", "length"),
-                      stat_threshold = Inf) {
+                       n_chains,
+                       offspring_dist,
+                       track_pop,
+                       statistic = c("size", "length"),
+                       stat_threshold = Inf) {
   # Check that inputs are well specified
   checkmate::assert_data_frame(sim_df, min.cols = 3, min.rows = n_chains)
   checkmate::assert_integerish(
@@ -112,10 +112,10 @@
 #' @author James M. Azam
 #' @keywords internal
 .new_epichains_summary <- function(chains_summary,
-                                  n_chains,
-                                  statistic,
-                                  offspring_dist,
-                                  stat_threshold) {
+                                   n_chains,
+                                   statistic,
+                                   offspring_dist,
+                                   stat_threshold) {
   # Assemble the elements of the object
   obj <- chains_summary
   class(obj) <- c("epichains_summary", class(chains_summary))
@@ -141,10 +141,10 @@
 #' @author James M. Azam
 #' @keywords internal
 .epichains_summary <- function(chains_summary,
-                              n_chains,
-                              offspring_dist,
-                              statistic = c("size", "length"),
-                              stat_threshold = Inf) {
+                               n_chains,
+                               offspring_dist,
+                               statistic = c("size", "length"),
+                               stat_threshold = Inf) {
   # chain_summary can sometimes contain infinite values, so check
   # that finite elements are integerish.
   checkmate::check_integerish(
@@ -194,12 +194,12 @@
 #' # population up to chain size 10.
 #' set.seed(32)
 #' chains_pois_offspring <- simulate_chains(
-#'  n_chains = 10,
-#'  statistic = "size",
-#'  offspring_dist = rpois,
-#'  stat_threshold = 10,
-#'  generation_time = function(n) rep(3, n),
-#'  lambda = 2
+#'   n_chains = 10,
+#'   statistic = "size",
+#'   offspring_dist = rpois,
+#'   stat_threshold = 10,
+#'   generation_time = function(n) rep(3, n),
+#'   lambda = 2
 #' )
 #' chains_pois_offspring # Print the object
 print.epichains <- function(x, ...) {
@@ -226,11 +226,11 @@ print.epichains <- function(x, ...) {
 #' # population up to chain size 10.
 #' set.seed(32)
 #' chain_summary_print_eg <- simulate_chain_stats(
-#'  n_chains = 10,
-#'  statistic = "size",
-#'  offspring_dist = rpois,
-#'  stat_threshold = 10,
-#'  lambda = 2
+#'   n_chains = 10,
+#'   statistic = "size",
+#'   offspring_dist = rpois,
+#'   stat_threshold = 10,
+#'   lambda = 2
 #' )
 #' chain_summary_print_eg # Print the object
 print.epichains_summary <- function(x, ...) {
@@ -322,19 +322,19 @@ format.epichains_summary <- function(x, ...) {
         "Max: %s",
         ifelse(
           is.infinite(
-            statistics[["max_stat"]]),
-            paste0(">=", attr(x, "stat_threshold")
+            statistics[["max_stat"]]
           ),
+          paste0(">=", attr(x, "stat_threshold")),
           statistics[["max_stat"]]
         )
       ),
       sprintf(
         "Min: %s",
         ifelse(
           is.infinite(
-            statistics[["min_stat"]]),
-          paste0(">=", attr(x, "stat_threshold")
+            statistics[["min_stat"]]
           ),
+          paste0(">=", attr(x, "stat_threshold")),
           statistics[["min_stat"]]
         )
       )
@@ -455,11 +455,11 @@ summary.epichains <- function(object, ...) {
 #' # population up to chain size 10.
 #' set.seed(32)
 #' chain_stats <- simulate_chain_stats(
-#'  n_chains = 10,
-#'  statistic = "size",
-#'  offspring_dist = rpois,
-#'  stat_threshold = 10,
-#'  lambda = 2
+#'   n_chains = 10,
+#'   statistic = "size",
+#'   offspring_dist = rpois,
+#'   stat_threshold = 10,
+#'   lambda = 2
 #' )
 #' summary(chain_stats)
 summary.epichains_summary <- function(object, ...) {
@@ -526,7 +526,7 @@ summary.epichains_summary <- function(object, ...) {
   stopifnot(
     "object does not contain the correct columns" =
       c("chain", "infector", "infectee", "generation") %in%
-      colnames(x),
+        colnames(x),
     "column `chain` must be a numeric" =
       is.numeric(x$chain),
     "column `infector` must be a numeric" =
@@ -643,11 +643,11 @@ tail.epichains <- function(x, ...) {
 #' cases_per_gen <- aggregate(chains, by = "generation")
 #' head(cases_per_gen)
 aggregate.epichains <- function(x,
-                                     by = c(
-                                       "time",
-                                       "generation"
-                                     ),
-                                     ...) {
+                                by = c(
+                                  "time",
+                                  "generation"
+                                ),
+                                ...) {
   .validate_epichains(x)
   checkmate::assert_string(by)
   # Get grouping variable