Homogenizing argument names

R/ModelSEIR.R

@@ -2,11 +2,11 @@
 #'
 #' @param name String. Name of the virus.
 #' @param prevalence Double. Initial proportion of individuals with the virus.
-#' @param infectiousness Numeric scalar between 0 and 1. Virus's rate of 
+#' @param transmission_rate Numeric scalar between 0 and 1. Virus's rate of 
 #' infection.
 #' @param incubation_days Numeric scalar greater than 0. Average number of 
 #' incubation days.
-#' @param recovery Numeric scalar between 0 and 1. Rate of recovery from virus. 
+#' @param recovery_rate Numeric scalar between 0 and 1. Rate of recovery_rate from virus. 
 #' @param x Object of class SEIR. 
 #' @param ... Currently ignore.
 #' @export
@@ -16,7 +16,7 @@
 #' - The `ModelSEIR`function returns a model of class [epiworld_model].
 #' @examples 
 #' model_seir <- ModelSEIR(name = "COVID-19", prevalence = 0.01, 
-#' infectiousness = 0.9, recovery = 0.1, incubation_days = 4)
+#' transmission_rate = 0.9, recovery_rate = 0.1, incubation_days = 4)
 #' 
 #' # Adding a small world population
 #' agents_smallworld(
@@ -34,11 +34,11 @@
 #' plot(model_seir, main = "SEIR Model")
 #' @seealso epiworld-methods
 ModelSEIR <- function(
-    name, prevalence, infectiousness, incubation_days, recovery
+    name, prevalence, transmission_rate, incubation_days, recovery_rate
 ) {
 
   structure(
-    ModelSEIR_cpp(name, prevalence, infectiousness, incubation_days, recovery),
+    ModelSEIR_cpp(name, prevalence, transmission_rate, incubation_days, recovery_rate),
     class = c("epiworld_seir", "epiworld_model")
   )
 

R/ModelSEIRCONN.R

@@ -7,11 +7,11 @@
 #' @param n Integer greater than zero. Population size.
 #' @param prevalence Initial proportion of individuals with the virus.
 #' @param contact_rate Numeric scalar. Average number of contacts per step.
-#' @param prob_transmission Numeric scalar between 0 and 1. Probability of 
+#' @param transmission_rate Numeric scalar between 0 and 1. Probability of 
 #' transmission.
 #' @param incubation_days Numeric scalar greater than 0. Average number of 
 #' incubation days.
-#' @param prob_recovery Numeric scalar between 0 and 1. Probability of recovery.
+#' @param recovery_rate Numeric scalar between 0 and 1. Probability of recovery_rate.
 #' @param x Object of class SEIRCONN. 
 #' @param ... Currently ignore. 
 #' @param n Number of individuals in the population.
@@ -28,8 +28,8 @@
 #'   n                   = 10000,
 #'   contact_rate        = 2, 
 #'   incubation_days     = 7, 
-#'   prob_transmission   = 0.5,
-#'   prob_recovery       = 0.3
+#'   transmission_rate   = 0.5,
+#'   recovery_rate       = 0.3
 #' )
 #'   
 #' # Running and printing
@@ -49,13 +49,13 @@
 #' plot(model_seirconn)
 #' @seealso epiworld-methods
 ModelSEIRCONN <- function(
-    name, n, prevalence, contact_rate, prob_transmission, 
-    incubation_days, prob_recovery
+    name, n, prevalence, contact_rate, transmission_rate, 
+    incubation_days, recovery_rate
 ) {
 
   structure(
     ModelSEIRCONN_cpp(name, n, prevalence, contact_rate, 
-                      prob_transmission, incubation_days, prob_recovery),
+                      transmission_rate, incubation_days, recovery_rate),
     class = c("epiworld_seirconn", "epiworld_model")
   )
 

R/ModelSIR.R

@@ -6,19 +6,19 @@
 #' @param name String. Name of the virus
 
 #' @param prevalence Double. Initial proportion of individuals with the virus.
-#' @param infectiousness Numeric scalar between 0 and 1. Virus's rate of 
+#' @param transmission_rate Numeric scalar between 0 and 1. Virus's rate of 
 #' infection.
-#' @param recovery Numeric scalar between 0 and 1. Rate of recovery from virus. 
+#' @param recovery_rate Numeric scalar between 0 and 1. Rate of recovery_rate from virus. 
 #' @param x Object of class SIR. 
-#' @param ... Currently ignore. 
+#' @param ... Additional arguments passed to [graphics::plot].
 #' @export
 #' @family Models
 #' @aliases epiworld_sir
 #' @returns
 #' - The `ModelSIR` function returns a model of class [epiworld_model].
 #' @examples 
 #' model_sir <- ModelSIR(name = "COVID-19", prevalence = 0.01, 
-#'                       infectiousness = 0.9, recovery = 0.1)
+#'                       transmission_rate = 0.9, recovery_rate = 0.1)
 #' 
 #' # Adding a small world population
 #' agents_smallworld(
@@ -37,11 +37,11 @@
 #' plot(model_sir)
 #' @seealso epiworld-methods
 ModelSIR <- function(
-    name, prevalence, infectiousness, recovery
+    name, prevalence, transmission_rate, recovery_rate
 ) {
 
   structure(
-    ModelSIR_cpp(name, prevalence, infectiousness, recovery),
+    ModelSIR_cpp(name, prevalence, transmission_rate, recovery_rate),
     class = c("epiworld_sir", "epiworld_model")
   )
 

R/ModelSIRCONN.R

@@ -2,9 +2,9 @@
 #' @param name String. Name of the virus
 #' @param prevalence Double. Initial proportion of individuals with the virus.
 #' @param contact_rate Numeric scalar. Average number of contacts per step.
-#' @param prob_transmission Numeric scalar between 0 and 1. Probability of 
+#' @param transmission_rate Numeric scalar between 0 and 1. Probability of 
 #' transmission.
-#' @param prob_recovery Numeric scalar between 0 and 1. Probability of recovery.
+#' @param recovery_rate Numeric scalar between 0 and 1. Probability of recovery.
 #' @param x Object of class SIRCONN. 
 #' @param ... Currently ignore.
 #' @param n Number of individuals in the population.
@@ -20,8 +20,8 @@
 #'   n                   = 10000,
 #'   prevalence          = 0.01,
 #'   contact_rate        = 5,
-#'   prob_transmission   = 0.4,
-#'   prob_recovery       = 0.95
+#'   transmission_rate   = 0.4,
+#'   recovery_rate       = 0.95
 #' )
 #'   
 #' # Running and printing
@@ -31,12 +31,12 @@
 #' plot(model_sirconn,  main = "SIRCONN Model")
 #' @seealso epiworld-methods
 ModelSIRCONN <- function(
-    name, n, prevalence, contact_rate, prob_transmission, prob_recovery
+    name, n, prevalence, contact_rate, transmission_rate, recovery_rate
 ) {
 
   structure(
     ModelSIRCONN_cpp(name, n, prevalence, contact_rate, 
-                     prob_transmission, prob_recovery),
+                     transmission_rate, recovery_rate),
     class = c("epiworld_sirconn", "epiworld_model")
   )
 

R/ModelSIRLogit.R

@@ -6,7 +6,7 @@
 #' @param coef_infect_cols Integer vector. Columns in the coeficient.
 #' @param coef_recover_cols Integer vector. Columns in the coeficient.
 #' @param prob_infection Numeric scalar. Baseline probability of infection.
-#' @param prob_recovery  Numeric scalar. Baseline probability of recovery.
+#' @param recovery_rate  Numeric scalar. Baseline probability of recovery.
 #' @param prevalence Numeric scalar. Prevalence (initial state) in proportion.
 #'
 #' @export
@@ -25,7 +25,7 @@
 #'   Female    = sample.int(2, n, replace = TRUE) - 1
 #' )
 #' 
-#' # Declare coefficients for each sex regarding infectiousness and recovery.
+#' # Declare coefficients for each sex regarding transmission_rate and recovery.
 #' coef_infect  <- c(.1, -2, 2)
 #' coef_recover <- rnorm(2)
 #' 
@@ -38,7 +38,7 @@
 #'   coef_infect_cols  = 1L:ncol(X),
 #'   coef_recover_cols = 1L:ncol(X),
 #'   prob_infection = .8,
-#'   prob_recovery = .3,
+#'   recovery_rate = .3,
 #'   prevalence = .01
 #' )
 #' 
@@ -69,7 +69,7 @@ ModelSIRLogit <- function(
   coef_infect_cols,
   coef_recover_cols,
   prob_infection,
-  prob_recovery,
+  recovery_rate,
   prevalence
 ) {
 
@@ -83,7 +83,7 @@ ModelSIRLogit <- function(
       coef_infect_cols - 1L,
       coef_recover_cols - 1L,
       prob_infection,
-      prob_recovery,
+      recovery_rate,
       prevalence
     ),
     class = c("epiworld_sir", "epiworld_model")

R/ModelSIS.R

@@ -4,9 +4,9 @@
 #'
 #' @param name String. Name of the virus.
 #' @param prevalence Double. Initial proportion of individuals with the virus.
-#' @param infectiousness Numeric scalar between 0 and 1. Virus's rate of 
+#' @param transmission_rate Numeric scalar between 0 and 1. Virus's rate of 
 #' infection.
-#' @param recovery Numeric scalar between 0 and 1. Rate of recovery from virus. 
+#' @param recovery_rate Numeric scalar between 0 and 1. Rate of recovery from virus. 
 #' @param x Object of class SIS. 
 #' @param ... Currently ignore. 
 #' @export
@@ -16,7 +16,7 @@
 #' @aliases epiworld_sis
 #' @examples 
 #' model_sis <- ModelSIS(name = "COVID-19", prevalence = 0.01, 
-#'                      infectiousness = 0.9, recovery = 0.1)
+#'                      transmission_rate = 0.9, recovery_rate = 0.1)
 #' 
 #' # Adding a small world population
 #' agents_smallworld(
@@ -36,10 +36,10 @@
 #' 
 #' @seealso epiworld-methods
 ModelSIS <- function(
-    name, prevalence, infectiousness, recovery) {
+    name, prevalence, transmission_rate, recovery_rate) {
 
   structure(
-    ModelSIS_cpp(name, prevalence, infectiousness, recovery),
+    ModelSIS_cpp(name, prevalence, transmission_rate, recovery_rate),
     class = c("epiworld_sis", "epiworld_model")
   )
 

R/ModelSURV.R

@@ -17,7 +17,7 @@
 #' @param prop_vax_redux_infect Double. Factor by which the vaccine reduces
 #' the chances of becoming infected.
 #' @param surveillance_prob Double. Probability of testing an agent.
-#' @param prob_transmission Double. Raw transmission probability.
+#' @param transmission_rate Double. Raw transmission probability.
 #' @param prob_death Double. Raw probability of death for symptomatic 
 #' individuals.
 #' @param prob_noreinfect Double. Probability of no re-infection.
@@ -40,7 +40,7 @@
 #'   prop_vax_redux_transm = 0.8,
 #'   prop_vax_redux_infect = 0.95,
 #'   surveillance_prob     = 0.1,
-#'   prob_transmission     = 0.2,
+#'   transmission_rate     = 0.2,
 #'   prob_death            = 0.001,
 #'   prob_noreinfect       = 0.5
 #' )
@@ -65,13 +65,13 @@
 ModelSURV <- function(
     name, prevalence, efficacy_vax, latent_period, infect_period, prob_symptoms, 
     prop_vaccinated, prop_vax_redux_transm, prop_vax_redux_infect, 
-    surveillance_prob, prob_transmission, prob_death, prob_noreinfect  
+    surveillance_prob, transmission_rate, prob_death, prob_noreinfect  
 ) {
 
   structure(
     ModelSURV_cpp(name, prevalence, efficacy_vax, latent_period, infect_period, 
                   prob_symptoms, prop_vaccinated, prop_vax_redux_transm, 
-                  prop_vax_redux_infect, surveillance_prob, prob_transmission, 
+                  prop_vax_redux_infect, surveillance_prob, transmission_rate, 
                   prob_death, prob_noreinfect),
     class = c("epiworld_surv", "epiworld_model")
   )

R/agents-methods.R

@@ -20,8 +20,8 @@
 #' n                   = 10000,
 #' prevalence          = 0.01,
 #' contact_rate        = 5,
-#' prob_transmission   = 0.4,
-#' prob_recovery       = 0.95
+#' transmission_rate   = 0.4,
+#' recovery_rate       = 0.95
 #' )
 #' 
 #' run(model_sirconn, ndays = 100, seed = 1912)

R/agents.R

@@ -20,8 +20,8 @@
 #' @examples
 #' 
 #' # Initializing SIR model with agents_smallworld
-#' sir <- ModelSIR(name = "COVID-19", prevalence = 0.01, infectiousness = 0.9, 
-#'                 recovery = 0.1)
+#' sir <- ModelSIR(name = "COVID-19", prevalence = 0.01, transmission_rate = 0.9, 
+#'                 recovery_rate = 0.1)
 #' agents_smallworld(
 #'    sir,
 #'    n = 1000, 

R/cpp11.R

@@ -72,32 +72,32 @@ get_generation_time_cpp <- function(model) {
   .Call(`_epiworldR_get_generation_time_cpp`, model)
 }
 
-ModelSURV_cpp <- function(name, prevalence, efficacy_vax, latent_period, prob_symptoms, prop_vaccinated, prop_vax_redux_transm, infect_period, prop_vax_redux_infect, surveillance_prob, prob_transmission, prob_death, prob_noreinfect) {
-  .Call(`_epiworldR_ModelSURV_cpp`, name, prevalence, efficacy_vax, latent_period, prob_symptoms, prop_vaccinated, prop_vax_redux_transm, infect_period, prop_vax_redux_infect, surveillance_prob, prob_transmission, prob_death, prob_noreinfect)
+ModelSURV_cpp <- function(name, prevalence, efficacy_vax, latent_period, prob_symptoms, prop_vaccinated, prop_vax_redux_transm, infect_period, prop_vax_redux_infect, surveillance_prob, transmission_rate, prob_death, prob_noreinfect) {
+  .Call(`_epiworldR_ModelSURV_cpp`, name, prevalence, efficacy_vax, latent_period, prob_symptoms, prop_vaccinated, prop_vax_redux_transm, infect_period, prop_vax_redux_infect, surveillance_prob, transmission_rate, prob_death, prob_noreinfect)
 }
 
-ModelSEIR_cpp <- function(name, prevalence, infectiousness, incubation_days, recovery) {
-  .Call(`_epiworldR_ModelSEIR_cpp`, name, prevalence, infectiousness, incubation_days, recovery)
+ModelSEIR_cpp <- function(name, prevalence, transmission_rate, incubation_days, recovery_rate) {
+  .Call(`_epiworldR_ModelSEIR_cpp`, name, prevalence, transmission_rate, incubation_days, recovery_rate)
 }
 
-ModelSIS_cpp <- function(name, prevalence, infectiousness, recovery) {
-  .Call(`_epiworldR_ModelSIS_cpp`, name, prevalence, infectiousness, recovery)
+ModelSIS_cpp <- function(name, prevalence, transmission_rate, recovery_rate) {
+  .Call(`_epiworldR_ModelSIS_cpp`, name, prevalence, transmission_rate, recovery_rate)
 }
 
-ModelSIRCONN_cpp <- function(name, n, prevalence, contact_rate, prob_transmission, prob_recovery) {
-  .Call(`_epiworldR_ModelSIRCONN_cpp`, name, n, prevalence, contact_rate, prob_transmission, prob_recovery)
+ModelSIRCONN_cpp <- function(name, n, prevalence, contact_rate, transmission_rate, recovery_rate) {
+  .Call(`_epiworldR_ModelSIRCONN_cpp`, name, n, prevalence, contact_rate, transmission_rate, recovery_rate)
 }
 
-ModelSIR_cpp <- function(name, prevalence, infectiousness, recovery) {
-  .Call(`_epiworldR_ModelSIR_cpp`, name, prevalence, infectiousness, recovery)
+ModelSIR_cpp <- function(name, prevalence, transmission_rate, recovery_rate) {
+  .Call(`_epiworldR_ModelSIR_cpp`, name, prevalence, transmission_rate, recovery_rate)
 }
 
-ModelSEIRCONN_cpp <- function(name, n, prevalence, contact_rate, prob_transmission, incubation_days, prob_recovery) {
-  .Call(`_epiworldR_ModelSEIRCONN_cpp`, name, n, prevalence, contact_rate, prob_transmission, incubation_days, prob_recovery)
+ModelSEIRCONN_cpp <- function(name, n, prevalence, contact_rate, transmission_rate, incubation_days, recovery_rate) {
+  .Call(`_epiworldR_ModelSEIRCONN_cpp`, name, n, prevalence, contact_rate, transmission_rate, incubation_days, recovery_rate)
 }
 
-ModelSIRLogit_cpp <- function(vname, data, ncols, coefs_infect, coefs_recover, coef_infect_cols, coef_recover_cols, prob_infection, prob_recovery, prevalence) {
-  .Call(`_epiworldR_ModelSIRLogit_cpp`, vname, data, ncols, coefs_infect, coefs_recover, coef_infect_cols, coef_recover_cols, prob_infection, prob_recovery, prevalence)
+ModelSIRLogit_cpp <- function(vname, data, ncols, coefs_infect, coefs_recover, coef_infect_cols, coef_recover_cols, prob_infection, recovery_rate, prevalence) {
+  .Call(`_epiworldR_ModelSIRLogit_cpp`, vname, data, ncols, coefs_infect, coefs_recover, coef_infect_cols, coef_recover_cols, prob_infection, recovery_rate, prevalence)
 }
 
 ModelDiffNet_cpp <- function(name, prevalence, prob_adopt, normalize_exposure, data, data_ncols, data_cols, params) {

R/data.R

@@ -21,9 +21,9 @@
 #'   n                 = 10000,
 #'   prevalence        = 0.1,
 #'   contact_rate      = 2.0, 
-#'   prob_transmission = 0.8,
+#'   transmission_rate = 0.8,
 #'   incubation_days   = 7.0,
-#'   prob_recovery     = 0.3
+#'   recovery_rate     = 0.3
 #' )
 #' 
 #' # Running the simulation for 50 steps (days)

R/global-actions.R

@@ -16,8 +16,8 @@
 #'   n                   = 10000,
 #'   prevalence          = 0.01,
 #'   contact_rate        = 5,
-#'   prob_transmission   = 0.4,
-#'   prob_recovery       = 0.95
+#'   transmission_rate   = 0.4,
+#'   recovery_rate       = 0.95
 #' )
 #' 
 #' # Creating a tool
@@ -45,8 +45,8 @@
 #'   n                   = 10000,
 #'   prevalence          = 0.01,
 #'   contact_rate        = 5,
-#'   prob_transmission   = 0.4,
-#'   prob_recovery       = 0.95
+#'   transmission_rate   = 0.4,
+#'   recovery_rate       = 0.95
 #' )
 #' 
 #' closure_day_10 <- globalaction_set_params("Contact rate", 0, day = 10)

R/make_saver.R

@@ -34,7 +34,7 @@
 #'   prevalence = 0.01,
 #'   n = 1000,
 #'   contact_rate = 2,
-#'   prob_transmission = 0.9, prob_recovery = 0.1
+#'   transmission_rate = 0.9, recovery_rate = 0.1
 #'   )
 #' 
 #' # Generating a saver

R/model-methods.R

@@ -29,8 +29,8 @@ stopifnot_model <- function(model) {
 #' n                   = 10000,
 #' prevalence          = 0.01,
 #' contact_rate        = 5,
-#' prob_transmission   = 0.4,
-#' prob_recovery       = 0.95
+#' transmission_rate   = 0.4,
+#' recovery_rate       = 0.95
 #' )
 #' 
 #' # Queuing - If you wish to implement the queuing function, declare whether