diff --git a/man/opc.Rd b/man/opc.Rd
new file mode 100644
index 0000000..36c21b0
--- /dev/null
+++ b/man/opc.Rd
@@ -0,0 +1,198 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/operating_characteristics.R
+\name{opc}
+\alias{opc}
+\title{Operating characteristics for EWOC simulations}
+\usage{
+opc(sim_list, pdlt_list, mtd_list, toxicity_margin = NULL, mtd_margin = NULL)
+}
+\arguments{
+\item{sim_list}{a list of 'ewoc_simulation' objects for different scenarios
+created using the \code{\link[ewoc]{ewoc_simulation}} function.}
+
+\item{pdlt_list}{a list of functions to calculate the probability of toxicity with a numeric vector
+of doses as input and a numeric vector of probabilities as output.}
+
+\item{mtd_list}{a list of numerical values indicating the true MTD for each scenario.}
+
+\item{toxicity_margin}{a numerical value of the acceptable margin of distance from the
+\code{target_rate}.}
+
+\item{mtd_margin}{a numerical value of the acceptable margin of distance from the
+\code{mtd_list}.}
+}
+\value{
+\code{dlt_rate} See \code{\link[ewoc]{dlt_rate}}.
+
+\code{dose_toxicity} See \code{\link[ewoc]{optimal_toxicity}}.
+
+\code{mtd_toxicity} See \code{\link[ewoc]{optimal_toxicity}}.
+
+\code{statistics} See \code{\link[ewoc]{mtd_bias}} and \code{\link[ewoc]{mtd_mse}}.
+
+\code{dose_efficiency} See \code{\link[ewoc]{optimal_mtd}}.
+
+\code{mtd_efficiency} See \code{\link[ewoc]{optimal_mtd}}.
+
+\code{stop} See \code{\link[ewoc]{stop_rule}}.
+}
+\description{
+Generic operating characteristics for one or more scenarios in EWOC simulations.
+}
+\examples{
+\dontshow{
+### Only one simulation
+DLT <- 0
+dose <- 20
+step_zero <- ewoc_d1classic(DLT ~ dose, type = 'discrete',
+                          theta = 0.33, alpha = 0.25,
+                          min_dose = 20, max_dose = 100,
+                          dose_set = seq(20, 100, 20),
+                          rho_prior = matrix(1, ncol = 2, nrow = 1),
+                          mtd_prior = matrix(1, ncol = 2, nrow = 1),
+                          rounding = "nearest")
+response_sim <- response_d1classic(rho = 0.05, mtd = 60, theta = 0.33,
+                                 min_dose = 20, max_dose = 100)
+sim <- ewoc_simulation(step_zero = step_zero,
+                     n_sim = 1, sample_size = 30, n_cohort = 1,
+                     alpha_strategy = "conditional",
+                     response_sim = response_sim,
+                     fixed_first_cohort =  TRUE,
+                     ncores = 2)
+
+pdlt <- pdlt_d1classic(rho = 0.05, mtd = 60, theta = 0.33,
+                      min_dose = 20, max_dose = 100)
+
+opc(sim_list = list(sim), pdlt_list = list(pdlt),
+   mtd_list = list(60), toxicity_margin = 0.05, mtd_margin = 6)
+
+### Two or more simulations
+
+sim_list <- list()
+mtd_list <- list()
+pdlt_list <- list()
+
+DLT <- 0
+dose <- 20
+step_zero <- ewoc_d1classic(DLT ~ dose, type = 'discrete',
+                          theta = 0.33, alpha = 0.25,
+                          min_dose = 20, max_dose = 100,
+                          dose_set = seq(20, 100, 20),
+                          rho_prior = matrix(1, ncol = 2, nrow = 1),
+                          mtd_prior = matrix(1, ncol = 2, nrow = 1),
+                          rounding = "nearest")
+mtd_list[[1]] <- 60
+response_sim <- response_d1classic(rho = 0.05, mtd = mtd_list[[1]],
+                                 theta = 0.33,
+                                 min_dose = 20, max_dose = 100)
+sim_list[[1]] <- ewoc_simulation(step_zero = step_zero,
+                     n_sim = 1, sample_size = 30, n_cohort = 1,
+                     alpha_strategy = "conditional",
+                     response_sim = response_sim,
+                     fixed_first_cohort =  TRUE,
+                     ncores = 2)
+pdlt_list[[1]] <- pdlt_d1classic(rho = 0.05, mtd = mtd_list[[1]],
+                               theta = 0.33,
+                               min_dose = 20, max_dose = 100)
+
+mtd_list[[2]] <- 40
+response_sim <- response_d1classic(rho = 0.05, mtd = mtd_list[[2]],
+                                 theta = 0.33,
+                                 min_dose = 20, max_dose = 100)
+sim_list[[2]] <- ewoc_simulation(step_zero = step_zero,
+                     n_sim = 1, sample_size = 30, n_cohort = 1,
+                     alpha_strategy = "conditional",
+                     response_sim = response_sim,
+                     fixed_first_cohort =  TRUE,
+                     ncores = 2)
+
+pdlt_list[[2]] <- pdlt_d1classic(rho = 0.05, mtd = mtd_list[[2]],
+                               theta = 0.33,
+                               min_dose = 20, max_dose = 100)
+
+opc(sim_list = sim_list, pdlt_list = pdlt_list,
+   mtd_list = mtd_list, toxicity_margin = 0.05, mtd_margin = 6)
+
+
+
+}
+
+
+\dontrun{
+### Only one simulation
+DLT <- 0
+dose <- 20
+step_zero <- ewoc_d1classic(DLT ~ dose, type = 'discrete',
+                          theta = 0.33, alpha = 0.25,
+                          min_dose = 20, max_dose = 100,
+                          dose_set = seq(20, 100, 20),
+                          rho_prior = matrix(1, ncol = 2, nrow = 1),
+                          mtd_prior = matrix(1, ncol = 2, nrow = 1),
+                          rounding = "nearest")
+response_sim <- response_d1classic(rho = 0.05, mtd = 60, theta = 0.33,
+                                 min_dose = 20, max_dose = 100)
+sim <- ewoc_simulation(step_zero = step_zero,
+                     n_sim = 1, sample_size = 30, n_cohort = 1,
+                     alpha_strategy = "conditional",
+                     response_sim = response_sim,
+                     fixed_first_cohort =  TRUE,
+                     ncores = 2)
+
+pdlt <- pdlt_d1classic(rho = 0.05, mtd = 60, theta = 0.33,
+                      min_dose = 20, max_dose = 100)
+
+opc(sim_list = list(sim), pdlt_list = list(pdlt),
+   mtd_list = list(60), toxicity_margin = 0.05, mtd_margin = 6)
+
+### Two or more simulations
+
+sim_list <- list()
+mtd_list <- list()
+pdlt_list <- list()
+
+DLT <- 0
+dose <- 20
+step_zero <- ewoc_d1classic(DLT ~ dose, type = 'discrete',
+                          theta = 0.33, alpha = 0.25,
+                          min_dose = 20, max_dose = 100,
+                          dose_set = seq(20, 100, 20),
+                          rho_prior = matrix(1, ncol = 2, nrow = 1),
+                          mtd_prior = matrix(1, ncol = 2, nrow = 1),
+                          rounding = "nearest")
+mtd_list[[1]] <- 60
+response_sim <- response_d1classic(rho = 0.05, mtd = mtd_list[[1]],
+                                 theta = 0.33,
+                                 min_dose = 20, max_dose = 100)
+sim_list[[1]] <- ewoc_simulation(step_zero = step_zero,
+                     n_sim = 1, sample_size = 30, n_cohort = 1,
+                     alpha_strategy = "conditional",
+                     response_sim = response_sim,
+                     fixed_first_cohort =  TRUE,
+                     ncores = 2)
+pdlt_list[[1]] <- pdlt_d1classic(rho = 0.05, mtd = mtd_list[[1]],
+                               theta = 0.33,
+                               min_dose = 20, max_dose = 100)
+
+mtd_list[[2]] <- 40
+response_sim <- response_d1classic(rho = 0.05, mtd = mtd_list[[2]],
+                                 theta = 0.33,
+                                 min_dose = 20, max_dose = 100)
+sim_list[[2]] <- ewoc_simulation(step_zero = step_zero,
+                     n_sim = 1, sample_size = 30, n_cohort = 1,
+                     alpha_strategy = "conditional",
+                     response_sim = response_sim,
+                     fixed_first_cohort =  TRUE,
+                     ncores = 2)
+
+pdlt_list[[2]] <- pdlt_d1classic(rho = 0.05, mtd = mtd_list[[2]],
+                               theta = 0.33,
+                               min_dose = 20, max_dose = 100)
+
+opc(sim_list = sim_list, pdlt_list = pdlt_list,
+   mtd_list = mtd_list, toxicity_margin = 0.05, mtd_margin = 6)
+}
+
+}
+\references{
+Diniz, M. A., Tighiouart, M., & Rogatko, A. (2019). Comparison between continuous and discrete doses for model based designs in cancer dose finding. PloS one, 14(1).
+}