Update README.md

README.md

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-# epiworlRcalibrate
+
+## 📊 simulate_calibrate_sir() and 🔧 calibrate_sir()
+
+### ✍️ Authors: George Vega Yon, Sima NJF
+
+### 🚀 simulate_calibrate_sir()
+
+This function simulates and calibrates an SIR (Susceptible-Infected-Recovered) model using TensorFlow and Keras in R. It generates simulated epidemic data, trains a Convolutional Neural Network (CNN) model, and evaluates the model's performance. Key steps include generating model parameters, running simulations, preparing data for training, splitting data, building, and evaluating the CNN model.
+
+📝 **Function Arguments**:
+- `N`: Number of simulation runs
+- `n`: Population size
+- `ndays`: Number of days to simulate
+- `ncores`: Number of cores for parallel processing
+
+```{r}
+# 📌 Example usage of simulate_calibrate_sir function
+library(epiworldR)
+library(keras3)
+library(tensorflow)
+library(reticulate)
+reticulate::import("numpy")
+library(epiworldRcalibrate)
+
+N <- 2e4
+n <- 5000
+ndays <- 50
+ncores <- 20
+epochs <- 2
+verbose <- 2
+
+simulate_calibrate_sir <- function(N, n, ndays, ncores, epochs, verbose) {
+  # ⚙️ Generate Theta and Seeds
+  theta <- generate_theta(N, n)
+  seeds <- sample.int(.Machine$integer.max, N, TRUE)
+
+  # 🧪 Run Simulations
+  matrices <- run_simulations(N, n, ndays, ncores, theta, seeds)
+
+  # 🔍 Filter Non-Null Data
+  filtered_data <- filter_non_null(matrices, theta)
+  matrices <- filtered_data$matrices
+  theta <- filtered_data$theta
+  N <- filtered_data$N
+
+  # 📊 Prepare Data for TensorFlow
+  arrays_1d <- prepare_data_for_tensorflow(matrices, N)
+  theta2 <- as.data.table(copy(theta))
+  theta2$crate <- plogis(theta2$crate / 10)
+
+  # 🔀 Split Data into Training and Testing Sets
+  data_split <- split_data(arrays_1d, theta2, N)
+  train <- data_split$train
+  test <- data_split$test
+
+  # 🏗️ Build and Train the CNN Model
+  model <- build_cnn_model(dim(arrays_1d)[-1], ncol(theta))
+  train_model(model, train, epochs = epochs, verbose = verbose)
+
+  # 📈 Evaluate the Model
+  eval_results <- evaluate_model(model, test, theta)
+  pred <- eval_results$pred
+  MAEs <- eval_results$MAEs
+  plot_results(pred, test, theta, MAEs, N, floor(N * 0.7))
+  return(list(pred = pred, MAEs = MAEs))
+}
+
+# ▶️ Call the function
+simulate_calibrate_sir(N, n, ndays, ncores, epochs, verbose)
+```
+
+### 🔧 calibrate_sir()
+
+This function is used to predict SIR model parameters based on input data. It takes as input a numeric matrix or array containing counts of infected individuals over a period of 30 or 60 days and returns predicted values for parameters like prevalence, contact rate, transmission probability, and precision.
+
+```{r}
+# 📌 Example usage of calibrate_sir function
+library(epiworldR)
+library(keras3)
+library(tensorflow)
+library(reticulate)
+reticulate::import("numpy")
+library(epiworldRcalibrate)
+
+N <- 1
+n <- 5000
+set.seed(123)
+theta <- generate_theta(N, n)
+ncores <- 20
+ndays <- 60
+seeds <- 123
+
+# 🧪 Run simulations
+m <- epiworldR::ModelSIRCONN(
+  "mycon",
+  prevalence = theta$preval[1],
+  contact_rate = theta$crate[1],
+  transmission_rate = theta$ptran[1],
+  recovery_rate = theta$prec[1],
+  n = n
+)
+
+verbose_off(m)
+run(m, ndays = ndays)
+incidence <- epiworldR::plot_incidence(m, plot = FALSE)
+data <- incidence$Infected
+
+# 💾 Save theta and simulations data
+theta2 <- as.data.table(copy(theta))
+theta2$crate <- plogis(theta2$crate / 10)
+
+# 🔧 Calibrate SIR model
+result <- calibrate_sir(data)
+print(result)
+```
+
+