diff --git a/DESCRIPTION b/DESCRIPTION
index 0a82571..50448ae 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: accept
 Title: The Acute COPD Exacerbation Prediction Tool (ACCEPT)
-Version: 1.1.1
+Version: 1.2.0
 Authors@R: c(
     person("Amin", "Adibi", email = "adibi@alumni.ubc.ca", role = c("aut", "cre")),
     person("Mohsen", "Sadatsafavi", email = "mohsen.sadatsafavi@ubc.ca", role = c("aut", "cph")),
diff --git a/NAMESPACE b/NAMESPACE
index 2a45c64..49246ff 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -9,6 +9,7 @@ export(accept)
 export(accept1)
 export(accept2)
 export(accept3)
+export(accept3_cprd)
 export(plotExacerbations)
 export(plotHeatMap)
 export(predictCountProb)
@@ -16,6 +17,10 @@ export(set_openai_api_key)
 export(show_openai_api_key)
 import(dplyr)
 import(tidyselect)
+importFrom(dplyr,mutate)
+importFrom(dplyr,select)
+importFrom(dplyr,starts_with)
+importFrom(dplyr,tibble)
 importFrom(hardhat,scream)
 importFrom(reldist,wtd.quantile)
 importFrom(splines,bs)
diff --git a/R/predict.R b/R/predict.R
index b2888dd..a472a41 100644
--- a/R/predict.R
+++ b/R/predict.R
@@ -667,7 +667,7 @@ accept2 <- function (patientData, random_sampling_N = 1e2, lastYrExacCol = "Last
 #' @param version indicates which version of ACCEPT needs to be called. Options include "accept1", "accept2", "accept3" (default).
 #' @param prediction_interval default is FALSE. If set to TRUE, returns prediction intervals of the predictions.
 #' @param return_predictors default is FALSE. IF set to TRUE, returns the predictors along with prediction results.
-#' @param country Required for accept3 version. Three-letter ISO country code (e.g., "CAN", "USA", "GBR"). Supported countries: ARG, AUS, BRA, CAN, COL, DEU, DNK, ESP, FRA, GBR, ITA, JPN, KOR, MEX, NLD, NOR, SWE, USA. For unsupported countries, provide obs_modsev_risk parameter or add it as a column in the data.
+#' @param country Required for accept3 version. Three-letter ISO country code (e.g., "CAN", "USA", "GBR"). Supported countries: ARG, AUS, BRA, CAN, COL, DEU, DNK, ESP, FRA, GBR, ITA, JPN, KOR, MEX, NLD, NOR, SWE, USA. For the UK, two care-setting-specific versions are available: "GBR-primary" (primary care, ACCEPT 3.0-CPRD) and "GBR-specialty" (specialty care). Bare "GBR" defaults to specialty care with a warning. For unsupported countries, provide obs_modsev_risk parameter or add it as a column in the data.
 #' @param obs_modsev_risk Observed moderate-to-severe exacerbation risk for unsupported countries. Can be provided as a parameter or as a column in newdata.
 #' @param ... for other versions of accept.
 #' @return patientData with prediction.
@@ -700,8 +700,8 @@ accept <- function(newdata, format="tibble", version = "accept3", prediction_int
     if (length(invalid_rows) > 0) {
       invalid_ids <- if ("ID" %in% colnames(newdata)) newdata$ID[invalid_rows] else invalid_rows
       stop(paste0("LastYrSevExacCount exceeds LastYrExacCount for patient(s) with ID(s): ",
-                   paste(invalid_ids, collapse = ", "),
-                   ". Severe exacerbation count cannot be greater than total exacerbation count."))
+                  paste(invalid_ids, collapse = ", "),
+                  ". Severe exacerbation count cannot be greater than total exacerbation count."))
     }
   }
 
@@ -746,6 +746,26 @@ accept <- function(newdata, format="tibble", version = "accept3", prediction_int
 
     # Validate and normalize country code
     country <- toupper(country)
+
+    # GBR has two care-setting-specific versions:
+    #   "GBR-PRIMARY"   -> primary care, ACCEPT 3.0-CPRD (accept3_cprd)
+    #   "GBR-SPECIALTY" -> specialty care, standard accept3 GBR recalibration
+    # Bare "GBR" is ambiguous and defaults to specialty care with a warning.
+    if (country %in% c("GBR", "GBR-PRIMARY", "GBR-SPECIALTY", "GBR-SPECIALITY")) {
+      if (country == "GBR") {
+        warning("country = 'GBR' is ambiguous: defaulting to specialty care. Use 'GBR-primary' for primary care (ACCEPT 3.0-CPRD) or 'GBR-specialty' for specialty care.")
+        country <- "GBR-SPECIALTY"
+      }
+
+      if (country == "GBR-PRIMARY") {
+        # Primary care: route to the CPRD-recalibrated model.
+        return(accept3_cprd(patientData = newdata, return_predictors = return_predictors))
+      }
+
+      # Specialty care: use the standard accept3 GBR recalibration below.
+      country <- "GBR"
+    }
+
     supported_countries <- c("ARG", "AUS", "BRA", "CAN", "COL", "DEU", "DNK", "ESP", "FRA", "GBR", "ITA", "JPN", "KOR", "MEX", "NLD", "NOR", "SWE", "USA")
 
     if (!country %in% supported_countries && country != "XXX") {
@@ -814,7 +834,7 @@ accept <- function(newdata, format="tibble", version = "accept3", prediction_int
     # Add predictor columns if requested
     if (return_predictors) {
       result_df <- cbind(newdata, result_df[, c("predicted_exac_probability", "predicted_exac_rate",
-                                                  "predicted_severe_exac_probability", "predicted_severe_exac_rate")])
+                                                "predicted_severe_exac_probability", "predicted_severe_exac_rate")])
     }
 
     return(as_tibble(result_df))
@@ -1050,8 +1070,8 @@ predictCountProb <- function (patientResults, n = 10, shortened = TRUE){
 accept3 <- function(country, ID, age, male, BMI, smoker, mMRC = NA, CVD, ICS, LABA, LAMA, LastYrExacCount,  LastYrSevExacCount, FEV1, SGRQ = NA, oxygen, obs_modsev_risk) {
   # Create base tibble
   df <- tibble(country = country, ID = ID, age = age, male = male, BMI = BMI, smoker = smoker, statin = CVD,
-                   ICS = ICS, LABA = LABA, LAMA = LAMA, LastYrExacCount = LastYrExacCount, LastYrSevExacCount = LastYrSevExacCount,
-                   FEV1 = FEV1, oxygen = oxygen, obs_modsev_risk = obs_modsev_risk)
+               ICS = ICS, LABA = LABA, LAMA = LAMA, LastYrExacCount = LastYrExacCount, LastYrSevExacCount = LastYrSevExacCount,
+               FEV1 = FEV1, oxygen = oxygen, obs_modsev_risk = obs_modsev_risk)
 
   # Add SGRQ or mMRC depending on what's available
   if (!is.na(SGRQ)) {
@@ -1082,3 +1102,258 @@ accept3 <- function(country, ID, age, male, BMI, smoker, mMRC = NA, CVD, ICS, LA
 }
 
 
+
+
+#' Predicts COPD exacerbation risk for UK primary-care patients using ACCEPT 3.0-CPRD
+#'
+#' @description
+#' ACCEPT 3.0-CPRD is a UK primary-care recalibration of ACCEPT 2.0 derived from
+#' the CPRD (Clinical Practice Research Datalink) primary-care dataset. It applies
+#' two Cox-model recalibration parameters: an optimism-corrected baseline
+#' hazard (H0) and slope (beta), separately for moderate-to-severe and severe
+#' exacerbations.
+#'
+#' When optional predictors (LABA, oxygen, ICS, LAMA, statin/CVD, BMI, smoker)
+#' are missing, a CPRD-specific sequential triangular regression imputation model
+#' fills them in before prediction.
+#'
+#' @param patientData A tibble of patients in the same format as
+#'   \code{samplePatients}. Mandatory columns: \code{ID}, \code{age},
+#'   \code{male}, \code{FEV1}, \code{LastYrExacCount},
+#'   \code{LastYrSevExacCount}, and either \code{mMRC} or \code{SGRQ}.
+#'   Optional columns: \code{LABA}, \code{oxygen}, \code{ICS}, \code{LAMA},
+#'   \code{statin}, \code{BMI}, \code{smoker}.
+#' @param return_predictors Logical. If \code{TRUE}, the input predictors are
+#'   returned alongside predictions. Default \code{FALSE}.
+#'
+#'@param quiet Logical. If \code{TRUE} (default), suppresses imputation messages.
+#'  Set to \code{FALSE} to report which predictors were imputed.
+#'
+#' @return A tibble with columns:
+#'   \itemize{
+#'     \item \code{predicted_exac_probability}        - recalibrated moderate-to-severe risk
+#'     \item \code{predicted_exac_rate}               - corresponding rate (-log(1-p))
+#'     \item \code{predicted_severe_exac_probability} - recalibrated severe risk
+#'     \item \code{predicted_severe_exac_rate}        - corresponding rate
+#'   }
+#'   If \code{return_predictors = TRUE}, input columns are prepended.
+#'
+#' @details
+#' ## Recalibration formula
+#' For each outcome the recalibrated 1-year risk is:
+#' \deqn{\hat{p}_{UK} = 1 - \exp\!\Bigl(-H_0 \cdot \exp(\beta \cdot \log(-\log(1-\hat{p}_{2})))\Bigr)}
+#' where \eqn{\hat{p}_{2}} is the ACCEPT 2.0 predicted probability, and
+#' \eqn{H_0} and \eqn{\beta} are the optimism-corrected parameters from the
+#' CPRD Cox model (Table in manuscript):
+#' \itemize{
+#'   \item Moderate-to-severe: \eqn{H_0 = 0.676}, \eqn{\beta = 0.986}
+#'   \item Severe:             \eqn{H_0 = 0.482}, \eqn{\beta = 1.124}
+#' }
+#'
+#' ## Optional-predictor imputation
+#' Missing optional predictors are imputed sequentially (triangular matrix):
+#' LABA -> oxygen -> ICS -> LAMA -> statin -> BMI -> smoker.
+#' Each model uses mandatory predictors (age, male, mMRC, FEV1,
+#' LastYrSevExacCount, LastYrExacCount) plus any previously imputed
+#' optional predictors. Binary variables are imputed with logistic regression
+#' (probability rounded to 0/1); BMI with linear regression.
+#'
+#' @examples
+#' results <- accept3_cprd(samplePatients)
+#'
+#' @seealso \code{\link{accept}}, \code{\link{accept2}}, \code{\link{accept3}}
+#'
+#' @importFrom dplyr tibble mutate select starts_with
+#' @export
+accept3_cprd <- function(patientData,
+                       return_predictors = FALSE,
+                       quiet = TRUE) {
+
+  if (!tibble::is_tibble(patientData)) {
+    stop("patientData must be a tibble. Use as_tibble() to convert.")
+  }
+  if (any(patientData$LastYrSevExacCount > patientData$LastYrExacCount)) {
+    invalid_ids <- patientData$ID[patientData$LastYrSevExacCount > patientData$LastYrExacCount]
+    stop(paste0("LastYrSevExacCount exceeds LastYrExacCount for patient(s): ",
+                paste(invalid_ids, collapse = ", "),
+                ". Severe count cannot exceed total count."))
+  }
+
+  # 1. Convert mMRC -> SGRQ if needed
+  if (!"SGRQ" %in% colnames(patientData)) {
+    if ("CAT" %in% colnames(patientData)) {
+      warning("SGRQ not found. Using CAT score instead.")
+      patientData$SGRQ <- 18.87 + 1.53 * patientData$CAT
+    } else if ("mMRC" %in% colnames(patientData)) {
+      message("SGRQ not found. Using mMRC score instead.")
+      patientData$SGRQ <- 20.43 + 14.77 * patientData$mMRC
+    } else {
+      stop("Either mMRC, SGRQ, or CAT must be provided. None were found in patientData.")
+    }
+  }
+
+  # 2. CPRD-specific optional-predictor imputation
+  # Intercepts and coefficients from Table A3 of the ACCEPT 3.0-CPRD manuscript.
+  # Column order in each coef vector:
+  #   (Intercept), age, male, mMRC, FEV1, LastYrSevExacCount,
+  #   LastYrExacCount [, LABA [, oxygen [, ICS [, LAMA [, statin [, BMI]]]]]]
+
+  cprd_imp <- list(
+
+    LABA = list(
+      binary = TRUE,
+      coef   = c(0.418, -0.013, 0.009, 0.438, -0.004, -0.063, 0.333)
+    ),
+
+    oxygen = list(
+      binary = TRUE,
+      coef   = c(-7.600, 0.006, -0.197, 0.942, -0.005, 0.188, 0.040,
+                 0.556)                                     # +LABA
+    ),
+
+    ICS = list(
+      binary = TRUE,
+      coef   = c(-1.430, -0.004, -0.157, 0.085, -0.002, -0.030, 0.168,
+                 3.372, 0.409)                              # +LABA, oxygen
+    ),
+
+    LAMA = list(
+      binary = TRUE,
+      coef   = c(-0.586, -0.007, 0.089, 0.337, -0.003, 0.000, 0.153,
+                 1.545, 0.203, -0.632)                     # +LABA, oxygen, ICS
+    ),
+
+    statin = list(
+      binary = TRUE,
+      coef   = c(-2.711, 0.015, 0.200, -0.004, 0.001, -0.032, 0.009,
+                 0.180, -0.196, -0.259, 0.207)             # +LABA, oxygen, ICS, LAMA
+    ),
+
+    BMI = list(
+      binary    = FALSE,
+      clamp_low = 10,
+      clamp_hi  = 60,
+      coef      = c(28.944, -0.074, 0.195, 0.421, 0.027, -0.527, 0.010,
+                    0.451, -0.093, -0.051, -0.105, 1.629) # +LABA, oxygen, ICS, LAMA, statin
+    ),
+
+    smoker = list(
+      binary = TRUE,
+      coef   = c(5.449, -0.061, -0.099, 0.123, 0.000, 0.039, -0.017,
+                 -0.056, -0.648, -0.246, 0.069, 0.031, -0.058) # all optional
+    )
+  )
+
+  # Sequential imputation order matches the triangular matrix
+  optional_order <- c("LABA", "oxygen", "ICS", "LAMA", "statin", "BMI", "smoker")
+
+  # Use mMRC if available, otherwise back-transform from SGRQ
+  if ("SGRQ" %in% colnames(patientData)) {
+    if (!"mMRC" %in% colnames(patientData)) {
+      patientData$mMRC <- (patientData$SGRQ - 20.43) / 14.77
+    } else {
+      na_idx <- is.na(patientData$mMRC)
+      if (any(na_idx)) {
+        patientData$mMRC[na_idx] <- (patientData$SGRQ[na_idx] - 20.43) / 14.77
+      }
+    }
+  }
+  mandatory_preds <- c("age", "male", "mMRC", "FEV1",
+                       "LastYrSevExacCount", "LastYrExacCount")
+
+  # Mandatory predictors drive both the optional-predictor imputation and the
+  # downstream accept2() call. Any remaining NA would silently propagate to NA
+  # predictions, so fail fast with a clear message.
+  missing_mandatory <- mandatory_preds[!mandatory_preds %in% colnames(patientData)]
+  if (length(missing_mandatory) > 0) {
+    stop(paste0("accept3_cprd: missing mandatory predictor column(s): ",
+                paste(missing_mandatory, collapse = ", "), "."))
+  }
+  na_mandatory <- vapply(mandatory_preds, function(v) any(is.na(patientData[[v]])), logical(1))
+  if (any(na_mandatory)) {
+    stop(paste0("accept3_cprd: mandatory predictor(s) contain missing values that could not be resolved: ",
+                paste(mandatory_preds[na_mandatory], collapse = ", "),
+                ". Provide complete values (mMRC can be supplied directly or via SGRQ)."))
+  }
+
+  imputed_vars <- character(0)
+
+  for (vname in optional_order) {
+    if (!vname %in% colnames(patientData) ||
+        any(is.na(patientData[[vname]]))) {
+
+      spec      <- cprd_imp[[vname]]
+      pred_cols <- c(mandatory_preds, imputed_vars)
+
+      X  <- as.matrix(cbind(1, patientData[, pred_cols]))
+      X  <- matrix(as.numeric(X), nrow = nrow(X), ncol = ncol(X))
+      lp <- as.vector(X %*% spec$coef)
+
+      if (spec$binary) {
+        pred_vals <- round(exp(lp) / (1 + exp(lp)))
+      } else {
+        pred_vals <- lp
+        if (!is.null(spec$clamp_low)) pred_vals <- pmax(pred_vals, spec$clamp_low)
+        if (!is.null(spec$clamp_hi))  pred_vals <- pmin(pred_vals, spec$clamp_hi)
+      }
+
+      if (!vname %in% colnames(patientData)) {
+        patientData[[vname]] <- pred_vals
+        if (!quiet) message(paste0("accept3_cprd: '", vname, "' not found - imputed using CPRD model."))
+      } else {
+        na_idx <- is.na(patientData[[vname]])
+        patientData[[vname]][na_idx] <- pred_vals[na_idx]
+        if (any(na_idx) && !quiet)
+          message(paste0("accept3_cprd: ", sum(na_idx), " missing value(s) in '",
+                         vname, "' imputed using CPRD model."))
+      }
+    }
+
+    # Always track all optional variables for subsequent models
+    # regardless of whether they needed imputation
+    if (vname %in% colnames(patientData)) {
+      imputed_vars <- c(imputed_vars, vname)
+    }
+  }
+  # 3. ACCEPT 2.0 predictions
+  accept2_preds <- accept2(patientData = patientData)
+
+  p2_modsev <- accept2_preds$predicted_exac_probability
+  p2_sev    <- accept2_preds$predicted_severe_exac_probability
+
+  # 4. CPRD recalibration
+  # Optimism-corrected parameters from CPRD bootstrap:
+  #   Moderate-to-severe: Baseline Hazard H0 = 0.676 (95% CI 0.671-0.681)
+  #                       Slope beta         = 0.986 (95% CI 0.977-0.995)
+  #   Severe:             Baseline Hazard H0 = 0.482 (95% CI 0.472-0.495)
+  #                       Slope beta         = 1.124 (95% CI 1.107-1.142)
+
+  H0_modsev   <- 0.676
+  beta_modsev <- 0.986
+
+  H0_sev      <- 0.482
+  beta_sev    <- 1.124
+
+  lp_modsev <- log(-log(1 - p2_modsev))
+  lp_sev    <- log(-log(1 - p2_sev))
+
+  cprd_modsev <- 1 - exp(-H0_modsev * exp(beta_modsev * lp_modsev))
+  cprd_sev    <- 1 - exp(-H0_sev    * exp(beta_sev    * lp_sev))
+
+  # 4. Assemble output
+  out <- dplyr::tibble(
+    predicted_exac_probability        = round(cprd_modsev, 4),
+    predicted_exac_rate               = round(-log(1 - cprd_modsev), 4),
+    predicted_severe_exac_probability = round(cprd_sev, 4),
+    predicted_severe_exac_rate        = round(-log(1 - cprd_sev), 4)
+  )
+
+  if (return_predictors) {
+    out <- dplyr::bind_cols(patientData, out)
+  } else {
+    out$ID <- patientData$ID
+    out    <- dplyr::select(out, "ID", dplyr::everything())
+  }
+
+  return(out)
+}
diff --git a/man/accept.Rd b/man/accept.Rd
index d8db0fc..1ccc365 100644
--- a/man/accept.Rd
+++ b/man/accept.Rd
@@ -26,7 +26,7 @@ accept(
 
 \item{return_predictors}{default is FALSE. IF set to TRUE, returns the predictors along with prediction results.}
 
-\item{country}{Required for accept3 version. Three-letter ISO country code (e.g., "CAN", "USA", "GBR"). Supported countries: ARG, AUS, BRA, CAN, COL, DEU, DNK, ESP, FRA, GBR, ITA, JPN, KOR, MEX, NLD, NOR, SWE, USA. For unsupported countries, provide obs_modsev_risk parameter or add it as a column in the data.}
+\item{country}{Required for accept3 version. Three-letter ISO country code (e.g., "CAN", "USA", "GBR"). Supported countries: ARG, AUS, BRA, CAN, COL, DEU, DNK, ESP, FRA, GBR, ITA, JPN, KOR, MEX, NLD, NOR, SWE, USA. For the UK, two care-setting-specific versions are available: "GBR-primary" (primary care, ACCEPT 3.0-CPRD) and "GBR-specialty" (specialty care). Bare "GBR" defaults to specialty care with a warning. For unsupported countries, provide obs_modsev_risk parameter or add it as a column in the data.}
 
 \item{obs_modsev_risk}{Observed moderate-to-severe exacerbation risk for unsupported countries. Can be provided as a parameter or as a column in newdata.}
 
diff --git a/man/accept3_cprd.Rd b/man/accept3_cprd.Rd
new file mode 100644
index 0000000..4c12963
--- /dev/null
+++ b/man/accept3_cprd.Rd
@@ -0,0 +1,70 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/predict.R
+\name{accept3_cprd}
+\alias{accept3_cprd}
+\title{Predicts COPD exacerbation risk for UK primary-care patients using ACCEPT 3.0-CPRD}
+\usage{
+accept3_cprd(patientData, return_predictors = FALSE, quiet = TRUE)
+}
+\arguments{
+\item{patientData}{A tibble of patients in the same format as
+\code{samplePatients}. Mandatory columns: \code{ID}, \code{age},
+\code{male}, \code{FEV1}, \code{LastYrExacCount},
+\code{LastYrSevExacCount}, and either \code{mMRC} or \code{SGRQ}.
+Optional columns: \code{LABA}, \code{oxygen}, \code{ICS}, \code{LAMA},
+\code{statin}, \code{BMI}, \code{smoker}.}
+
+\item{return_predictors}{Logical. If \code{TRUE}, the input predictors are
+returned alongside predictions. Default \code{FALSE}.}
+
+\item{quiet}{Logical. If \code{TRUE} (default), suppresses imputation messages.
+Set to \code{FALSE} to report which predictors were imputed.}
+}
+\value{
+A tibble with columns:
+  \itemize{
+    \item \code{predicted_exac_probability}        - recalibrated moderate-to-severe risk
+    \item \code{predicted_exac_rate}               - corresponding rate (-log(1-p))
+    \item \code{predicted_severe_exac_probability} - recalibrated severe risk
+    \item \code{predicted_severe_exac_rate}        - corresponding rate
+  }
+  If \code{return_predictors = TRUE}, input columns are prepended.
+}
+\description{
+ACCEPT 3.0-CPRD is a UK primary-care recalibration of ACCEPT 2.0 derived from
+the CPRD (Clinical Practice Research Datalink) primary-care dataset. It applies
+two Cox-model recalibration parameters: an optimism-corrected baseline
+hazard (H0) and slope (beta), separately for moderate-to-severe and severe
+exacerbations.
+
+When optional predictors (LABA, oxygen, ICS, LAMA, statin/CVD, BMI, smoker)
+are missing, a CPRD-specific sequential triangular regression imputation model
+fills them in before prediction.
+}
+\details{
+## Recalibration formula
+For each outcome the recalibrated 1-year risk is:
+\deqn{\hat{p}_{UK} = 1 - \exp\!\Bigl(-H_0 \cdot \exp(\beta \cdot \log(-\log(1-\hat{p}_{2})))\Bigr)}
+where \eqn{\hat{p}_{2}} is the ACCEPT 2.0 predicted probability, and
+\eqn{H_0} and \eqn{\beta} are the optimism-corrected parameters from the
+CPRD Cox model (Table in manuscript):
+\itemize{
+  \item Moderate-to-severe: \eqn{H_0 = 0.676}, \eqn{\beta = 0.986}
+  \item Severe:             \eqn{H_0 = 0.482}, \eqn{\beta = 1.124}
+}
+
+## Optional-predictor imputation
+Missing optional predictors are imputed sequentially (triangular matrix):
+LABA -> oxygen -> ICS -> LAMA -> statin -> BMI -> smoker.
+Each model uses mandatory predictors (age, male, mMRC, FEV1,
+LastYrSevExacCount, LastYrExacCount) plus any previously imputed
+optional predictors. Binary variables are imputed with logistic regression
+(probability rounded to 0/1); BMI with linear regression.
+}
+\examples{
+results <- accept3_cprd(samplePatients)
+
+}
+\seealso{
+\code{\link{accept}}, \code{\link{accept2}}, \code{\link{accept3}}
+}
diff --git a/tests/testthat/test-predict.R b/tests/testthat/test-predict.R
index a487783..5c934bd 100644
--- a/tests/testthat/test-predict.R
+++ b/tests/testthat/test-predict.R
@@ -21,11 +21,11 @@ test_that("accept3 default prediction works", {
   expect_true("predicted_severe_exac_probability" %in% names(result))
   expect_true("predicted_severe_exac_rate" %in% names(result))
   expect_equal(nrow(result), 2)
-  
+
   # Verify rate calculation: rate = -log(1-p)
   expected_rate <- -log(1 - result$predicted_exac_probability)
   expect_equal(result$predicted_exac_rate, expected_rate, tolerance = 1e-6)
-  
+
   expected_sev_rate <- -log(1 - result$predicted_severe_exac_probability)
   expect_equal(result$predicted_severe_exac_rate, expected_sev_rate, tolerance = 1e-6)
 })
@@ -35,9 +35,9 @@ test_that("accept3 requires country parameter", {
 })
 
 test_that("accept1 and accept2 warn when country parameter is provided", {
-  expect_warning(accept(samplePatients[1,], version="accept1", country="CAN"), 
+  expect_warning(accept(samplePatients[1,], version="accept1", country="CAN"),
                  "not used by accept1.*ignored")
-  expect_warning(accept(samplePatients[1,], version="accept2", country="USA"), 
+  expect_warning(accept(samplePatients[1,], version="accept2", country="USA"),
                  "not used by accept2.*ignored")
 })
 
@@ -372,3 +372,94 @@ test_that("Different SGRQ values produce different results (no conversion)", {
   # Different SGRQ values should produce different results
   expect_false(isTRUE(all.equal(result_45$predicted_exac_probability, result_50$predicted_exac_probability)))
 })
+
+
+
+test_that("accept3_cprd returns correct columns and shape", {
+  results <- accept3_cprd(samplePatients)
+  expect_true(tibble::is_tibble(results))
+  expect_equal(nrow(results), nrow(samplePatients))
+  expect_true(all(c("ID", "predicted_exac_probability",
+                    "predicted_exac_rate",
+                    "predicted_severe_exac_probability",
+                    "predicted_severe_exac_rate") %in% colnames(results)))
+})
+
+test_that("accept3_cprd predictions are between 0 and 1", {
+  results <- accept3_cprd(samplePatients)
+  expect_true(all(results$predicted_exac_probability > 0 & results$predicted_exac_probability < 1))
+  expect_true(all(results$predicted_severe_exac_probability > 0 & results$predicted_severe_exac_probability < 1))
+})
+
+test_that("accept3_cprd gives expected predictions on fixed input", {
+  results <- accept3_cprd(samplePatients)
+  expect_equal(results$predicted_exac_probability[1], 0.6986, tolerance = 0.001)
+  expect_equal(results$predicted_severe_exac_probability[1], 0.356, tolerance = 0.001)
+})
+
+test_that("accept3_cprd imputes missing optional predictors", {
+  patients_no_optional <- samplePatients
+  patients_no_optional$LABA   <- NULL
+  patients_no_optional$oxygen <- NULL
+  patients_no_optional$ICS    <- NULL
+  patients_no_optional$LAMA   <- NULL
+  patients_no_optional$statin <- NULL
+  patients_no_optional$BMI    <- NULL
+  patients_no_optional$smoker <- NULL
+  results <- accept3_cprd(patients_no_optional, quiet = TRUE)
+  expect_equal(nrow(results), nrow(samplePatients))
+  expect_true(all(results$predicted_exac_probability > 0 & results$predicted_exac_probability < 1))
+})
+
+test_that("accept3_cprd errors on non-tibble input", {
+  expect_error(accept3_cprd(as.data.frame(samplePatients)))
+})
+
+test_that("accept3_cprd errors when severe count exceeds total", {
+  patients_invalid <- samplePatients
+  patients_invalid$LastYrSevExacCount[1] <- 99
+  expect_error(accept3_cprd(patients_invalid))
+})
+
+test_that("accept3_cprd errors when a mandatory predictor is NA", {
+  patients_na_mand <- samplePatients
+  patients_na_mand$FEV1[1] <- NA
+  expect_error(accept3_cprd(patients_na_mand), "mandatory predictor")
+})
+
+test_that("accept3_cprd back-fills NA mMRC from SGRQ", {
+  patients_mmrc_na <- samplePatients
+  patients_mmrc_na$mMRC    <- 2
+  patients_mmrc_na$mMRC[1] <- NA
+  patients_mmrc_na$SGRQ    <- 50
+  results <- accept3_cprd(patients_mmrc_na)
+  expect_equal(nrow(results), nrow(samplePatients))
+  expect_true(all(!is.na(results$predicted_exac_probability)))
+})
+
+test_that("accept(country = 'GBR-primary') routes to accept3_cprd", {
+  via_accept <- accept(samplePatients, country = "GBR-primary")
+  via_cprd   <- accept3_cprd(samplePatients)
+  expect_equal(via_accept$predicted_exac_probability,
+               via_cprd$predicted_exac_probability)
+  expect_equal(via_accept$predicted_severe_exac_probability,
+               via_cprd$predicted_severe_exac_probability)
+})
+
+test_that("accept(country = 'GBR-specialty') uses standard accept3 recalibration", {
+  specialty <- accept(samplePatients, country = "GBR-specialty")
+  primary   <- accept(samplePatients, country = "GBR-primary")
+  expect_true(tibble::is_tibble(specialty))
+  expect_equal(nrow(specialty), nrow(samplePatients))
+  # The two care settings use different recalibrations and should differ.
+  expect_false(isTRUE(all.equal(specialty$predicted_exac_probability,
+                                primary$predicted_exac_probability)))
+})
+
+test_that("accept(country = 'GBR') warns and defaults to specialty care", {
+  expect_warning(gbr <- accept(samplePatients, country = "GBR"),
+                 "ambiguous")
+  specialty <- accept(samplePatients, country = "GBR-specialty")
+  expect_equal(gbr$predicted_exac_probability,
+               specialty$predicted_exac_probability)
+})