diff --git a/PMout.Rdata b/PMout.Rdata
new file mode 100644
index 000000000..3a8a88991
Binary files /dev/null and b/PMout.Rdata differ
diff --git a/R/PM_cov.R b/R/PM_cov.R
index 40159ad9a..cdd2ce2b0 100755
--- a/R/PM_cov.R
+++ b/R/PM_cov.R
@@ -54,10 +54,10 @@ PM_cov <- R6::R6Class(
     #' Creation of new `PM_cov` object is automatic and not generally necessary
     #' for the user to do.
     #' @param PMdata include `r template("PMdata")`.
-    #' @param path include `r template("path")`.
+    #' @param json A parsed JSON list from `result.json`, as read by [PM_parse].
     #' @param ... Not currently used.
-    initialize = function(PMdata = NULL, path = ".", ...) {
-      self$data <- private$make(PMdata, path)
+    initialize = function(PMdata = NULL, json = NULL, ...) {
+      self$data <- private$make(PMdata, json)
     },
     #' @description
     #' Stepwise linear regression of covariates and Bayesian posterior
@@ -94,29 +94,125 @@ PM_cov <- R6::R6Class(
     }
   ), # end public
   private = list(
-    make = function(data, path) {
-      if (file.exists(file.path(path, "posterior.csv"))) {
-        posts <- readr::read_csv(file = file.path( path, "posterior.csv"), show_col_types = FALSE)
-      } else if (inherits(data, "PM_cov") & !is.null(data$data)) { # file not there, and already PM_cov
+    make = function(data, json = NULL) {
+      # --- Obtain posterior data ---
+      if (!is.null(json) && !is.null(json$posterior)) {
+        par_names <- json$settings$parameters$parameters$name
+        # json$theta is already a matrix [nspp x npar]
+        theta_mat <- json$theta
+        colnames(theta_mat) <- par_names
+        theta <- tibble::as_tibble(theta_mat)
+        theta$prob <- json$w
+        # json$data$subjects is a data.frame with column $id
+        subject_ids <- json$data$subjects$id
+        # json$posterior is a matrix [nsubjects x nspp]
+        post_list <- lapply(seq_len(nrow(json$posterior)), function(i) {
+          probs <- json$posterior[i, ]
+          df <- theta[, par_names, drop = FALSE]
+          df$prob <- probs
+          df$id <- subject_ids[i]
+          df$point <- seq_len(length(probs))
+          df
+        })
+        posts <- dplyr::bind_rows(post_list) %>%
+          dplyr::relocate(id, point, dplyr::everything())
+      } else if (inherits(data, "PM_cov") & !is.null(data$data)) {
         class(data$data) <- c("PM_cov_data", "data.frame")
         return(data$data)
       } else {
         cli::cli_warn(c(
           "!" = "Unable to generate covariate-posterior information.",
-          "i" = "{.file {file.path(path, 'posterior.csv')}} does not exist, and result does not have valid {.code PM_cov} object."
+          "i" = "No JSON posterior and no valid {.code PM_cov} object."
         ))
         return(NULL)
       }
       
-      if (file.exists(file.path(path, "covs.csv"))) {
-        covs <- readr::read_csv(file = file.path(path, "covs.csv"), show_col_types = FALSE)
-      } else if (inherits(data, "PM_cov")) { # file not there, and already PM_cov
+      # --- Obtain covariates data ---
+      if (!is.null(json) && !is.null(json$data$subjects)) {
+        subjects <- json$data$subjects
+        n_subj <- nrow(subjects)
+        cov_list <- vector("list", n_subj * 50L)
+        list_idx <- 0L
+
+        for (i in seq_len(n_subj)) {
+          subj_id <- subjects$id[i]
+          occ_df <- subjects$occasions[[i]]
+          n_occ <- nrow(occ_df)
+
+          for (j in seq_len(n_occ)) {
+            block <- occ_df$index[j]
+            covs_inner <- occ_df[["covariates"]][["covariates"]]
+            cov_names <- names(covs_inner)
+            if (length(cov_names) == 0) next
+
+            # Collect segments per covariate for this occasion
+            seg_list <- setNames(vector("list", length(cov_names)), cov_names)
+            all_from <- numeric(0)
+            for (cn in cov_names) {
+              segs <- covs_inner[[cn]][["segments"]][[j]]
+              seg_list[[cn]] <- segs
+              all_from <- c(all_from, segs[["from"]])
+            }
+            all_times <- sort(unique(all_from))
+
+            # Evaluate covariates at each unique segment start time
+            for (t_val in all_times) {
+              row_data <- list(id = subj_id, time = t_val, block = block)
+              for (cn in cov_names) {
+                segs <- seg_list[[cn]]
+                n_seg <- nrow(segs)
+                value <- NA_real_
+                for (k in seq_len(n_seg)) {
+                  seg_from <- segs[["from"]][k]
+                  seg_to <- segs[["to"]][k]
+                  in_seg <- (t_val >= seg_from) &&
+                    (is.na(seg_to) || t_val < seg_to)
+                  # Match at or beyond the last segment start
+                  if (!in_seg && k == n_seg && t_val >= seg_from) {
+                    in_seg <- TRUE
+                  }
+                  if (in_seg) {
+                    lin <- segs[["method"]][["Linear"]]
+                    cf <- segs[["method"]][["CarryForward"]]
+                    if (!is.null(lin) && !is.na(lin[["intercept"]][k])) {
+                      value <- lin[["intercept"]][k] +
+                        lin[["slope"]][k] * (t_val - seg_from)
+                    } else if (!is.null(cf) && !is.na(cf[["value"]][k])) {
+                      value <- cf[["value"]][k]
+                    }
+                    break
+                  }
+                }
+                row_data[[cn]] <- value
+              }
+              list_idx <- list_idx + 1L
+              cov_list[[list_idx]] <- tibble::as_tibble(row_data)
+            }
+          }
+        }
+
+        if (list_idx > 0L) {
+          covs <- dplyr::bind_rows(cov_list[seq_len(list_idx)])
+          # Deduplicate: keep one row per unique covariate combination
+          # within each subject/block, retaining the earliest time
+          covs <- covs %>%
+            dplyr::group_by(id, block) %>%
+            dplyr::distinct(
+              dplyr::across(dplyr::all_of(cov_names)),
+              .keep_all = TRUE
+            ) %>%
+            dplyr::ungroup()
+        } else {
+          cli::cli_warn(c("!" = "No covariates found in JSON data."))
+          return(NULL)
+        }
+      } else if (inherits(data, "PM_cov")) {
         class(data$data) <- c("PM_cov_data", "data.frame")
         return(data$data)
       } else {
         cli::cli_warn(c(
           "!" = "Unable to generate covariate-posterior information.",
-          "i" = "{.file {file.path(path, 'covs.csv')}} does not exist, and result does not have valid {.code PM_cov} object."
+          "i" = "No covariate data and no valid {.code PM_cov} object."
         ))
         return(NULL)
       }
diff --git a/R/PM_cycle.R b/R/PM_cycle.R
index 99b9ccb39..b24888711 100755
--- a/R/PM_cycle.R
+++ b/R/PM_cycle.R
@@ -55,11 +55,10 @@ PM_cycle <- R6::R6Class(
     #' Creation of new `PM_cycle` object is automatic and not generally necessary
     #' for the user to do.
     #' @param PMdata include `r template("PMdata")`.
-    #' @param path include `r template("path")`.
+    #' @param json A parsed JSON list from `result.json`, as read by [PM_parse].
     #' @param ... Not currently used.
-    
-    initialize = function(PMdata = NULL, path = ".", ...) {
-      self$data <- private$make(PMdata, path )
+    initialize = function(PMdata = NULL, json = NULL, ...) {
+      self$data <- private$make(PMdata, json)
     },
     #' @description
     #' Plot method
@@ -119,59 +118,99 @@ PM_cycle <- R6::R6Class(
     }
   ), # end active
   private = list(
-    make = function(data, path) {
-      if (file.exists(file.path(path, "cycles.csv"))) {
-        raw <- readr::read_csv(file = file.path(path, "cycles.csv"), show_col_types = FALSE)
-        if (nrow(raw)==0){ # posterior 
+    make = function(data, json = NULL) {
+      # --- Obtain cycles data ---
+      if (!is.null(json) && !is.null(json$cyclelog)) {
+        cycles_raw <- json$cyclelog$cycles
+        # cycles_raw is a data.frame with columns: cycle, objf, error_models, theta, nspp, delta_objf, status
+        raw <- tibble::tibble(
+          cycle = cycles_raw$cycle,
+          neg2ll = -2 * cycles_raw$objf,
+          nspp = cycles_raw$nspp,
+          delta_objf = cycles_raw$delta_objf,
+          status = sapply(cycles_raw$status, function(s) {
+            if (is.list(s)) names(s)[1] else as.character(s)
+          })
+        )
+        # Extract gamlam values from error_models
+        # cycles_raw$error_models is a data.frame with column $models (list column)
+        gamlam_vals <- sapply(cycles_raw$error_models$models, function(models) {
+          sapply(models, function(m) {
+            if (is.list(m) && !is.null(names(m))) {
+              # m is e.g. list(Additive = list(lambda = ..., poly = ...))
+              model_type <- names(m)[1]
+              lam <- m[[model_type]]$lambda
+              if (is.list(lam)) lam[[1]] else lam
+            } else {
+              NA
+            }
+          })
+        })
+        # Extract per-parameter mean/sd/median from theta in each cycle
+        par_names <- json$settings$parameters$parameters$name
+        n_params <- length(par_names)
+        # cycles_raw$theta is a list column of matrices [nspp_i x npar]
+        cycle_stats <- lapply(cycles_raw$theta, function(theta_mat) {
+          # theta_mat is already a matrix after jsonlite simplification
+          if (is.null(theta_mat) || (is.matrix(theta_mat) && nrow(theta_mat) == 0)) return(NULL)
+          if (!is.matrix(theta_mat)) theta_mat <- matrix(theta_mat, ncol = n_params)
+          stats <- list()
+          for (j in seq_len(n_params)) {
+            vals <- theta_mat[, j]
+            stats[[paste0(par_names[j], ".mean")]] <- mean(vals)
+            stats[[paste0(par_names[j], ".sd")]] <- sd(vals)
+            stats[[paste0(par_names[j], ".median")]] <- median(vals)
+          }
+          as.data.frame(stats)
+        })
+        cycle_stats_df <- dplyr::bind_rows(cycle_stats)
+        raw <- dplyr::bind_cols(raw, cycle_stats_df)
+        # Add gamlam columns
+        if (is.matrix(gamlam_vals)) {
+          for (k in seq_len(nrow(gamlam_vals))) {
+            raw[[paste0("gamlam.", k - 1)]] <- gamlam_vals[k, ]
+          }
+        } else {
+          raw[["gamlam.0"]] <- gamlam_vals
+        }
+        if (nrow(raw) == 0) {
           raw <- data.frame(cycle = 0, status = "Posterior")
-          write.csv(raw, file.path(path, "cycles.csv"), row.names = FALSE)
         }
-      } else if (inherits(data, "PM_cycle") & !is.null(data$data)) { # file not there, and already PM_cycle
+      } else if (inherits(data, "PM_cycle") & !is.null(data$data)) {
         class(data$data) <- c("PM_cycle_data", "list")
         return(data$data)
       } else {
         cli::cli_warn(c(
           "!" = "Unable to generate cycle information.",
-          "i" = "{.file {file.path(path, 'cycles.csv')}} does not exist, and result does not have valid {.code PM_cycle} object."
+          "i" = "No JSON cyclelog and no valid {.code PM_cycle} object."
         ))
         return(NULL)
       }
       
-      
-      if (file.exists(file.path(path, "pred.csv"))) {
-        op_raw <- readr::read_csv(file = file.path(path, "pred.csv"), 
-        col_types = list(
-          time = readr::col_double(),
-          outeq = readr::col_integer(),
-          block = readr::col_integer(),
-          obs = readr::col_double(),
-          cens = readr::col_character(),
-          pop_mean = readr::col_double(),
-          pop_median = readr::col_double(),
-          post_mean = readr::col_double(),
-          post_median = readr::col_double()
-        ), show_col_types = FALSE) %>% filter(!is.na(obs))
-      } else if (inherits(data, "PM_cycle")) { # file not there, and already PM_op
+      # --- Obtain predictions data (for AIC/BIC n_subjects) ---
+      if (!is.null(json) && !is.null(json$predictions)) {
+        op_raw <- tibble::as_tibble(json$predictions$predictions) %>% filter(!is.na(obs))
+      } else if (inherits(data, "PM_cycle")) {
         class(data$data) <- c("PM_cycle_data", "list")
         return(data$data)
       } else {
         cli::cli_warn(c(
           "!" = "Unable to generate cycle information.",
-          "i" = "{.file {file.path(path, 'pred.csv')}} does not exist, and result does not have valid {.code PM_cycle} object."
+          "i" = "No JSON predictions and no valid {.code PM_cycle} object."
         ))
         return(NULL)
       }
       
-      
-      if (file.exists(file.path(path, "settings.json"))) {
-        config <- jsonlite::fromJSON(file.path(path, "settings.json"))
-      } else if (inherits(data, "PM_cycle") & !is.null(data$data)) { # file not there, and already PM_op
+      # --- Obtain settings/config ---
+      if (!is.null(json) && !is.null(json$settings)) {
+        config <- json$settings
+      } else if (inherits(data, "PM_cycle") & !is.null(data$data)) {
         class(data$data) <- c("PM_cycle_data", "list")
         return(data$data)
       } else {
         cli::cli_warn(c(
           "!" = "Unable to generate cycle information.",
-          "i" = "{.file {file.path(path, 'settings.json')}} does not exist, and result does not have valid {.code PM_cycle} object."
+          "i" = "No JSON settings and no valid {.code PM_cycle} object."
         ))
         return(NULL)
       }
diff --git a/R/PM_final.R b/R/PM_final.R
index b37c778af..bb277ccfc 100755
--- a/R/PM_final.R
+++ b/R/PM_final.R
@@ -80,10 +80,10 @@ PM_final <- R6::R6Class(
     #' Creation of new `PM_final` object is automatic and not generally necessary
     #' for the user to do.
     #' @param PMdata include `r template("PMdata")`.
-    #' @param path include `r template("path")`.
+    #' @param json A parsed JSON list from `result.json`, as read by [PM_parse].
     #' @param ... Not currently used.
-    initialize = function(PMdata = NULL, path = ".", ...) {
-      self$data <- private$make(PMdata, path)
+    initialize = function(PMdata = NULL, json = NULL, ...) {
+      self$data <- private$make(PMdata, json)
       class(self) <- c(c("NPAG", "IT2B")[1 + as.numeric(is.null(self$popPoints))], class(self))
     },
     #' @description
@@ -230,42 +230,64 @@ PM_final <- R6::R6Class(
     }
   ), # end active
   private = list(
-    make = function(data, path) {
-      if (file.exists(file.path(path, "theta.csv"))) {
-        theta <- readr::read_csv(file = file.path(path, "theta.csv"), show_col_types = FALSE)
-      } else if (inherits(data, "PM_final") & !is.null(data$data)) { # file not there, and already PM_final
+    make = function(data, json = NULL) {
+      # --- Obtain theta ---
+      if (!is.null(json) && !is.null(json$theta)) {
+        par_names <- json$settings$parameters$parameters$name
+        # json$theta is already a matrix [nspp x npar] after jsonlite simplification
+        theta_mat <- json$theta
+        colnames(theta_mat) <- par_names
+        theta <- tibble::as_tibble(theta_mat)
+        theta$prob <- json$w
+      } else if (inherits(data, "PM_final") & !is.null(data$data)) {
         class(data$data) <- c("PM_final_data", "list")
         return(data$data)
       } else {
         cli::cli_warn(c(
           "!" = "Unable to generate final cycle information.",
-          "i" = "{.file {file.path(path, 'theta.csv')}} does not exist, and result does not have valid {.code PM_final} object."
+          "i" = "No JSON theta and no valid {.code PM_final} object."
         ))
         return(NULL)
       }
       
-      if (file.exists(file.path(path, "posterior.csv"))) {
-        post <- readr::read_csv(file = file.path(path, "posterior.csv"), show_col_types = FALSE)
-      } else if (inherits(data, "PM_final") & !is.null(data$data)) { # file not there, and already PM_final
+      # --- Obtain posterior ---
+      if (!is.null(json) && !is.null(json$posterior)) {
+        par_names <- json$settings$parameters$parameters$name
+        # json$posterior is a matrix [nsubjects x nspp] after jsonlite simplification
+        # rows = subjects, columns = support points (probabilities)
+        # json$data$subjects is a data.frame with column $id
+        subject_ids <- json$data$subjects$id
+        post_list <- lapply(seq_len(nrow(json$posterior)), function(i) {
+          probs <- json$posterior[i, ]
+          df <- theta[, par_names, drop = FALSE]
+          df$prob <- probs
+          df$id <- subject_ids[i]
+          df$point <- seq_len(length(probs))
+          df
+        })
+        post <- dplyr::bind_rows(post_list) %>%
+          dplyr::relocate(id, point, dplyr::everything())
+      } else if (inherits(data, "PM_final") & !is.null(data$data)) {
         class(data$data) <- c("PM_final_data", "data.frame")
         return(data$data)
       } else {
         cli::cli_warn(c(
           "!" = "Unable to generate final cycle information.",
-          "i" = "{.file {file.path(path, 'posterior.csv')}} does not exist, and result does not have valid {.code PM_final} object."
+          "i" = "No JSON posterior and no valid {.code PM_final} object."
         ))
         return(NULL)
       }
       
-      if (file.exists(file.path(path, "settings.json"))) {
-        config <- jsonlite::fromJSON(file.path(path, "settings.json"))
-      } else if (inherits(data, "PM_final")) { # file not there, and already PM_final
+      # --- Obtain settings/config ---
+      if (!is.null(json) && !is.null(json$settings)) {
+        config <- json$settings
+      } else if (inherits(data, "PM_final")) {
         class(data$data) <- c("PM_final_data", "data.frame")
         return(data$data)
       } else {
         cli::cli_warn(c(
           "!" = "Unable to generate final cycle information.",
-          "i" = "{.file {file.path(path, 'settings.json')}} does not exist, and result does not have valid {.code PM_final} object."
+          "i" = "No JSON settings and no valid {.code PM_final} object."
         ))
         return(NULL)
       }
diff --git a/R/PM_op.R b/R/PM_op.R
index 654a43345..820ca0f19 100755
--- a/R/PM_op.R
+++ b/R/PM_op.R
@@ -74,10 +74,10 @@ PM_op <- R6::R6Class(
     #' Creation of new `PM_op` object is automatic at the end of a run and not generally necessary
     #' for the user to do.
     #' @param PMdata include `r template("PMdata")`.
-    #' @param path include `r template("path")`.
+    #' @param json A parsed JSON list from `result.json`, as read by [PM_parse].
     #' @param ... Not currently used.
-    initialize = function(PMdata = NULL, path = ".", ...) {
-      op <- private$make(PMdata, path)
+    initialize = function(PMdata = NULL, json = NULL, ...) {
+      op <- private$make(PMdata, json)
       self$data <- op
       if (length(op) > 1) { # all the objects were made
         self$id <- op$id
@@ -128,55 +128,51 @@ PM_op <- R6::R6Class(
   }
 ), # end public
 private = list(
-  make = function(data, path) {
-    if (file.exists(file.path(path, "pred.csv"))) {
-      op_raw <- readr::read_csv(file = file.path(path, "pred.csv"), 
-      col_types = list(
-        time = readr::col_double(),
-        outeq = readr::col_integer(),
-        block = readr::col_integer(),
-        obs = readr::col_double(),
-        cens = readr::col_character(),
-        pop_mean = readr::col_double(),
-        pop_median = readr::col_double(),
-        post_mean = readr::col_double(),
-        post_median = readr::col_double()
-      ), show_col_types = FALSE) %>% filter(!is.na(obs))
-      
-      if(!"cens" %in% names(op_raw)){
-        op_raw <- op_raw %>% mutate(cens = "none") # if cens column missing, assume all observed
-      } 
-      
-      
-    } else if (inherits(data, "PM_op") & !is.null(data$data)) { # file not there, and already PM_op
+  make = function(data, json = NULL) {
+    # --- Obtain predictions data ---
+    if (!is.null(json) && !is.null(json$predictions)) {
+      op_raw <- tibble::as_tibble(json$predictions$predictions) %>% filter(!is.na(obs))
+      if (!"cens" %in% names(op_raw)) {
+        op_raw <- op_raw %>% mutate(cens = "none")
+      }
+    } else if (inherits(data, "PM_op") & !is.null(data$data)) {
       if(!"cens" %in% names(data$data)){
-        data$data <- data$data %>% mutate(cens = "none") # if cens column missing, assume all observed
+        data$data <- data$data %>% mutate(cens = "none")
       } 
       class(data$data) <- c("PM_op_data", "data.frame")
       return(data$data)
     } else {
       cli::cli_warn(c(
         "!" = "Unable to generate obs-pred information.",
-        "i" = "{.file {file.path(path, 'pred.csv')}} does not exist, and result does not have valid {.code PM_op} object ."
+        "i" = "No JSON predictions and no valid {.code PM_op} object."
       ))
       return(NULL)
     }
     
-    
-    
-    if (file.exists(file.path(path, "settings.json"))) {
-      config <- jsonlite::fromJSON(file.path(path, "settings.json"))
-    } else if (inherits(data, "PM_op")) { # file not there, and already PM_op
+    # --- Obtain settings/config ---
+    if (!is.null(json) && !is.null(json$settings)) {
+      config <- json$settings
+    } else if (inherits(data, "PM_op")) {
       class(data$data) <- c("PM_op_data", "data.frame")
       return(data$data)
     } else {
       cli::cli_warn(c(
         "!" = "Unable to generate obs-pred information.",
-        "i" = "{.file {file.path(path, 'settings.json')}} does not exist, and result does not have valid {.code PM_op} object."
+        "i" = "No JSON settings and no valid {.code PM_op} object."
       ))
       return(NULL)
     }
-    poly <- map(config$errormodels$models, \(x) x %>% pluck("poly")) %>% bind_rows()
+    # config$errormodels$models is a list: each element is either
+    # list(Additive = list(lambda=..., poly=list(c0=,c1=,c2=,c3=))) or "None"
+    poly <- map(config$errormodels$models, function(m) {
+      if (is.list(m)) {
+        model_type <- names(m)[1]
+        p <- m[[model_type]]$poly
+        if (!is.null(p)) as.data.frame(p) else data.frame(c0 = 0, c1 = 0, c2 = 0, c3 = 0)
+      } else {
+        data.frame(c0 = 0, c1 = 0, c2 = 0, c3 = 0)
+      }
+    }) %>% bind_rows()
     op <- op_raw %>%
     # left_join(pred_raw, by = c("id", "time", "outeq")) %>%
     pivot_longer(cols = c(pop_mean, pop_median, post_mean, post_median)) %>%
diff --git a/R/PM_parse.R b/R/PM_parse.R
index 5b437d1c2..90a73ae8d 100755
--- a/R/PM_parse.R
+++ b/R/PM_parse.R
@@ -7,8 +7,11 @@
 #'
 #' A flexible parser for Pmetrics output
 #' @details
-#' Currently written for the Rust implementation of NPAG
-#' @param path The directory containing the output from the Rust-implementation of NPAG
+#' Currently written for the Rust implementation of NPAG.
+#' Reads a single `result.json` file and passes the parsed JSON
+#' to each component constructor.
+#' @param path The directory containing the `result.json` output from the Rust-implementation of NPAG
+#' @param result_file The name of the result JSON file. Default is "result.json".
 #' @param fit The relative path to a "fit.rds" file, which is normal output after a fit containing the data and model used.
 #' @param write If `TRUE` (default), saves the output as "PMout.Rdata" in the specified path.
 #' @return The output of `PM_parse` is a list containing the following elements
@@ -22,7 +25,7 @@
 #' @export
 #' @keywords internal 
 
-PM_parse <- function(path = ".", fit = "fit.rds", write = TRUE) {
+PM_parse <- function(path = ".", result_file = "result.json", fit = "fit.rds", write = TRUE) {
  
   if (is.character(fit) && file.exists(file.path(path, "../inputs", fit))) {
     # fit is a character string pointing to a file, load it
@@ -32,87 +35,86 @@ PM_parse <- function(path = ".", fit = "fit.rds", write = TRUE) {
     fit_object <- NULL
   }
 
-  
   if (!dir.exists(path)) {
     cli::cli_abort(c("x" = "The directory {.path {path}} does not exist."))
-}
+  }
 
-# assumes pred.csv and settings.json are in wd
-op <- rlang::try_fetch(PM_op$new(path = path),
-error = function(e) {
-  cli::cli_warn("Unable to create {.cls PM_op} object", parent = e)
-  return(NULL)
-}
-)
+  # Read the single result JSON file
+  result_path <- file.path(path, result_file)
+  if (!file.exists(result_path)) {
+    cli::cli_abort(c("x" = "The result file {.file {result_path}} does not exist."))
+  }
+  result_json <- jsonlite::fromJSON(
+    suppressWarnings(readLines(result_path, warn = FALSE)),
+    simplifyVector = TRUE,
+    simplifyDataFrame = TRUE
+  )
 
-# assumes theta.csv and posterior.csv are in wd
-final <- rlang::try_fetch(PM_final$new(path = path),
-error = function(e) {
-  cli::cli_warn("Unable to create {.cls PM_final} object", parent = e)
-  return(NULL)
-}
-)
+  # Pass parsed JSON to each constructor
+  op <- rlang::try_fetch(PM_op$new(json = result_json),
+    error = function(e) {
+      cli::cli_warn("Unable to create {.cls PM_op} object", parent = e)
+      return(NULL)
+    }
+  )
 
-# assumes cycles.csv, and settings.json are in wd
-cycle <- rlang::try_fetch(PM_cycle$new(path = path),
-error = function(e) {
-  cli::cli_warn("Unable to create {.cls PM_cycle} object", parent = e)
-  return(NULL)
-}
-)
+  final <- rlang::try_fetch(PM_final$new(json = result_json),
+    error = function(e) {
+      cli::cli_warn("Unable to create {.cls PM_final} object", parent = e)
+      return(NULL)
+    }
+  )
 
-# assumes pred.csv is in wd
-pop <- rlang::try_fetch(PM_pop$new(path = path),
-error = function(e) {
-  cli::cli_warn("Unable to create {.cls PM_pop} object", parent = e)
-  return(NULL)
-}
-)
+  cycle <- rlang::try_fetch(PM_cycle$new(json = result_json),
+    error = function(e) {
+      cli::cli_warn("Unable to create {.cls PM_cycle} object", parent = e)
+      return(NULL)
+    }
+  )
 
-# assumes pred.csv is in wd
-post <- rlang::try_fetch(PM_post$new(path = path),
-error = function(e) {
-  cli::cli_warn("Unable to create {.cls PM_post} object", parent = e)
-  return(NULL)
-}
-)
+  pop <- rlang::try_fetch(PM_pop$new(json = result_json),
+    error = function(e) {
+      cli::cli_warn("Unable to create {.cls PM_pop} object", parent = e)
+      return(NULL)
+    }
+  )
 
-cov <- rlang::try_fetch(PM_cov$new(path = path),
-error = function(e) {
-  cli::cli_warn("Unable to create {.cls PM_cov} object", parent = e)
-  return(NULL)
-}
-)
+  post <- rlang::try_fetch(PM_post$new(json = result_json),
+    error = function(e) {
+      cli::cli_warn("Unable to create {.cls PM_post} object", parent = e)
+      return(NULL)
+    }
+  )
 
-core <- list(
-  data = fit_object$data,
-  model = fit_object$model,
-  op = op,
-  cov = cov,
-  post = post,
-  pop = pop,
-  cycle = cycle,
-  final = final,
-  backend = "rust",
-  algorithm = "NPAG",
-  numeqt = 1,
-  converge = cycle$data$converged,
-  config = rlang::try_fetch(jsonlite::fromJSON(suppressWarnings(readLines(file.path(path, "settings.json"), warn = FALSE))),
-  sys = as.list(Sys.info()) %>% keep(names(.) %in% c("sysname", "machine")) %>% paste(collapse = " "),
-  
-  error = function(e) {
-    cli::cli_warn(c("!" = "Unable to read {.file {file.path(path, 'settings.json')}}"))
-    return(NULL)
-  }
-)
-)
+  cov <- rlang::try_fetch(PM_cov$new(json = result_json),
+    error = function(e) {
+      cli::cli_warn("Unable to create {.cls PM_cov} object", parent = e)
+      return(NULL)
+    }
+  )
 
-class(core) <- "PM_result"
+  core <- list(
+    data = fit_object$data,
+    model = fit_object$model,
+    op = op,
+    cov = cov,
+    post = post,
+    pop = pop,
+    cycle = cycle,
+    final = final,
+    backend = "rust",
+    algorithm = "NPAG",
+    numeqt = 1,
+    converge = cycle$data$converged,
+    config = result_json$settings,
+    sys = as.list(Sys.info()) %>% keep(names(.) %in% c("sysname", "machine")) %>% paste(collapse = " ")
+  )
 
+  class(core) <- "PM_result"
 
-if (write) {
-  save(core, file = file.path(path, "PMout.Rdata"))
-}
+  if (write) {
+    save(core, file = file.path(path, "PMout.Rdata"))
+  }
 
-return(invisible(core))
+  return(invisible(core))
 }
diff --git a/R/PM_pop.R b/R/PM_pop.R
index 225598359..69988221e 100755
--- a/R/PM_pop.R
+++ b/R/PM_pop.R
@@ -54,10 +54,10 @@ PM_pop <- R6::R6Class(
     #' Creation of new `PM_pop` object is automatic and not generally necessary
     #' for the user to do.
     #' @param PMdata include `r template("PMdata")`.
-    #' @param path include `r template("path")`.
+    #' @param json A parsed JSON list from `result.json`, as read by [PM_parse].
     #' @param ... Not currently used.
-    initialize = function(PMdata = NULL, path = ".", ...) {
-      self$data <- private$make(PMdata, path)
+    initialize = function(PMdata = NULL, json = NULL, ...) {
+      self$data <- private$make(PMdata, json)
     },
     #' @description
     #' Plot method
@@ -91,27 +91,17 @@ PM_pop <- R6::R6Class(
   }
 ), # end public
 private = list(
-  make = function(data, path) {
-    if (file.exists(file.path(path, "pred.csv"))) {
-      op_raw <- readr::read_csv(file = file.path(path, "pred.csv"), 
-      col_types = list(
-        time = readr::col_double(),
-        outeq = readr::col_integer(),
-        block = readr::col_integer(),
-        obs = readr::col_double(),
-        cens = readr::col_character(),
-        pop_mean = readr::col_double(),
-        pop_median = readr::col_double(),
-        post_mean = readr::col_double(),
-        post_median = readr::col_double()
-      ), show_col_types = FALSE) 
-    } else if (inherits(data, "PM_pop") & !is.null(data$data)) { # file not there, and already PM_pop
+  make = function(data, json = NULL) {
+    # --- Obtain predictions data ---
+    if (!is.null(json) && !is.null(json$predictions)) {
+      op_raw <- tibble::as_tibble(json$predictions$predictions)
+    } else if (inherits(data, "PM_pop") & !is.null(data$data)) {
       class(data$data) <- c("PM_pop_data", "data.frame")
       return(data$data)
     } else {
       cli::cli_warn(c(
         "!" = "Unable to generate pop pred information.",
-        "i" = "{.file {file.path(path, 'pred.csv')}} does not exist, and result does not have valid {.code PM_pop} object."
+        "i" = "No JSON predictions and no valid {.code PM_pop} object."
       ))
       return(NULL)
     }
diff --git a/R/PM_post.R b/R/PM_post.R
index 46cd9d722..5c90605e6 100755
--- a/R/PM_post.R
+++ b/R/PM_post.R
@@ -55,10 +55,10 @@ PM_post <- R6::R6Class(
     #' Creation of new `PM_post` object is automatic and not generally necessary
     #' for the user to do.
     #' @param PMdata include `r template("PMdata")`.
-    #' @param path include `r template("path")`.
+    #' @param json A parsed JSON list from `result.json`, as read by [PM_parse].
     #' @param ... Not currently used.
-    initialize = function(PMdata = NULL, path = ".", ...) {
-      self$data <- private$make(PMdata, path)
+    initialize = function(PMdata = NULL, json = NULL, ...) {
+      self$data <- private$make(PMdata, json)
     },
     #' @description
     #' Plot method
@@ -92,27 +92,17 @@ PM_post <- R6::R6Class(
   }
 ), # end public
 private = list(
-  make = function(data, path) {
-    if (file.exists(file.path(path, "pred.csv"))) {
-      op_raw <- readr::read_csv(file = file.path(path, "pred.csv"), 
-      col_types = list(
-        time = readr::col_double(),
-        outeq = readr::col_integer(),
-        block = readr::col_integer(),
-        obs = readr::col_double(),
-        cens = readr::col_character(),
-        pop_mean = readr::col_double(),
-        pop_median = readr::col_double(),
-        post_mean = readr::col_double(),
-        post_median = readr::col_double()
-      ), show_col_types = FALSE) 
-    } else if (inherits(data, "PM_post") & !is.null(data$data)) { # file not there, and already PM_post
+  make = function(data, json = NULL) {
+    # --- Obtain predictions data ---
+    if (!is.null(json) && !is.null(json$predictions)) {
+      op_raw <- tibble::as_tibble(json$predictions$predictions)
+    } else if (inherits(data, "PM_post") & !is.null(data$data)) {
       class(data$data) <- c("PM_post_data", "data.frame")
       return(data$data)
     } else {
       cli::cli_warn(c(
         "!" = "Unable to generate post pred information.",
-        "i" = "{.file {file.path(path, 'pred.csv')}} does not exist, and result does not have valid {.code PM_post} object."
+        "i" = "No JSON predictions and no valid {.code PM_post} object."
       ))
       return(NULL)
     }
diff --git a/man/PM_cov.Rd b/man/PM_cov.Rd
index 62fc5a492..69fa8cb87 100755
--- a/man/PM_cov.Rd
+++ b/man/PM_cov.Rd
@@ -69,7 +69,7 @@ parameter means and medians
 \subsection{Method \code{new()}}{
 Create new object populated with covariate-parameter information
 \subsection{Usage}{
-\if{html}{\out{<div class="r">}}\preformatted{PM_cov$new(PMdata = NULL, path = ".", ...)}\if{html}{\out{</div>}}
+\if{html}{\out{<div class="r">}}\preformatted{PM_cov$new(PMdata = NULL, json = NULL, ...)}\if{html}{\out{</div>}}
 }
 
 \subsection{Arguments}{
@@ -78,8 +78,7 @@ Create new object populated with covariate-parameter information
 \item{\code{PMdata}}{include Saved, parsed output of prior run, used when source files are not available.
 .}
 
-\item{\code{path}}{include Path to the folder containing the raw results of the run. Default is the current working directory.
-.}
+\item{\code{json}}{A parsed JSON list from \code{result.json}, as read by \link{PM_parse}.}
 
 \item{\code{...}}{Not currently used.}
 }
diff --git a/man/PM_cycle.Rd b/man/PM_cycle.Rd
index 41f82a34d..b41f3e2c4 100755
--- a/man/PM_cycle.Rd
+++ b/man/PM_cycle.Rd
@@ -87,7 +87,7 @@ at each cycle,  normalized to initial standard deviation}
 \subsection{Method \code{new()}}{
 Create new object populated with  cycle information
 \subsection{Usage}{
-\if{html}{\out{<div class="r">}}\preformatted{PM_cycle$new(PMdata = NULL, path = ".", ...)}\if{html}{\out{</div>}}
+\if{html}{\out{<div class="r">}}\preformatted{PM_cycle$new(PMdata = NULL, json = NULL, ...)}\if{html}{\out{</div>}}
 }
 
 \subsection{Arguments}{
@@ -96,8 +96,7 @@ Create new object populated with  cycle information
 \item{\code{PMdata}}{include Saved, parsed output of prior run, used when source files are not available.
 .}
 
-\item{\code{path}}{include Path to the folder containing the raw results of the run. Default is the current working directory.
-.}
+\item{\code{json}}{A parsed JSON list from \code{result.json}, as read by \link{PM_parse}.}
 
 \item{\code{...}}{Not currently used.}
 }
diff --git a/man/PM_final.Rd b/man/PM_final.Rd
index 2737b57e9..10ee45714 100755
--- a/man/PM_final.Rd
+++ b/man/PM_final.Rd
@@ -180,7 +180,7 @@ with shrinkage to assess the information content of the data.}
 \subsection{Method \code{new()}}{
 Create new object populated with final cycle information
 \subsection{Usage}{
-\if{html}{\out{<div class="r">}}\preformatted{PM_final$new(PMdata = NULL, path = ".", ...)}\if{html}{\out{</div>}}
+\if{html}{\out{<div class="r">}}\preformatted{PM_final$new(PMdata = NULL, json = NULL, ...)}\if{html}{\out{</div>}}
 }
 
 \subsection{Arguments}{
@@ -189,8 +189,7 @@ Create new object populated with final cycle information
 \item{\code{PMdata}}{include Saved, parsed output of prior run, used when source files are not available.
 .}
 
-\item{\code{path}}{include Path to the folder containing the raw results of the run. Default is the current working directory.
-.}
+\item{\code{json}}{A parsed JSON list from \code{result.json}, as read by \link{PM_parse}.}
 
 \item{\code{...}}{Not currently used.}
 }
diff --git a/man/PM_op.Rd b/man/PM_op.Rd
index e8d346815..277e6fdb8 100755
--- a/man/PM_op.Rd
+++ b/man/PM_op.Rd
@@ -85,7 +85,7 @@ or individual predictions based on Bayesian posterior parameter value distributi
 \subsection{Method \code{new()}}{
 Create new object populated with observed vs. predicted data
 \subsection{Usage}{
-\if{html}{\out{<div class="r">}}\preformatted{PM_op$new(PMdata = NULL, path = ".", ...)}\if{html}{\out{</div>}}
+\if{html}{\out{<div class="r">}}\preformatted{PM_op$new(PMdata = NULL, json = NULL, ...)}\if{html}{\out{</div>}}
 }
 
 \subsection{Arguments}{
@@ -94,8 +94,7 @@ Create new object populated with observed vs. predicted data
 \item{\code{PMdata}}{include Saved, parsed output of prior run, used when source files are not available.
 .}
 
-\item{\code{path}}{include Path to the folder containing the raw results of the run. Default is the current working directory.
-.}
+\item{\code{json}}{A parsed JSON list from \code{result.json}, as read by \link{PM_parse}.}
 
 \item{\code{...}}{Not currently used.}
 }
diff --git a/man/PM_parse.Rd b/man/PM_parse.Rd
index d5922d719..ee9bdfd50 100644
--- a/man/PM_parse.Rd
+++ b/man/PM_parse.Rd
@@ -4,10 +4,17 @@
 \alias{PM_parse}
 \title{Parse Pmetrics output}
 \usage{
-PM_parse(path = ".", fit = "fit.rds", write = TRUE)
+PM_parse(
+  path = ".",
+  result_file = "result.json",
+  fit = "fit.rds",
+  write = TRUE
+)
 }
 \arguments{
-\item{path}{The directory containing the output from the Rust-implementation of NPAG}
+\item{path}{The directory containing the \code{result.json} output from the Rust-implementation of NPAG}
+
+\item{result_file}{The name of the result JSON file. Default is "result.json".}
 
 \item{fit}{The relative path to a "fit.rds" file, which is normal output after a fit containing the data and model used.}
 
@@ -30,7 +37,9 @@ The output of \code{PM_parse} is a list containing the following elements
 A flexible parser for Pmetrics output
 }
 \details{
-Currently written for the Rust implementation of NPAG
+Currently written for the Rust implementation of NPAG.
+Reads a single \code{result.json} file and passes the parsed JSON
+to each component constructor.
 }
 \author{
 Michael Neely and Markus Hovd
diff --git a/man/PM_pop.Rd b/man/PM_pop.Rd
index 1473b3f9d..42266ca88 100755
--- a/man/PM_pop.Rd
+++ b/man/PM_pop.Rd
@@ -68,7 +68,7 @@ Michael Neely, Julian Otalvaro
 Create new object populated with population predicted data at
 regular, frequent intervals
 \subsection{Usage}{
-\if{html}{\out{<div class="r">}}\preformatted{PM_pop$new(PMdata = NULL, path = ".", ...)}\if{html}{\out{</div>}}
+\if{html}{\out{<div class="r">}}\preformatted{PM_pop$new(PMdata = NULL, json = NULL, ...)}\if{html}{\out{</div>}}
 }
 
 \subsection{Arguments}{
@@ -77,8 +77,7 @@ regular, frequent intervals
 \item{\code{PMdata}}{include Saved, parsed output of prior run, used when source files are not available.
 .}
 
-\item{\code{path}}{include Path to the folder containing the raw results of the run. Default is the current working directory.
-.}
+\item{\code{json}}{A parsed JSON list from \code{result.json}, as read by \link{PM_parse}.}
 
 \item{\code{...}}{Not currently used.}
 }
diff --git a/man/PM_post.Rd b/man/PM_post.Rd
index 0bfe4f85f..9eacbc38c 100755
--- a/man/PM_post.Rd
+++ b/man/PM_post.Rd
@@ -68,7 +68,7 @@ Michael Neely, Julian Otalvaro
 Create new object populated with Bayesian posterior predicted data at
 regular, frequent intervals
 \subsection{Usage}{
-\if{html}{\out{<div class="r">}}\preformatted{PM_post$new(PMdata = NULL, path = ".", ...)}\if{html}{\out{</div>}}
+\if{html}{\out{<div class="r">}}\preformatted{PM_post$new(PMdata = NULL, json = NULL, ...)}\if{html}{\out{</div>}}
 }
 
 \subsection{Arguments}{
@@ -77,8 +77,7 @@ regular, frequent intervals
 \item{\code{PMdata}}{include Saved, parsed output of prior run, used when source files are not available.
 .}
 
-\item{\code{path}}{include Path to the folder containing the raw results of the run. Default is the current working directory.
-.}
+\item{\code{json}}{A parsed JSON list from \code{result.json}, as read by \link{PM_parse}.}
 
 \item{\code{...}}{Not currently used.}
 }
diff --git a/result.json b/result.json
new file mode 100644
index 000000000..41beeecaa
--- /dev/null
+++ b/result.json
@@ -0,0 +1,186729 @@
+{
+  "data": {
+    "subjects": [
+      {
+        "id": "1",
+        "occasions": [
+          {
+            "events": [
+              {
+                "Infusion": {
+                  "time": -48.0,
+                  "amount": 500.0,
+                  "input": 0,
+                  "duration": 0.5,
+                  "occasion": 0
+                }
+              },
+              {
+                "Infusion": {
+                  "time": 0.0,
+                  "amount": 500.0,
+                  "input": 0,
+                  "duration": 0.5,
+                  "occasion": 0
+                }
+              },
+              {
+                "Observation": {
+                  "time": 0.5,
+                  "value": 3.337169,
+                  "outeq": 0,
+                  "errorpoly": {
+                    "c0": 0.0,
+                    "c1": 0.1,
+                    "c2": 0.0,
+                    "c3": 0.0
+                  },
+                  "occasion": 0,
+                  "censoring": "none"
+                }
+              },
+              {
+                "Observation": {
+                  "time": 1.0,
+                  "value": 3.475056,
+                  "outeq": 0,
+                  "errorpoly": {
+                    "c0": 0.0,
+                    "c1": 0.1,
+                    "c2": 0.0,
+                    "c3": 0.0
+                  },
+                  "occasion": 0,
+                  "censoring": "none"
+                }
+              },
+              {
+                "Observation": {
+                  "time": 2.0,
+                  "value": 2.621165,
+                  "outeq": 0,
+                  "errorpoly": {
+                    "c0": 0.0,
+                    "c1": 0.1,
+                    "c2": 0.0,
+                    "c3": 0.0
+                  },
+                  "occasion": 0,
+                  "censoring": "none"
+                }
+              },
+              {
+                "Observation": {
+                  "time": 3.0,
+                  "value": 1.910384,
+                  "outeq": 0,
+                  "errorpoly": {
+                    "c0": 0.0,
+                    "c1": 0.1,
+                    "c2": 0.0,
+                    "c3": 0.0
+                  },
+                  "occasion": 0,
+                  "censoring": "none"
+                }
+              },
+              {
+                "Observation": {
+                  "time": 4.0,
+                  "value": 1.425661,
+                  "outeq": 0,
+                  "errorpoly": {
+                    "c0": 0.0,
+                    "c1": 0.1,
+                    "c2": 0.0,
+                    "c3": 0.0
+                  },
+                  "occasion": 0,
+                  "censoring": "none"
+                }
+              },
+              {
+                "Observation": {
+                  "time": 6.0,
+                  "value": 0.8007754,
+                  "outeq": 0,
+                  "errorpoly": {
+                    "c0": 0.0,
+                    "c1": 0.1,
+                    "c2": 0.0,
+                    "c3": 0.0
+                  },
+                  "occasion": 0,
+                  "censoring": "none"
+                }
+              },
+              {
+                "Observation": {
+                  "time": 8.0,
+                  "value": 0.352914,
+                  "outeq": 0,
+                  "errorpoly": {
+                    "c0": 0.0,
+                    "c1": 0.1,
+                    "c2": 0.0,
+                    "c3": 0.0
+                  },
+                  "occasion": 0,
+                  "censoring": "none"
+                }
+              },
+              {
+                "Observation": {
+                  "time": 12.0,
+                  "value": 0.1020168,
+                  "outeq": 0,
+                  "errorpoly": {
+                    "c0": 0.0,
+                    "c1": 0.1,
+                    "c2": 0.0,
+                    "c3": 0.0
+                  },
+                  "occasion": 0,
+                  "censoring": "none"
+                }
+              },
+              {
+                "Observation": {
+                  "time": 18.0,
+                  "value": 0.01511893,
+                  "outeq": 0,
+                  "errorpoly": {
+                    "c0": 0.0,
+                    "c1": 0.1,
+                    "c2": 0.0,
+                    "c3": 0.0
+                  },
+                  "occasion": 0,
+                  "censoring": "none"
+                }
+              },
+              {
+                "Observation": {
+                  "time": 24.0,
+                  "value": 0.002599922,
+                  "outeq": 0,
+                  "errorpoly": {
+                    "c0": 0.0,
+                    "c1": 0.1,
+                    "c2": 0.0,
+                    "c3": 0.0
+                  },
+                  "occasion": 0,
+                  "censoring": "none"
+                }
+              }
+            ],
+            "covariates": {
+              "covariates": {}
+            },
+            "index": 0
+          }
+        ]
+      },
+      {
+        "id": "10",
+        "occasions": [
+          {
+            "events": [
+              {
+                "Infusion": {
+                  "time": -48.0,
+                  "amount": 500.0,
+                  "input": 0,
+                  "duration": 0.5,
+                  "occasion": 0
+                }
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+              {
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