Fix/do conversions run input typo

base/workflow/R/do_conversions.R

@@ -28,7 +28,7 @@ do_conversions <- function(settings, overwrite.met = FALSE, overwrite.fia = FALS
       next
     }
 
-    input.tag <- names(settings$run$input)[i]
+    input.tag <- names(settings$run$inputs)[i]
     PEcAn.logger::logger.info("PROCESSING: ",input.tag)
 
     # Check for existing file paths , skips if the file exists

modules/data.land/R/soil_process.R

@@ -62,10 +62,10 @@ soil_process <- function(settings, input, dbfiles, overwrite = FALSE, run.local
 
     if (length(newfile) == 0) {
       # No files exist yet — extract from gSSURGO
-      radius <- ifelse(is.null(settings$run$input$soil$radius), 100,
-                       as.numeric(settings$run$input$soil$radius))
-      grid_size <- max(3, ifelse(is.null(settings$run$input$soil$grid_size), 3,
-                                 as.numeric(settings$run$input$soil$grid_size)))
+      radius <- ifelse(is.null(settings$run$inputs$soil$radius), 100,
+                       as.numeric(settings$run$inputs$soil$radius))
+      grid_size <- max(3, ifelse(is.null(settings$run$inputs$soil$grid_size), 3,
+                                 as.numeric(settings$run$inputs$soil$grid_size)))
 
       grid_extent  <- radius * sqrt(pi)
       grid_spacing <- grid_extent / (grid_size - 1)

modules/meta.analysis/R/run.meta.analysis.R

@@ -32,13 +32,18 @@ meta_analysis_standalone <- function(
   random = TRUE,
   threshold = 1.2,
   use_ghs = TRUE,
-  gamma_tau = 0.01
+  prior_n  = 0.02,
+  prior_cv = sqrt(0.001),
+  tauA     = NULL,
+  tauB     = NULL
 ) {
   stopifnot(
     is.list(trait_data),
     is.logical(use_ghs),
-    is.numeric(gamma_tau),
-    gamma_tau > 0
+    is.numeric(prior_n),  prior_n > 0,
+    is.numeric(prior_cv), prior_cv > 0,
+    is.null(tauA) || (is.numeric(tauA) && tauA > 0),
+    is.null(tauB) || is.numeric(tauB)
   )
 
   # Create output directory if it doesn't already exist
@@ -101,14 +106,19 @@ meta_analysis_standalone <- function(
   )
 
   ## Set gamma distribution prior
+  resolved_tauA <- if (!is.null(tauA)) tauA else prior_n / 2
+
   prior_variances <- as.data.frame(rep(1, nrow(priors)))
   row.names(prior_variances) <- row.names(priors)
-  prior_variances[names(trait_average), ] <- 0.001 * trait_average ^ 2
-  prior_variances["seedling_mortality", 1] <- 1
-  taupriors <- list(
-    tauA = gamma_tau,
-    tauB = apply(prior_variances, 1, function(x) min(gamma_tau, x))
-  )
+  prior_variances[names(trait_average), ] <- prior_cv ^ 2 * trait_average ^ 2
+
+  resolved_tauB <- if (!is.null(tauB)) {
+    tauB
+  } else {
+    apply(prior_variances, 1, function(x) resolved_tauA * x)
+  }
+
+  taupriors <- list(tauA = resolved_tauA, tauB = resolved_tauB)
 
   ### Run the meta-analysis
   trait_mcmc <- pecan.ma(jagged_data,

scripts/workflow.bm.R

@@ -100,7 +100,7 @@ for (i in seq_along(settings$run$inputs)) {
     next
   }
 
-  input.tag <- names(settings$run$input)[i]
+  input.tag <- names(settings$run$inputs)[i]
 
   # fia database
   if ((input["input"] == "fia") && (status.check("FIA2ED") == 0)) {

scripts/workflow.pda.R

@@ -63,7 +63,7 @@ if (length(which(commandArgs() == "--continue")) == 0) {
       next
     }
 
-    input.tag <- names(settings$run$input)[i]
+    input.tag <- names(settings$run$inputs)[i]
 
     # fia database
     if (input["input"] == "fia") {