diff --git a/R/fimsframe.R b/R/fimsframe.R
index 85535ba60..0efd4c0ae 100644
--- a/R/fimsframe.R
+++ b/R/fimsframe.R
@@ -16,7 +16,7 @@ setClass(
     data = "data.frame", # can use c( ) or list here.
     fleets = "numeric",
     nyrs = "integer",
-    start_year = "integer", 
+    start_year = "integer",
     end_year = "integer"
   )
 )
@@ -260,7 +260,7 @@ setValidity(
     if (!"dateend" %in% colnames(object@data)) {
       errors <- c(errors, "data must contain 'uncertainty'")
     }
-      
+
 
     # TODO: Add checks for other slots
 
@@ -327,7 +327,7 @@ FIMSFrame <- function(data) {
     data = data,
     fleets = fleets,
     nyrs = nyrs,
-    start_year = start_year, 
+    start_year = start_year,
     end_year = end_year
   )
   return(out)
@@ -357,7 +357,7 @@ FIMSFrameAge <- function(data) {
     data = data,
     fleets = fleets,
     nyrs = nyrs,
-    start_year = start_year, 
+    start_year = start_year,
     end_year = end_year,
     ages = ages,
     nages = nages,
diff --git a/tests/concurrent/fims_concurrent_mpi.R b/tests/concurrent/fims_concurrent_mpi.R
index 2ae702ed9..bd15bf293 100644
--- a/tests/concurrent/fims_concurrent_mpi.R
+++ b/tests/concurrent/fims_concurrent_mpi.R
@@ -3,21 +3,21 @@ library(minimizR)
 
 # Load the R MPI package if it is not already loaded.
 if (!is.loaded("mpi_initialize")) {
-    library("Rmpi")
+  library("Rmpi")
 }
 
 #
 # In case R exits unexpectedly, have it automatically clean up
 # resources taken up by Rmpi (slaves, memory, etc...)
 .Last <- function() {
-    if (is.loaded("mpi_initialize")) {
-        if (mpi.comm.size(1) > 0) {
-            print("Please use mpi.close.Rslaves() to close slaves.")
-            mpi.close.Rslaves()
-        }
-        print("Please use mpi.quit() to quit R")
-        .Call("mpi_finalize")
+  if (is.loaded("mpi_initialize")) {
+    if (mpi.comm.size(1) > 0) {
+      print("Please use mpi.close.Rslaves() to close slaves.")
+      mpi.close.Rslaves()
     }
+    print("Please use mpi.quit() to quit R")
+    .Call("mpi_finalize")
+  }
 }
 
 
@@ -27,14 +27,14 @@ working_dir <- getwd()
 maindir <- tempdir()
 model_input <- ASSAMC::save_initial_input()
 FIMS_C0_estimation <- ASSAMC::save_initial_input(
-base_case = TRUE,
-input_list = model_input,
-maindir = maindir,
-om_sim_num = 1,
-keep_sim_num = 1,
-figure_number = 1,
-seed_num = 9924,
-case_name = "FIMS_C0_estimation"
+  base_case = TRUE,
+  input_list = model_input,
+  maindir = maindir,
+  om_sim_num = 1,
+  keep_sim_num = 1,
+  figure_number = 1,
+  seed_num = 9924,
+  case_name = "FIMS_C0_estimation"
 )
 
 ASSAMC::run_om(input_list = FIMS_C0_estimation)
@@ -45,166 +45,166 @@ setwd(working_dir)
 
 
 
-NUMBER_OF_MODEL_RUNS<-100
+NUMBER_OF_MODEL_RUNS <- 100
 
 
 # Fix and change R0 randomly to get different output for each
 # model run.
-init_fims<-function(i){
-    fims <- Rcpp::Module("fims", PACKAGE = "FIMS")
-    
-    # Recruitment
-    recruitment <- new(fims$BevertonHoltRecruitment)
-    # logR_sd is NOT logged. It needs to enter the model logged b/c the exp() is taken
-    # before the likelihood calculation
-    recruitment$log_sigma_recruit$value <- log(om_input$logR_sd)
-    recruitment$log_rzero$value <- log(om_input$R0 + runif(1,min=0, max=1000))
-    recruitment$log_rzero$is_random_effect <- FALSE
-    recruitment$log_rzero$estimated <- FALSE
-    recruitment$logit_steep$value <- -log(1.0 - om_input$h) + log(om_input$h - 0.2)
-    recruitment$logit_steep$is_random_effect <- FALSE
-    recruitment$logit_steep$estimated <- FALSE
-    recruitment$estimate_deviations <- TRUE
-    # recruit deviations should enter the model in normal space.
-    # The log is taken in the likelihood calculations
-    # alternative setting: recruitment$deviations <- rep(1, length(om_input$logR.resid))
-    recruitment$deviations <- exp(om_input$logR.resid)
-    
-    # Data
-    catch <- em_input$L.obs$fleet1
-    fishing_fleet_index <- new(fims$Index, length(catch))
-    fishing_fleet_index$index_data <- catch
-    fishing_fleet_age_comp <- new(fims$AgeComp, length(catch), om_input$nages)
-    fishing_fleet_age_comp$age_comp_data <- c(t(em_input$L.age.obs$fleet1)) * em_input$n.L$fleet1
-    
-    survey_index <- em_input$surveyB.obs$survey1
-    survey_fleet_index <- new(fims$Index, length(survey_index))
-    survey_fleet_index$index_data <- survey_index
-    survey_fleet_age_comp <- new(fims$AgeComp, length(survey_index), om_input$nages)
-    survey_fleet_age_comp$age_comp_data <- c(t(em_input$survey.age.obs$survey1)) * em_input$n.survey$survey1
-    
-    # Growth
-    ewaa_growth <- new(fims$EWAAgrowth)
-    ewaa_growth$ages <- om_input$ages
-    ewaa_growth$weights <- om_input$W.mt
-    
-    # Maturity
-    maturity <- new(fims$LogisticMaturity)
-    maturity$median$value <- om_input$A50.mat
-    maturity$median$is_random_effect <- FALSE
-    maturity$median$estimated <- FALSE
-    maturity$slope$value <- om_input$slope
-    maturity$slope$is_random_effect <- FALSE
-    maturity$slope$estimated <- FALSE
-    
-    # Fleet
-    # Create the fishing fleet
-    fishing_fleet_selectivity <- new(fims$LogisticSelectivity)
-    fishing_fleet_selectivity$median$value <- om_input$sel_fleet$fleet1$A50.sel1
-    fishing_fleet_selectivity$median$is_random_effect <- FALSE
-    fishing_fleet_selectivity$median$estimated <- TRUE
-    fishing_fleet_selectivity$slope$value <- om_input$sel_fleet$fleet1$slope.sel1
-    fishing_fleet_selectivity$slope$is_random_effect <- FALSE
-    fishing_fleet_selectivity$slope$estimated <- TRUE
-    
-    fishing_fleet <- new(fims$Fleet)
-    fishing_fleet$nages <- om_input$nages
-    fishing_fleet$nyears <- om_input$nyr
-    fishing_fleet$log_Fmort <- log(om_output$f)
-    fishing_fleet$estimate_F <- TRUE
-    fishing_fleet$random_F <- FALSE
-    fishing_fleet$log_q <- log(1.0)
-    fishing_fleet$estimate_q <- FALSE
-    fishing_fleet$random_q <- FALSE
-    fishing_fleet$log_obs_error$value <- log(sqrt(log(em_input$cv.L$fleet1^2 + 1)))
-    fishing_fleet$log_obs_error$estimated <- FALSE
-    # Need get_id() for setting up observed agecomp and index data?
-    fishing_fleet$SetAgeCompLikelihood(1)
-    fishing_fleet$SetIndexLikelihood(1)
-    fishing_fleet$SetSelectivity(fishing_fleet_selectivity$get_id())
-    fishing_fleet$SetObservedIndexData(fishing_fleet_index$get_id())
-    fishing_fleet$SetObservedAgeCompData(fishing_fleet_age_comp$get_id())
-    
-    # Create the survey fleet
-    survey_fleet_selectivity <- new(fims$LogisticSelectivity)
-    survey_fleet_selectivity$median$value <- om_input$sel_survey$survey1$A50.sel1
-    survey_fleet_selectivity$median$is_random_effect <- FALSE
-    survey_fleet_selectivity$median$estimated <- TRUE
-    survey_fleet_selectivity$slope$value <- om_input$sel_survey$survey1$slope.sel1
-    survey_fleet_selectivity$slope$is_random_effect <- FALSE
-    survey_fleet_selectivity$slope$estimated <- TRUE
-    
-    survey_fleet <- new(fims$Fleet)
-    survey_fleet$is_survey <- TRUE
-    survey_fleet$nages <- om_input$nages
-    survey_fleet$nyears <- om_input$nyr
-    # survey_fleet$log_Fmort <- rep(log(0.0000000000000000000000000001), om_input$nyr) #-Inf?
-    survey_fleet$estimate_F <- FALSE
-    survey_fleet$random_F <- FALSE
-    survey_fleet$log_q <- log(om_output$survey_q$survey1)
-    survey_fleet$estimate_q <- TRUE
-    survey_fleet$random_q <- FALSE
-    survey_fleet$log_obs_error$value <- log(sqrt(log(em_input$cv.survey$survey1^2 + 1)))
-    survey_fleet$log_obs_error$estimated <- FALSE
-    survey_fleet$SetAgeCompLikelihood(1)
-    survey_fleet$SetIndexLikelihood(1)
-    survey_fleet$SetSelectivity(survey_fleet_selectivity$get_id())
-    survey_fleet$SetObservedIndexData(survey_fleet_index$get_id())
-    survey_fleet$SetObservedAgeCompData(survey_fleet_age_comp$get_id())
-    
-    # Population
-    population <- new(fims$Population)
-    # is it a problem these are not Parameters in the Population interface?
-    # the Parameter class (from rcpp/rcpp_objects/rcpp_interface_base) cannot handle vectors,
-    # do we need a ParameterVector class?
-    population$log_M <- rep(log(om_input$M.age[1]), om_input$nyr * om_input$nages)
-    population$estimate_M <- FALSE
-    population$log_init_naa <- log(om_output$N.age[1, ])
-    population$estimate_init_naa <- TRUE
-    population$nages <- om_input$nages
-    population$ages <- om_input$ages
-    population$nfleets <- sum(om_input$fleet_num, om_input$survey_num)
-    population$nseasons <- 1
-    population$nyears <- om_input$nyr
-    population$prop_female <- om_input$proportion.female[1]
-    population$SetMaturity(maturity$get_id())
-    population$SetGrowth(ewaa_growth$get_id())
-    population$SetRecruitment(recruitment$get_id())
-    
-    ## Set-up TMB
-    fims$CreateTMBModel()
-    parameters <- list(p = fims$get_fixed())
-    obj<-TMB::MakeADFun(data = list(), parameters, DLL = "FIMS")
-    
-    fims$clear()
-    return(obj)
+init_fims <- function(i) {
+  fims <- Rcpp::Module("fims", PACKAGE = "FIMS")
+
+  # Recruitment
+  recruitment <- new(fims$BevertonHoltRecruitment)
+  # logR_sd is NOT logged. It needs to enter the model logged b/c the exp() is taken
+  # before the likelihood calculation
+  recruitment$log_sigma_recruit$value <- log(om_input$logR_sd)
+  recruitment$log_rzero$value <- log(om_input$R0 + runif(1, min = 0, max = 1000))
+  recruitment$log_rzero$is_random_effect <- FALSE
+  recruitment$log_rzero$estimated <- FALSE
+  recruitment$logit_steep$value <- -log(1.0 - om_input$h) + log(om_input$h - 0.2)
+  recruitment$logit_steep$is_random_effect <- FALSE
+  recruitment$logit_steep$estimated <- FALSE
+  recruitment$estimate_deviations <- TRUE
+  # recruit deviations should enter the model in normal space.
+  # The log is taken in the likelihood calculations
+  # alternative setting: recruitment$deviations <- rep(1, length(om_input$logR.resid))
+  recruitment$deviations <- exp(om_input$logR.resid)
+
+  # Data
+  catch <- em_input$L.obs$fleet1
+  fishing_fleet_index <- new(fims$Index, length(catch))
+  fishing_fleet_index$index_data <- catch
+  fishing_fleet_age_comp <- new(fims$AgeComp, length(catch), om_input$nages)
+  fishing_fleet_age_comp$age_comp_data <- c(t(em_input$L.age.obs$fleet1)) * em_input$n.L$fleet1
+
+  survey_index <- em_input$surveyB.obs$survey1
+  survey_fleet_index <- new(fims$Index, length(survey_index))
+  survey_fleet_index$index_data <- survey_index
+  survey_fleet_age_comp <- new(fims$AgeComp, length(survey_index), om_input$nages)
+  survey_fleet_age_comp$age_comp_data <- c(t(em_input$survey.age.obs$survey1)) * em_input$n.survey$survey1
+
+  # Growth
+  ewaa_growth <- new(fims$EWAAgrowth)
+  ewaa_growth$ages <- om_input$ages
+  ewaa_growth$weights <- om_input$W.mt
+
+  # Maturity
+  maturity <- new(fims$LogisticMaturity)
+  maturity$median$value <- om_input$A50.mat
+  maturity$median$is_random_effect <- FALSE
+  maturity$median$estimated <- FALSE
+  maturity$slope$value <- om_input$slope
+  maturity$slope$is_random_effect <- FALSE
+  maturity$slope$estimated <- FALSE
+
+  # Fleet
+  # Create the fishing fleet
+  fishing_fleet_selectivity <- new(fims$LogisticSelectivity)
+  fishing_fleet_selectivity$median$value <- om_input$sel_fleet$fleet1$A50.sel1
+  fishing_fleet_selectivity$median$is_random_effect <- FALSE
+  fishing_fleet_selectivity$median$estimated <- TRUE
+  fishing_fleet_selectivity$slope$value <- om_input$sel_fleet$fleet1$slope.sel1
+  fishing_fleet_selectivity$slope$is_random_effect <- FALSE
+  fishing_fleet_selectivity$slope$estimated <- TRUE
+
+  fishing_fleet <- new(fims$Fleet)
+  fishing_fleet$nages <- om_input$nages
+  fishing_fleet$nyears <- om_input$nyr
+  fishing_fleet$log_Fmort <- log(om_output$f)
+  fishing_fleet$estimate_F <- TRUE
+  fishing_fleet$random_F <- FALSE
+  fishing_fleet$log_q <- log(1.0)
+  fishing_fleet$estimate_q <- FALSE
+  fishing_fleet$random_q <- FALSE
+  fishing_fleet$log_obs_error$value <- log(sqrt(log(em_input$cv.L$fleet1^2 + 1)))
+  fishing_fleet$log_obs_error$estimated <- FALSE
+  # Need get_id() for setting up observed agecomp and index data?
+  fishing_fleet$SetAgeCompLikelihood(1)
+  fishing_fleet$SetIndexLikelihood(1)
+  fishing_fleet$SetSelectivity(fishing_fleet_selectivity$get_id())
+  fishing_fleet$SetObservedIndexData(fishing_fleet_index$get_id())
+  fishing_fleet$SetObservedAgeCompData(fishing_fleet_age_comp$get_id())
+
+  # Create the survey fleet
+  survey_fleet_selectivity <- new(fims$LogisticSelectivity)
+  survey_fleet_selectivity$median$value <- om_input$sel_survey$survey1$A50.sel1
+  survey_fleet_selectivity$median$is_random_effect <- FALSE
+  survey_fleet_selectivity$median$estimated <- TRUE
+  survey_fleet_selectivity$slope$value <- om_input$sel_survey$survey1$slope.sel1
+  survey_fleet_selectivity$slope$is_random_effect <- FALSE
+  survey_fleet_selectivity$slope$estimated <- TRUE
+
+  survey_fleet <- new(fims$Fleet)
+  survey_fleet$is_survey <- TRUE
+  survey_fleet$nages <- om_input$nages
+  survey_fleet$nyears <- om_input$nyr
+  # survey_fleet$log_Fmort <- rep(log(0.0000000000000000000000000001), om_input$nyr) #-Inf?
+  survey_fleet$estimate_F <- FALSE
+  survey_fleet$random_F <- FALSE
+  survey_fleet$log_q <- log(om_output$survey_q$survey1)
+  survey_fleet$estimate_q <- TRUE
+  survey_fleet$random_q <- FALSE
+  survey_fleet$log_obs_error$value <- log(sqrt(log(em_input$cv.survey$survey1^2 + 1)))
+  survey_fleet$log_obs_error$estimated <- FALSE
+  survey_fleet$SetAgeCompLikelihood(1)
+  survey_fleet$SetIndexLikelihood(1)
+  survey_fleet$SetSelectivity(survey_fleet_selectivity$get_id())
+  survey_fleet$SetObservedIndexData(survey_fleet_index$get_id())
+  survey_fleet$SetObservedAgeCompData(survey_fleet_age_comp$get_id())
+
+  # Population
+  population <- new(fims$Population)
+  # is it a problem these are not Parameters in the Population interface?
+  # the Parameter class (from rcpp/rcpp_objects/rcpp_interface_base) cannot handle vectors,
+  # do we need a ParameterVector class?
+  population$log_M <- rep(log(om_input$M.age[1]), om_input$nyr * om_input$nages)
+  population$estimate_M <- FALSE
+  population$log_init_naa <- log(om_output$N.age[1, ])
+  population$estimate_init_naa <- TRUE
+  population$nages <- om_input$nages
+  population$ages <- om_input$ages
+  population$nfleets <- sum(om_input$fleet_num, om_input$survey_num)
+  population$nseasons <- 1
+  population$nyears <- om_input$nyr
+  population$prop_female <- om_input$proportion.female[1]
+  population$SetMaturity(maturity$get_id())
+  population$SetGrowth(ewaa_growth$get_id())
+  population$SetRecruitment(recruitment$get_id())
+
+  ## Set-up TMB
+  fims$CreateTMBModel()
+  parameters <- list(p = fims$get_fixed())
+  obj <- TMB::MakeADFun(data = list(), parameters, DLL = "FIMS")
+
+  fims$clear()
+  return(obj)
 }
 
 
 
 
-run_fims<-function(begin,end){
-    library(FIMS)
-    library(minimizR)
-    minimizer<-1
-    results<-list()
-    
-    id <- mpi.comm.rank()
-    index<-1
-    for(i in begin[id]:end[id]){
-        obj<-init_fims(i)
-        if(minimizer == 1){
-            results[[index]] <-  optim(obj$par, obj$fn, obj$gr,
-            method = "BFGS",
-            control = list(maxit = 1000000, reltol = 1e-15)
-            )
-        }else{
-            results[[index]]<-minimizR(obj$par, obj$fn, obj$gr, control = list(tolerance = 1e-8))
-        }
-        
-        index = index+1
+run_fims <- function(begin, end) {
+  library(FIMS)
+  library(minimizR)
+  minimizer <- 1
+  results <- list()
+
+  id <- mpi.comm.rank()
+  index <- 1
+  for (i in begin[id]:end[id]) {
+    obj <- init_fims(i)
+    if (minimizer == 1) {
+      results[[index]] <- optim(obj$par, obj$fn, obj$gr,
+        method = "BFGS",
+        control = list(maxit = 1000000, reltol = 1e-15)
+      )
+    } else {
+      results[[index]] <- minimizR(obj$par, obj$fn, obj$gr, control = list(tolerance = 1e-8))
     }
-    return(results)
+
+    index <- index + 1
+  }
+  return(results)
 }
 
 
@@ -221,51 +221,50 @@ nsims <- NUMBER_OF_MODEL_RUNS
 begin <- rep(0, ns)
 end <- rep(0, ns)
 
-#create scenario segments
+# create scenario segments
 if (id == 0) {
-    segments <- nsims / ns
-    print(paste("segments ", segments))
-    for (i in 1:ns) {
-        if (i < ns) {
-            begin[i] <- as.integer((i - 1) * segments+1)
-            end[i] <- as.integer(i * segments)
-        } else{
-            begin[i] <- as.integer((i - 1) * segments+1)
-            end[i] <- nsims
-        }
+  segments <- nsims / ns
+  print(paste("segments ", segments))
+  for (i in 1:ns) {
+    if (i < ns) {
+      begin[i] <- as.integer((i - 1) * segments + 1)
+      end[i] <- as.integer(i * segments)
+    } else {
+      begin[i] <- as.integer((i - 1) * segments + 1)
+      end[i] <- nsims
     }
-    print(begin)
-    print(end)
+  }
+  print(begin)
+  print(end)
 }
 
 mpi.spawn.Rslaves(nslaves = ns)
 
 
-#capture minimizer output
-completed<-list()
+# capture minimizer output
+completed <- list()
 
 
 
-#pass the function to all slaves
-mpi.bcast.Robj2slave( all = TRUE)
-#mpi.bcast.Robj2slave(obj = run_fims)
-#mpi.bcast.Robj2slave(obj = init_fims)
+# pass the function to all slaves
+mpi.bcast.Robj2slave(all = TRUE)
+# mpi.bcast.Robj2slave(obj = run_fims)
+# mpi.bcast.Robj2slave(obj = init_fims)
 
-start<-Sys.time()
-#execute all slaves and append the completed list
-append(completed,mpi.remote.exec(run_fims, begin, end))
-end<-Sys.time()
+start <- Sys.time()
+# execute all slaves and append the completed list
+append(completed, mpi.remote.exec(run_fims, begin, end))
+end <- Sys.time()
 
 print(completed)
 
 
-runtime<- (end - start)
-print(paste0(paste0(paste0(NUMBER_OF_MODEL_RUNS," model runs completed in "),runtime)," seconds."))
+runtime <- (end - start)
+print(paste0(paste0(paste0(NUMBER_OF_MODEL_RUNS, " model runs completed in "), runtime), " seconds."))
 
-line=paste("Rmpi runtime ", runtime)
-write(line,file="time.txt",append=TRUE)
+line <- paste("Rmpi runtime ", runtime)
+write(line, file = "time.txt", append = TRUE)
 
 # Tell all slaves to close down, and exit the program
 mpi.close.Rslaves(dellog = FALSE)
 mpi.quit()
-
diff --git a/tests/concurrent/fims_concurrent_processR.R b/tests/concurrent/fims_concurrent_processR.R
index bfc9332d0..9aceb6fb4 100644
--- a/tests/concurrent/fims_concurrent_processR.R
+++ b/tests/concurrent/fims_concurrent_processR.R
@@ -3,7 +3,7 @@ library(minimizR)
 library(processR)
 
 
-#get the Rcpp module
+# get the Rcpp module
 p <- Rcpp::Module(module = "processR", PACKAGE = "processR")
 
 
@@ -32,20 +32,20 @@ setwd(working_dir)
 
 
 
-NUMBER_OF_MODEL_RUNS<-100
+NUMBER_OF_MODEL_RUNS <- 100
 
 
 # Fix and change R0 randomly to get different output for each
 # model run.
-init_fims<-function(i){
+init_fims <- function(i) {
   fims <- Rcpp::Module("fims", PACKAGE = "FIMS")
-  
+
   # Recruitment
   recruitment <- new(fims$BevertonHoltRecruitment)
   # logR_sd is NOT logged. It needs to enter the model logged b/c the exp() is taken
   # before the likelihood calculation
   recruitment$log_sigma_recruit$value <- log(om_input$logR_sd)
-  recruitment$log_rzero$value <- log(om_input$R0 + runif(1,min=0, max=1000))
+  recruitment$log_rzero$value <- log(om_input$R0 + runif(1, min = 0, max = 1000))
   recruitment$log_rzero$is_random_effect <- FALSE
   recruitment$log_rzero$estimated <- FALSE
   recruitment$logit_steep$value <- -log(1.0 - om_input$h) + log(om_input$h - 0.2)
@@ -56,25 +56,25 @@ init_fims<-function(i){
   # The log is taken in the likelihood calculations
   # alternative setting: recruitment$deviations <- rep(1, length(om_input$logR.resid))
   recruitment$deviations <- exp(om_input$logR.resid)
-  
+
   # Data
   catch <- em_input$L.obs$fleet1
   fishing_fleet_index <- new(fims$Index, length(catch))
   fishing_fleet_index$index_data <- catch
   fishing_fleet_age_comp <- new(fims$AgeComp, length(catch), om_input$nages)
   fishing_fleet_age_comp$age_comp_data <- c(t(em_input$L.age.obs$fleet1)) * em_input$n.L$fleet1
-  
+
   survey_index <- em_input$surveyB.obs$survey1
   survey_fleet_index <- new(fims$Index, length(survey_index))
   survey_fleet_index$index_data <- survey_index
   survey_fleet_age_comp <- new(fims$AgeComp, length(survey_index), om_input$nages)
   survey_fleet_age_comp$age_comp_data <- c(t(em_input$survey.age.obs$survey1)) * em_input$n.survey$survey1
-  
+
   # Growth
   ewaa_growth <- new(fims$EWAAgrowth)
   ewaa_growth$ages <- om_input$ages
   ewaa_growth$weights <- om_input$W.mt
-  
+
   # Maturity
   maturity <- new(fims$LogisticMaturity)
   maturity$median$value <- om_input$A50.mat
@@ -83,7 +83,7 @@ init_fims<-function(i){
   maturity$slope$value <- om_input$slope
   maturity$slope$is_random_effect <- FALSE
   maturity$slope$estimated <- FALSE
-  
+
   # Fleet
   # Create the fishing fleet
   fishing_fleet_selectivity <- new(fims$LogisticSelectivity)
@@ -93,7 +93,7 @@ init_fims<-function(i){
   fishing_fleet_selectivity$slope$value <- om_input$sel_fleet$fleet1$slope.sel1
   fishing_fleet_selectivity$slope$is_random_effect <- FALSE
   fishing_fleet_selectivity$slope$estimated <- TRUE
-  
+
   fishing_fleet <- new(fims$Fleet)
   fishing_fleet$nages <- om_input$nages
   fishing_fleet$nyears <- om_input$nyr
@@ -111,7 +111,7 @@ init_fims<-function(i){
   fishing_fleet$SetSelectivity(fishing_fleet_selectivity$get_id())
   fishing_fleet$SetObservedIndexData(fishing_fleet_index$get_id())
   fishing_fleet$SetObservedAgeCompData(fishing_fleet_age_comp$get_id())
-  
+
   # Create the survey fleet
   survey_fleet_selectivity <- new(fims$LogisticSelectivity)
   survey_fleet_selectivity$median$value <- om_input$sel_survey$survey1$A50.sel1
@@ -120,7 +120,7 @@ init_fims<-function(i){
   survey_fleet_selectivity$slope$value <- om_input$sel_survey$survey1$slope.sel1
   survey_fleet_selectivity$slope$is_random_effect <- FALSE
   survey_fleet_selectivity$slope$estimated <- TRUE
-  
+
   survey_fleet <- new(fims$Fleet)
   survey_fleet$is_survey <- TRUE
   survey_fleet$nages <- om_input$nages
@@ -138,7 +138,7 @@ init_fims<-function(i){
   survey_fleet$SetSelectivity(survey_fleet_selectivity$get_id())
   survey_fleet$SetObservedIndexData(survey_fleet_index$get_id())
   survey_fleet$SetObservedAgeCompData(survey_fleet_age_comp$get_id())
-  
+
   # Population
   population <- new(fims$Population)
   # is it a problem these are not Parameters in the Population interface?
@@ -157,12 +157,12 @@ init_fims<-function(i){
   population$SetMaturity(maturity$get_id())
   population$SetGrowth(ewaa_growth$get_id())
   population$SetRecruitment(recruitment$get_id())
-  
+
   ## Set-up TMB
   fims$CreateTMBModel()
   parameters <- list(p = fims$get_fixed())
-  obj<-TMB::MakeADFun(data = list(), parameters, DLL = "FIMS")
-  
+  obj <- TMB::MakeADFun(data = list(), parameters, DLL = "FIMS")
+
   fims$clear()
   return(obj)
 }
@@ -171,28 +171,28 @@ init_fims<-function(i){
 
 
 
-run_fims<-function(){
-  results<<-list()
+run_fims <- function() {
+  results <<- list()
   library(FIMS)
   library(minimizR)
-  minimizer<-2
+  minimizer <- 2
+
 
-  
   id <- processR.rank
-  index<-1
-  for(i in begin[id]:end[id]){
+  index <- 1
+  for (i in begin[id]:end[id]) {
     print(paste(id, paste(i, "me be here")))
-    obj<-init_fims(i)
-    if(minimizer == 1){
-      results[[index]] <<-  optim(obj$par, obj$fn, obj$gr,
-                                 method = "BFGS",
-                                 control = list(maxit = 1000000, reltol = 1e-15)
+    obj <- init_fims(i)
+    if (minimizer == 1) {
+      results[[index]] <<- optim(obj$par, obj$fn, obj$gr,
+        method = "BFGS",
+        control = list(maxit = 1000000, reltol = 1e-15)
       )
-    }else{
-      results[[index]]<<-minimizR(obj$par, obj$fn, obj$gr, control = list(tolerance = 1e-4, verbose = FALSE))
+    } else {
+      results[[index]] <<- minimizR(obj$par, obj$fn, obj$gr, control = list(tolerance = 1e-4, verbose = FALSE))
     }
-    #write(x = i, file = paste0(paste("id",processR.rank)),".txt")
-    index = index+1
+    # write(x = i, file = paste0(paste("id",processR.rank)),".txt")
+    index <- index + 1
   }
   # return(results)
 }
@@ -203,24 +203,24 @@ run_fims<-function(){
 
 
 
-id <- 0#mpi.comm.rank(comm = 0)
-ns <- processR::HardwareConcurrency() 
+id <- 0 # mpi.comm.rank(comm = 0)
+ns <- processR::HardwareConcurrency()
 
 nsims <- NUMBER_OF_MODEL_RUNS
 
 begin <- rep(0, ns)
 end <- rep(0, ns)
 
-#create scenario segments
+# create scenario segments
 if (id == 0) {
   segments <- nsims / ns
   print(paste("segments ", segments))
   for (i in 1:ns) {
     if (i < ns) {
-      begin[i] <- as.integer((i - 1) * segments+1)
+      begin[i] <- as.integer((i - 1) * segments + 1)
       end[i] <- as.integer(i * segments)
-    } else{
-      begin[i] <- as.integer((i - 1) * segments+1)
+    } else {
+      begin[i] <- as.integer((i - 1) * segments + 1)
       end[i] <- nsims
     }
   }
@@ -230,43 +230,42 @@ if (id == 0) {
 
 
 
-start<-Sys.time()
+start <- Sys.time()
 
-#create a pool of child processes
-pool<-list()
+# create a pool of child processes
+pool <- list()
 
-for(i in 1:processR::HardwareConcurrency()){
-  #create a new child process
+for (i in 1:processR::HardwareConcurrency()) {
+  # create a new child process
   pool[[i]] <- new(p$Process)
-  
-  #start the process. pass the function, environment, and rank
+
+  # start the process. pass the function, environment, and rank
   pool[[i]]$start(run_fims, environment(), i)
 }
 
-#iterate of the children and capture information
-for(i in 1:processR::HardwareConcurrency()){
-    
-   #wait for the child to complete
-   pool[[i]]$wait()
-   
-   #get child out stream
-   #message<-pool[[i]]$get_message()
-   
-   #access the childs environment
-   env<-as.environment(pool[[i]]$get_environment());
-   #show minimizer results
+# iterate of the children and capture information
+for (i in 1:processR::HardwareConcurrency()) {
+  # wait for the child to complete
+  pool[[i]]$wait()
+
+  # get child out stream
+  # message<-pool[[i]]$get_message()
+
+  # access the childs environment
+  env <- as.environment(pool[[i]]$get_environment())
+  # show minimizer results
   # print(env[["results"]])
 }
 
-end_<-Sys.time()
+end_ <- Sys.time()
 
 
 
-runtime<- (end_ - start)
-print(paste0(paste0(paste0(NUMBER_OF_MODEL_RUNS," model runs completed in "),runtime)," seconds."))
+runtime <- (end_ - start)
+print(paste0(paste0(paste0(NUMBER_OF_MODEL_RUNS, " model runs completed in "), runtime), " seconds."))
 
 print(begin)
 print(end)
 
-line=paste("processR runtime ", runtime)    
-write(line,file="time.txt",append=TRUE)
+line <- paste("processR runtime ", runtime)
+write(line, file = "time.txt", append = TRUE)
diff --git a/tests/concurrent/fims_concurrent_snowfall.R b/tests/concurrent/fims_concurrent_snowfall.R
index 1074a69ce..93d5cc109 100644
--- a/tests/concurrent/fims_concurrent_snowfall.R
+++ b/tests/concurrent/fims_concurrent_snowfall.R
@@ -9,14 +9,14 @@ working_dir <- getwd()
 maindir <- tempdir()
 model_input <- ASSAMC::save_initial_input()
 FIMS_C0_estimation <- ASSAMC::save_initial_input(
-base_case = TRUE,
-input_list = model_input,
-maindir = maindir,
-om_sim_num = 1,
-keep_sim_num = 1,
-figure_number = 1,
-seed_num = 9924,
-case_name = "FIMS_C0_estimation"
+  base_case = TRUE,
+  input_list = model_input,
+  maindir = maindir,
+  om_sim_num = 1,
+  keep_sim_num = 1,
+  figure_number = 1,
+  seed_num = 9924,
+  case_name = "FIMS_C0_estimation"
 )
 
 ASSAMC::run_om(input_list = FIMS_C0_estimation)
@@ -27,166 +27,166 @@ setwd(working_dir)
 
 
 
-NUMBER_OF_MODEL_RUNS<-100
+NUMBER_OF_MODEL_RUNS <- 100
 
 
 # Fix and change R0 randomly to get different output for each
 # model run.
-init_fims<-function(i){
-    fims <- Rcpp::Module("fims", PACKAGE = "FIMS")
-    
-    # Recruitment
-    recruitment <- new(fims$BevertonHoltRecruitment)
-    # logR_sd is NOT logged. It needs to enter the model logged b/c the exp() is taken
-    # before the likelihood calculation
-    recruitment$log_sigma_recruit$value <- log(om_input$logR_sd)
-    recruitment$log_rzero$value <- log(om_input$R0)# + runif(1,min=0, max=1000))
-    recruitment$log_rzero$is_random_effect <- FALSE
-    recruitment$log_rzero$estimated <- TRUE
-    recruitment$logit_steep$value <- -log(1.0 - om_input$h) + log(om_input$h - 0.2)
-    recruitment$logit_steep$is_random_effect <- FALSE
-    recruitment$logit_steep$estimated <- FALSE
-    recruitment$estimate_deviations <- TRUE
-    # recruit deviations should enter the model in normal space.
-    # The log is taken in the likelihood calculations
-    # alternative setting: recruitment$deviations <- rep(1, length(om_input$logR.resid))
-    recruitment$deviations <- exp(om_input$logR.resid)
-    
-    # Data
-    catch <- em_input$L.obs$fleet1
-    fishing_fleet_index <- new(fims$Index, length(catch))
-    fishing_fleet_index$index_data <- catch
-    fishing_fleet_age_comp <- new(fims$AgeComp, length(catch), om_input$nages)
-    fishing_fleet_age_comp$age_comp_data <- c(t(em_input$L.age.obs$fleet1)) * em_input$n.L$fleet1
-    
-    survey_index <- em_input$surveyB.obs$survey1
-    survey_fleet_index <- new(fims$Index, length(survey_index))
-    survey_fleet_index$index_data <- survey_index
-    survey_fleet_age_comp <- new(fims$AgeComp, length(survey_index), om_input$nages)
-    survey_fleet_age_comp$age_comp_data <- c(t(em_input$survey.age.obs$survey1)) * em_input$n.survey$survey1
-    
-    # Growth
-    ewaa_growth <- new(fims$EWAAgrowth)
-    ewaa_growth$ages <- om_input$ages
-    ewaa_growth$weights <- om_input$W.mt
-    
-    # Maturity
-    maturity <- new(fims$LogisticMaturity)
-    maturity$median$value <- om_input$A50.mat
-    maturity$median$is_random_effect <- FALSE
-    maturity$median$estimated <- FALSE
-    maturity$slope$value <- om_input$slope
-    maturity$slope$is_random_effect <- FALSE
-    maturity$slope$estimated <- FALSE
-    
-    # Fleet
-    # Create the fishing fleet
-    fishing_fleet_selectivity <- new(fims$LogisticSelectivity)
-    fishing_fleet_selectivity$median$value <- om_input$sel_fleet$fleet1$A50.sel1
-    fishing_fleet_selectivity$median$is_random_effect <- FALSE
-    fishing_fleet_selectivity$median$estimated <- TRUE
-    fishing_fleet_selectivity$slope$value <- om_input$sel_fleet$fleet1$slope.sel1
-    fishing_fleet_selectivity$slope$is_random_effect <- FALSE
-    fishing_fleet_selectivity$slope$estimated <- TRUE
-    
-    fishing_fleet <- new(fims$Fleet)
-    fishing_fleet$nages <- om_input$nages
-    fishing_fleet$nyears <- om_input$nyr
-    fishing_fleet$log_Fmort <- log(om_output$f)
-    fishing_fleet$estimate_F <- TRUE
-    fishing_fleet$random_F <- FALSE
-    fishing_fleet$log_q <- log(1.0)
-    fishing_fleet$estimate_q <- FALSE
-    fishing_fleet$random_q <- FALSE
-    fishing_fleet$log_obs_error$value <- log(sqrt(log(em_input$cv.L$fleet1^2 + 1)))
-    fishing_fleet$log_obs_error$estimated <- FALSE
-    # Need get_id() for setting up observed agecomp and index data?
-    fishing_fleet$SetAgeCompLikelihood(1)
-    fishing_fleet$SetIndexLikelihood(1)
-    fishing_fleet$SetSelectivity(fishing_fleet_selectivity$get_id())
-    fishing_fleet$SetObservedIndexData(fishing_fleet_index$get_id())
-    fishing_fleet$SetObservedAgeCompData(fishing_fleet_age_comp$get_id())
-    
-    # Create the survey fleet
-    survey_fleet_selectivity <- new(fims$LogisticSelectivity)
-    survey_fleet_selectivity$median$value <- om_input$sel_survey$survey1$A50.sel1
-    survey_fleet_selectivity$median$is_random_effect <- FALSE
-    survey_fleet_selectivity$median$estimated <- TRUE
-    survey_fleet_selectivity$slope$value <- om_input$sel_survey$survey1$slope.sel1
-    survey_fleet_selectivity$slope$is_random_effect <- FALSE
-    survey_fleet_selectivity$slope$estimated <- TRUE
-    
-    survey_fleet <- new(fims$Fleet)
-    survey_fleet$is_survey <- TRUE
-    survey_fleet$nages <- om_input$nages
-    survey_fleet$nyears <- om_input$nyr
-    # survey_fleet$log_Fmort <- rep(log(0.0000000000000000000000000001), om_input$nyr) #-Inf?
-    survey_fleet$estimate_F <- FALSE
-    survey_fleet$random_F <- FALSE
-    survey_fleet$log_q <- log(om_output$survey_q$survey1)
-    survey_fleet$estimate_q <- TRUE
-    survey_fleet$random_q <- FALSE
-    survey_fleet$log_obs_error$value <- log(sqrt(log(em_input$cv.survey$survey1^2 + 1)))
-    survey_fleet$log_obs_error$estimated <- FALSE
-    survey_fleet$SetAgeCompLikelihood(1)
-    survey_fleet$SetIndexLikelihood(1)
-    survey_fleet$SetSelectivity(survey_fleet_selectivity$get_id())
-    survey_fleet$SetObservedIndexData(survey_fleet_index$get_id())
-    survey_fleet$SetObservedAgeCompData(survey_fleet_age_comp$get_id())
-    
-    # Population
-    population <- new(fims$Population)
-    # is it a problem these are not Parameters in the Population interface?
-    # the Parameter class (from rcpp/rcpp_objects/rcpp_interface_base) cannot handle vectors,
-    # do we need a ParameterVector class?
-    population$log_M <- rep(log(om_input$M.age[1]), om_input$nyr * om_input$nages)
-    population$estimate_M <- FALSE
-    population$log_init_naa <- log(om_output$N.age[1, ])
-    population$estimate_init_naa <- TRUE
-    population$nages <- om_input$nages
-    population$ages <- om_input$ages
-    population$nfleets <- sum(om_input$fleet_num, om_input$survey_num)
-    population$nseasons <- 1
-    population$nyears <- om_input$nyr
-    population$prop_female <- om_input$proportion.female[1]
-    population$SetMaturity(maturity$get_id())
-    population$SetGrowth(ewaa_growth$get_id())
-    population$SetRecruitment(recruitment$get_id())
-    
-    ## Set-up TMB
-    fims$CreateTMBModel()
-    parameters <- list(p = fims$get_fixed())
-    obj<-TMB::MakeADFun(data = list(), parameters, DLL = "FIMS")
-    
-    fims$clear()
-    return(obj)
+init_fims <- function(i) {
+  fims <- Rcpp::Module("fims", PACKAGE = "FIMS")
+
+  # Recruitment
+  recruitment <- new(fims$BevertonHoltRecruitment)
+  # logR_sd is NOT logged. It needs to enter the model logged b/c the exp() is taken
+  # before the likelihood calculation
+  recruitment$log_sigma_recruit$value <- log(om_input$logR_sd)
+  recruitment$log_rzero$value <- log(om_input$R0) # + runif(1,min=0, max=1000))
+  recruitment$log_rzero$is_random_effect <- FALSE
+  recruitment$log_rzero$estimated <- TRUE
+  recruitment$logit_steep$value <- -log(1.0 - om_input$h) + log(om_input$h - 0.2)
+  recruitment$logit_steep$is_random_effect <- FALSE
+  recruitment$logit_steep$estimated <- FALSE
+  recruitment$estimate_deviations <- TRUE
+  # recruit deviations should enter the model in normal space.
+  # The log is taken in the likelihood calculations
+  # alternative setting: recruitment$deviations <- rep(1, length(om_input$logR.resid))
+  recruitment$deviations <- exp(om_input$logR.resid)
+
+  # Data
+  catch <- em_input$L.obs$fleet1
+  fishing_fleet_index <- new(fims$Index, length(catch))
+  fishing_fleet_index$index_data <- catch
+  fishing_fleet_age_comp <- new(fims$AgeComp, length(catch), om_input$nages)
+  fishing_fleet_age_comp$age_comp_data <- c(t(em_input$L.age.obs$fleet1)) * em_input$n.L$fleet1
+
+  survey_index <- em_input$surveyB.obs$survey1
+  survey_fleet_index <- new(fims$Index, length(survey_index))
+  survey_fleet_index$index_data <- survey_index
+  survey_fleet_age_comp <- new(fims$AgeComp, length(survey_index), om_input$nages)
+  survey_fleet_age_comp$age_comp_data <- c(t(em_input$survey.age.obs$survey1)) * em_input$n.survey$survey1
+
+  # Growth
+  ewaa_growth <- new(fims$EWAAgrowth)
+  ewaa_growth$ages <- om_input$ages
+  ewaa_growth$weights <- om_input$W.mt
+
+  # Maturity
+  maturity <- new(fims$LogisticMaturity)
+  maturity$median$value <- om_input$A50.mat
+  maturity$median$is_random_effect <- FALSE
+  maturity$median$estimated <- FALSE
+  maturity$slope$value <- om_input$slope
+  maturity$slope$is_random_effect <- FALSE
+  maturity$slope$estimated <- FALSE
+
+  # Fleet
+  # Create the fishing fleet
+  fishing_fleet_selectivity <- new(fims$LogisticSelectivity)
+  fishing_fleet_selectivity$median$value <- om_input$sel_fleet$fleet1$A50.sel1
+  fishing_fleet_selectivity$median$is_random_effect <- FALSE
+  fishing_fleet_selectivity$median$estimated <- TRUE
+  fishing_fleet_selectivity$slope$value <- om_input$sel_fleet$fleet1$slope.sel1
+  fishing_fleet_selectivity$slope$is_random_effect <- FALSE
+  fishing_fleet_selectivity$slope$estimated <- TRUE
+
+  fishing_fleet <- new(fims$Fleet)
+  fishing_fleet$nages <- om_input$nages
+  fishing_fleet$nyears <- om_input$nyr
+  fishing_fleet$log_Fmort <- log(om_output$f)
+  fishing_fleet$estimate_F <- TRUE
+  fishing_fleet$random_F <- FALSE
+  fishing_fleet$log_q <- log(1.0)
+  fishing_fleet$estimate_q <- FALSE
+  fishing_fleet$random_q <- FALSE
+  fishing_fleet$log_obs_error$value <- log(sqrt(log(em_input$cv.L$fleet1^2 + 1)))
+  fishing_fleet$log_obs_error$estimated <- FALSE
+  # Need get_id() for setting up observed agecomp and index data?
+  fishing_fleet$SetAgeCompLikelihood(1)
+  fishing_fleet$SetIndexLikelihood(1)
+  fishing_fleet$SetSelectivity(fishing_fleet_selectivity$get_id())
+  fishing_fleet$SetObservedIndexData(fishing_fleet_index$get_id())
+  fishing_fleet$SetObservedAgeCompData(fishing_fleet_age_comp$get_id())
+
+  # Create the survey fleet
+  survey_fleet_selectivity <- new(fims$LogisticSelectivity)
+  survey_fleet_selectivity$median$value <- om_input$sel_survey$survey1$A50.sel1
+  survey_fleet_selectivity$median$is_random_effect <- FALSE
+  survey_fleet_selectivity$median$estimated <- TRUE
+  survey_fleet_selectivity$slope$value <- om_input$sel_survey$survey1$slope.sel1
+  survey_fleet_selectivity$slope$is_random_effect <- FALSE
+  survey_fleet_selectivity$slope$estimated <- TRUE
+
+  survey_fleet <- new(fims$Fleet)
+  survey_fleet$is_survey <- TRUE
+  survey_fleet$nages <- om_input$nages
+  survey_fleet$nyears <- om_input$nyr
+  # survey_fleet$log_Fmort <- rep(log(0.0000000000000000000000000001), om_input$nyr) #-Inf?
+  survey_fleet$estimate_F <- FALSE
+  survey_fleet$random_F <- FALSE
+  survey_fleet$log_q <- log(om_output$survey_q$survey1)
+  survey_fleet$estimate_q <- TRUE
+  survey_fleet$random_q <- FALSE
+  survey_fleet$log_obs_error$value <- log(sqrt(log(em_input$cv.survey$survey1^2 + 1)))
+  survey_fleet$log_obs_error$estimated <- FALSE
+  survey_fleet$SetAgeCompLikelihood(1)
+  survey_fleet$SetIndexLikelihood(1)
+  survey_fleet$SetSelectivity(survey_fleet_selectivity$get_id())
+  survey_fleet$SetObservedIndexData(survey_fleet_index$get_id())
+  survey_fleet$SetObservedAgeCompData(survey_fleet_age_comp$get_id())
+
+  # Population
+  population <- new(fims$Population)
+  # is it a problem these are not Parameters in the Population interface?
+  # the Parameter class (from rcpp/rcpp_objects/rcpp_interface_base) cannot handle vectors,
+  # do we need a ParameterVector class?
+  population$log_M <- rep(log(om_input$M.age[1]), om_input$nyr * om_input$nages)
+  population$estimate_M <- FALSE
+  population$log_init_naa <- log(om_output$N.age[1, ])
+  population$estimate_init_naa <- TRUE
+  population$nages <- om_input$nages
+  population$ages <- om_input$ages
+  population$nfleets <- sum(om_input$fleet_num, om_input$survey_num)
+  population$nseasons <- 1
+  population$nyears <- om_input$nyr
+  population$prop_female <- om_input$proportion.female[1]
+  population$SetMaturity(maturity$get_id())
+  population$SetGrowth(ewaa_growth$get_id())
+  population$SetRecruitment(recruitment$get_id())
+
+  ## Set-up TMB
+  fims$CreateTMBModel()
+  parameters <- list(p = fims$get_fixed())
+  obj <- TMB::MakeADFun(data = list(), parameters, DLL = "FIMS")
+
+  fims$clear()
+  return(obj)
 }
 
 
 
 
-run_fims<-function(id,begin,end){
-    library(FIMS)
-    library(minimizR)
-    minimizer<-2
-    results<-list()
-    
-    
-    index<-1
-    for(i in begin[id]:end[id]){
-        obj<-init_fims(i)
-        if(minimizer == 1){
-            results[[index]] <-  optim(obj$par, obj$fn, obj$gr,
-            method = "BFGS",
-            control = list(maxit = 1000000, reltol = 1e-15)
-            )
-        }else{
-            results[[index]]<-minimizR(obj$par, obj$fn, obj$gr, control = list(tolerance = 1e-8))
-        }
-        
-        index = index+1
+run_fims <- function(id, begin, end) {
+  library(FIMS)
+  library(minimizR)
+  minimizer <- 2
+  results <- list()
+
+
+  index <- 1
+  for (i in begin[id]:end[id]) {
+    obj <- init_fims(i)
+    if (minimizer == 1) {
+      results[[index]] <- optim(obj$par, obj$fn, obj$gr,
+        method = "BFGS",
+        control = list(maxit = 1000000, reltol = 1e-15)
+      )
+    } else {
+      results[[index]] <- minimizR(obj$par, obj$fn, obj$gr, control = list(tolerance = 1e-8))
     }
-    return(results)
+
+    index <- index + 1
+  }
+  return(results)
 }
 
 
@@ -203,32 +203,32 @@ nsims <- NUMBER_OF_MODEL_RUNS
 begin <- rep(0, ns)
 end <- rep(0, ns)
 
-#create scenario segments
+# create scenario segments
 if (id == 0) {
-    segments <- nsims / ns
-    print(paste("segments ", segments))
-    for (i in 1:ns) {
-        if (i < ns) {
-            begin[i] <- as.integer((i - 1) * segments+1)
-            end[i] <- as.integer(i * segments)
-        } else{
-            begin[i] <- as.integer((i - 1) * segments+1)
-            end[i] <- nsims
-        }
+  segments <- nsims / ns
+  print(paste("segments ", segments))
+  for (i in 1:ns) {
+    if (i < ns) {
+      begin[i] <- as.integer((i - 1) * segments + 1)
+      end[i] <- as.integer(i * segments)
+    } else {
+      begin[i] <- as.integer((i - 1) * segments + 1)
+      end[i] <- nsims
     }
-    print(begin)
-    print(end)
+  }
+  print(begin)
+  print(end)
 }
 
-completed<-list()
+completed <- list()
 
-sfInit(parallel=TRUE, cpus=ns)
-sfExport( "run_fims", "init_fims", "om_input","om_output", "em_input")
-start<-Sys.time()
+sfInit(parallel = TRUE, cpus = ns)
+sfExport("run_fims", "init_fims", "om_input", "om_output", "em_input")
+start <- Sys.time()
 append(sfLapply(1:ns, run_fims, begin, end), completed)
-end<-Sys.time()
-runtime<-end-start
-print(paste0(paste0(paste0(NUMBER_OF_MODEL_RUNS," model runs completed in "),runtime)," seconds."))
+end <- Sys.time()
+runtime <- end - start
+print(paste0(paste0(paste0(NUMBER_OF_MODEL_RUNS, " model runs completed in "), runtime), " seconds."))
 
-line=paste("Snowfall runtime ", runtime)    
-write(line,file="time.txt",append=TRUE)
+line <- paste("Snowfall runtime ", runtime)
+write(line, file = "time.txt", append = TRUE)
diff --git a/tests/testthat/test-data-object.R b/tests/testthat/test-data-object.R
index 66532750f..d3be61c55 100644
--- a/tests/testthat/test-data-object.R
+++ b/tests/testthat/test-data-object.R
@@ -23,7 +23,6 @@ fleet_names_index <- dplyr::filter(
 nindex <- length(fleet_names_index)
 
 test_that("Can add index data to model", {
-
   indexdat <- vector(mode = "list", length = nindex)
   names(indexdat) <- fleet_names_index
 
@@ -38,7 +37,6 @@ test_that("Can add index data to model", {
 })
 
 test_that("Can add agecomp data to model", {
-
   agecompdat <- vector(mode = "list", length = nagecomp)
   names(agecompdat) <- fleet_names_agecomp
 
diff --git a/tests/testthat/test-fims-estimation.R b/tests/testthat/test-fims-estimation.R
index d79a02dce..d7bfaa1c0 100644
--- a/tests/testthat/test-fims-estimation.R
+++ b/tests/testthat/test-fims-estimation.R
@@ -16,7 +16,7 @@ FIMS_C0_estimation <- ASSAMC::save_initial_input(
   case_name = "FIMS_C0_estimation"
 )
 
-# generate om_input, om_output, and em_input 
+# generate om_input, om_output, and em_input
 # using function from the model comparison project
 ASSAMC::run_om(input_list = FIMS_C0_estimation)
 
@@ -37,10 +37,10 @@ setup_fims <- function(om_input, om_output, em_input) {
   # when there are other options, this would be where the option would be chosen)
   test_env$recruitment <- methods::new(test_env$fims$BevertonHoltRecruitment)
 
-  # NOTE: in first set of parameters below (for recruitment), 
-  # $is_random_effect (default is FALSE) and $estimated (default is FALSE) 
-  # are defined even if they match the defaults in order to provide an example 
-  # of how that is done. Other sections of the code below leave defaults in 
+  # NOTE: in first set of parameters below (for recruitment),
+  # $is_random_effect (default is FALSE) and $estimated (default is FALSE)
+  # are defined even if they match the defaults in order to provide an example
+  # of how that is done. Other sections of the code below leave defaults in
   # place as appropriate.
 
   # set up logR_sd
@@ -186,14 +186,14 @@ test_that("deterministic test of fims", {
     om_output = om_output,
     em_input = em_input
   )
-  # Set-up 
+  # Set-up
   deterministic_env$fims$CreateTMBModel()
   # CreateTMBModel calls a function in information that loops
   # over all the populations and fleets and sets all the pointers
   parameters <- list(p = deterministic_env$fims$get_fixed())
   # get_fixed function is an Rcpp function that loops over all Rcpp
   # modules and returned a vector of parameters being estimated
- 
+
   # Set up TMB's computational graph
   obj <- MakeADFun(data = list(), parameters, DLL = "FIMS")
 
@@ -354,7 +354,7 @@ test_that("nll test of fims", {
   # recruitment likelihood
   # log_devs is of length nyr-1
   rec_nll <- -sum(dnorm(
-    nll_env$recruitment$log_devs, rep(0, om_input$nyr-1),
+    nll_env$recruitment$log_devs, rep(0, om_input$nyr - 1),
     om_input$logR_sd, TRUE
   ))
 
@@ -485,15 +485,15 @@ test_that("estimation test of fims", {
   # recruitment log deviations
   # the initial value of om_input$logR.resid is dropped from the model
   sdr_rdev <- sdr_report[which(rownames(sdr_report) == "LogRecDev"), ]
-  rdev_are <- rep(0, length(om_input$logR.resid)-1)
+  rdev_are <- rep(0, length(om_input$logR.resid) - 1)
 
-  for (i in 1:length(report$log_recruit_dev[[1]]) ){
-    rdev_are[i] <- abs(report$log_recruit_dev[[1]][i] - om_input$logR.resid[i+1]) # /
+  for (i in 1:length(report$log_recruit_dev[[1]])) {
+    rdev_are[i] <- abs(report$log_recruit_dev[[1]][i] - om_input$logR.resid[i + 1]) # /
     #   exp(om_input$logR.resid[i])
     # expect_lte(rdev_are[i], 1) # 1
   }
   expect_lte(
-    sum(rdev_are > qnorm(.975) * sdr_rdev[1:length(om_input$logR.resid)-1, 2]),
+    sum(rdev_are > qnorm(.975) * sdr_rdev[1:length(om_input$logR.resid) - 1, 2]),
     0.05 * length(om_input$logR.resid)
   )
 
@@ -622,14 +622,14 @@ test_that("run FIMS in a for loop", {
     # logR_sd is NOT logged. It needs to enter the model logged b/c the exp() is taken
     # before the likelihood calculation
     recruitment$log_sigma_recruit$value <- log(om_input$logR_sd)
-    recruitment$log_rzero$value <- 13 #log(om_input$R0)
+    recruitment$log_rzero$value <- 13 # log(om_input$R0)
     recruitment$log_rzero$is_random_effect <- FALSE
     recruitment$log_rzero$estimated <- TRUE
     recruitment$logit_steep$value <- -log(1.0 - om_input$h) + log(om_input$h - 0.2)
     recruitment$logit_steep$is_random_effect <- FALSE
     recruitment$logit_steep$estimated <- FALSE
     recruitment$estimate_log_devs <- TRUE
-    recruitment$log_devs <- rep(0, length(om_input$logR.resid)-1)
+    recruitment$log_devs <- rep(0, length(om_input$logR.resid) - 1)
 
     # Data
     catch <- em_input$L.obs$fleet1
@@ -736,12 +736,12 @@ test_that("run FIMS in a for loop", {
     fims$CreateTMBModel()
     parameters <- list(p = fims$get_fixed())
     obj <- TMB::MakeADFun(data = list(), parameters, DLL = "FIMS")
-   
+
     opt <- with(obj, optim(par, fn, gr,
       method = "BFGS",
       control = list(maxit = 1000000, reltol = 1e-15)
     ))
-    
+
     report <- obj$report(obj$par)
     expect_false(is.null(report))
 
diff --git a/tests/testthat/test-fims-rcpp.R b/tests/testthat/test-fims-rcpp.R
index ca219bea6..58cd924a8 100644
--- a/tests/testthat/test-fims-rcpp.R
+++ b/tests/testthat/test-fims-rcpp.R
@@ -1,5 +1,4 @@
 test_that("Rcpp interface works for modules", {
-
   expect_no_error(parameter <- new(Parameter, .1))
   expect_no_error(beverton_holt <- new(BevertonHoltRecruitment))
   expect_no_error(logistic_selectivity <- new(LogisticSelectivity))
diff --git a/tests/testthat/test-fleet-interface.R b/tests/testthat/test-fleet-interface.R
index 0562e5db6..637b29a85 100644
--- a/tests/testthat/test-fleet-interface.R
+++ b/tests/testthat/test-fleet-interface.R
@@ -1,6 +1,5 @@
 test_that("Fleet: selectivity IDs can be added to the
 fleet module", {
-
   # Create selectivity for fleet 1
   selectivity_fleet1 <- new(LogisticSelectivity)
   expect_equal((selectivity_fleet1$get_id()), 1)
diff --git a/tests/testthat/test-maturity-interface.R b/tests/testthat/test-maturity-interface.R
index 907ef9684..4ded4336c 100644
--- a/tests/testthat/test-maturity-interface.R
+++ b/tests/testthat/test-maturity-interface.R
@@ -1,5 +1,4 @@
 test_that("Maturity input settings work as expected", {
-
   # Create maturity1
   maturity1 <- new(LogisticMaturity)
 
diff --git a/tests/testthat/test-recruitment-interface.R b/tests/testthat/test-recruitment-interface.R
index 4d55d639d..55e122b8d 100644
--- a/tests/testthat/test-recruitment-interface.R
+++ b/tests/testthat/test-recruitment-interface.R
@@ -1,6 +1,5 @@
 library(testthat)
 test_that("Recruitment input settings work as expected", {
-
   # Create recruitment
   recruitment <- new(BevertonHoltRecruitment)
   h <- 0.75
diff --git a/tests/testthat/test-selectivity-interface.R b/tests/testthat/test-selectivity-interface.R
index d720a0ed0..398fe0c9d 100644
--- a/tests/testthat/test-selectivity-interface.R
+++ b/tests/testthat/test-selectivity-interface.R
@@ -1,5 +1,4 @@
 test_that("Selectivity input settings work as expected", {
-
   # Create selectivity1
   selectivity1 <- new(LogisticSelectivity)
 
diff --git a/vignettes/fims-demo.Rmd b/vignettes/fims-demo.Rmd
index 5053674fa..8aab38bcd 100644
--- a/vignettes/fims-demo.Rmd
+++ b/vignettes/fims-demo.Rmd
@@ -257,13 +257,13 @@ We also need to set up log recruitment deviations. FIMS recruitment modules have
 ```{r rec-devs}
 recruitment$estimate_log_devs <- FALSE
 recruitment$log_devs <- c(
-  0.08904850,  0.43787763, -0.13299042, -0.43251973,
-  0.64861200,  0.50640852, -0.06958319,  0.30246260,
-  -0.08257384,  0.20740372,  0.15289604, -0.21709207,       
-  -0.13320626,  0.11225374, -0.10650836,  0.26877132,
-  0.24094126, -0.54480751, -0.23680557, -0.58483386,  
-  0.30122785,  0.21930545, -0.22281699, -0.51358369,       
-  0.15740234, -0.53988240, -0.19556523,  0.20094360,  
+  0.08904850, 0.43787763, -0.13299042, -0.43251973,
+  0.64861200, 0.50640852, -0.06958319, 0.30246260,
+  -0.08257384, 0.20740372, 0.15289604, -0.21709207,
+  -0.13320626, 0.11225374, -0.10650836, 0.26877132,
+  0.24094126, -0.54480751, -0.23680557, -0.58483386,
+  0.30122785, 0.21930545, -0.22281699, -0.51358369,
+  0.15740234, -0.53988240, -0.19556523, 0.20094360,
   0.37248740, -0.07163145
 )
 ```