diff --git a/R/01_read.R b/R/01_read.R
index 4eafffc..06fea1c 100644
--- a/R/01_read.R
+++ b/R/01_read.R
@@ -53,7 +53,7 @@ explorer <- function(data, x, y, id, metadata) {
   }
   x <- names(select(data, {{ x }}))
   y <- names(select(data, {{ y }}))
-  .keep <- names(select(data, {{ metadata }}))
+  metadata <- names(select(data, {{ metadata }}))
   for (i in y) {
     class_trait <- data[[i]] |> class()
     if (!class_trait %in% c("numeric", "integer")) {
@@ -62,14 +62,14 @@ explorer <- function(data, x, y, id, metadata) {
       )
     }
   }
-  check_metadata(data, .keep)
+  check_metadata(data, metadata)
   data <- data |>
-    select(all_of(c(id, .keep, x, y))) |>
+    select(all_of(c(id, metadata, x, y))) |>
     mutate(uid = .data[[id]], .keep = "unused", .before = 0) |>
     rename(x = all_of(x))
-  resum <- summarize_metadata(data, cols = c("uid", "x", .keep))
+  resum <- summarize_metadata(data, cols = c("uid", "x", metadata))
   dt_long <- data |>
-    select(uid, all_of(.keep), x, all_of(y)) |>
+    select(uid, all_of(metadata), x, all_of(y)) |>
     pivot_longer(all_of(y), names_to = "var", values_to = "y") |>
     relocate(x, .after = var)
   summ_vars <- dt_long |>
@@ -104,7 +104,7 @@ explorer <- function(data, x, y, id, metadata) {
     summ_metadata = resum,
     locals_min_max = max_min,
     dt_long = dt_long,
-    metadata = .keep,
+    metadata = metadata,
     x_var = x
   )
   class(out) <- "explorer"
diff --git a/R/02_modeler.R b/R/02_modeler.R
index 8941e1f..eae6054 100644
--- a/R/02_modeler.R
+++ b/R/02_modeler.R
@@ -10,8 +10,9 @@
 #' @param keep The names of the columns in `data` to keep across the analysis.
 #' @param fn A string specifying the name of the function to be used for the curve fitting. Default is \code{"fn_linear_sat"}.
 #' @param parameters Can be a named numeric vector specifying the initial values for the parameters to be optimized,
-#' or a data frame with columns \code{uid}, and the initial parameter values for each group id. Used for providing specific
-#' initial values per group id. Default is \code{NULL}.
+#' If a data frame it needs to have a column \code{uid}, and the initial parameter values for each group id.
+#' If list it needs to be a named list with the initial parameters and numeric or string values can be used (e.g. list(k = "max(y)", t1 = 40)).
+#' Used for providing specific initial values per group id. Default is \code{NULL}.
 #' @param lower Numeric vector specifying the lower bounds for the parameters. Default is \code{-Inf} for all parameters.
 #' @param upper Numeric vector specifying the upper bounds for the parameters. Default is \code{Inf} for all parameters.
 #' @param fixed_params Can be a list or data frame. If data frame it needs columns \code{uid}, and the fixed parameter values for each group id.
@@ -100,7 +101,7 @@ modeler <- function(data,
   if (!inherits(x, "explorer")) {
     stop("The object should be of class 'explorer'.")
   }
-  .keep <- x$metadata
+  metadata <- x$metadata
   variable <- unique(x$summ_vars$var)
   if (length(variable) != 1) stop("Only single response is allowed.")
   # Validate options
@@ -139,11 +140,11 @@ modeler <- function(data,
     droplevels()
   if (max_as_last) {
     dt <- dt |>
-      group_by(uid, across(all_of(.keep))) |>
+      group_by(uid, across(all_of(metadata))) |>
       mutate(max = max(y, na.rm = TRUE), pos = x[which.max(y)]) |>
       mutate(y = ifelse(x <= pos, y, max)) |>
       select(-max, -pos) |>
-      ungroup() # max_as_last(dt, .keep = .keep)
+      ungroup()
   }
   if (check_negative) {
     dt <- mutate(dt, y = ifelse(y < 0, 0, y))
@@ -168,14 +169,14 @@ modeler <- function(data,
     if (!all(nam_fix_params %in% args)) {
       stop("All fixed_params must be in:", fn)
     }
-    if (length(args) - length(nam_fix_params) <= 1 ) {
+    if (length(args) - length(nam_fix_params) <= 1) {
       stop("More than one parameter needs to be free.")
     }
   }
   # Validate initial values
   if (is.null(parameters)) {
     stop("Initial parameters need to be provided.")
-  } else if (is.numeric(parameters)) {
+  } else if (is.numeric(parameters)) { # Numeric Vector
     if (!sum(names(parameters) %in% args) == length(args)) {
       stop("names of parameters have to be in: ", fn)
     }
@@ -186,7 +187,7 @@ modeler <- function(data,
       pivot_longer(cols = -c(uid), names_to = "coef") |>
       nest_by(uid, .key = "initials") |>
       mutate(initials = list(pull(initials, value, coef)))
-  } else if ("data.frame" %in% class(parameters)) {
+  } else if ("data.frame" %in% class(parameters)) { # Data.frame
     nam_ini_vals <- colnames(parameters)
     if (!"uid" %in% nam_ini_vals) {
       stop("parameters should contain columns 'uid'.")
@@ -198,6 +199,29 @@ modeler <- function(data,
       pivot_longer(cols = -c(uid), names_to = "coef") |>
       nest_by(uid, .key = "initials") |>
       mutate(initials = list(pull(initials, value, coef)))
+  } else if ("list" %in% class(parameters)) { # List
+    if (!sum(names(parameters) %in% args) == length(args)) {
+      stop("parameters should have the same parameters as the function: ", fn)
+    }
+    init <- dt |>
+      select(uid, x, y) |>
+      group_by(uid)
+    for (j in names(parameters)) {
+      str <- parameters[[j]]
+      if ("numeric" %in% class(str)) {
+        express <- str
+      } else if ("character" %in% class(str)) {
+        express <- rlang::parse_expr(str)
+      }
+      init <- mutate(init, "{j}" := !!express)
+    }
+    init <- init |>
+      ungroup() |>
+      select(uid, all_of(names(parameters))) |>
+      unique.data.frame() |>
+      pivot_longer(cols = -c(uid), names_to = "coef") |>
+      nest_by(uid, .key = "initials") |>
+      mutate(initials = list(pull(initials, value, coef)))
   }
   # Merging with fixed parameters
   if (!is.null(fixed_params)) {
@@ -206,11 +230,6 @@ modeler <- function(data,
         pivot_longer(cols = -c(uid), names_to = "coef") |>
         nest_by(uid, .key = "fx_params") |>
         mutate(fx_params = list(pull(fx_params, value, coef)))
-      init <- init |>
-        full_join(fixed, by = c("uid")) |>
-        mutate(
-          initials = list(initials[!names(initials) %in% names(fixed_params)])
-        )
     } else if ("list" %in% class(fixed_params)) {
       fixed <- dt |>
         select(uid, x, y) |>
@@ -231,12 +250,12 @@ modeler <- function(data,
         pivot_longer(cols = -c(uid), names_to = "coef") |>
         nest_by(uid, .key = "fx_params") |>
         mutate(fx_params = list(pull(fx_params, value, coef)))
-      init <- init |>
-        full_join(fixed, by = c("uid")) |>
-        mutate(
-          initials = list(initials[!names(initials) %in% names(fixed_params)])
-        )
     }
+    init <- init |>
+      full_join(fixed, by = c("uid")) |>
+      mutate(
+        initials = list(initials[!names(initials) %in% names(fixed_params)])
+      )
   } else {
     fixed <- dt |>
       select(uid) |>
@@ -251,7 +270,7 @@ modeler <- function(data,
     fixed <- droplevels(filter(fixed, uid %in% subset))
   }
   dt_nest <- dt |>
-    nest_by(uid, across(all_of(.keep))) |>
+    nest_by(uid, across(all_of(metadata))) |>
     full_join(init, by = c("uid"))
   if (nrow(dt_nest) == 0) {
     stop("Check the ids for which you are filtering.")
@@ -296,7 +315,7 @@ modeler <- function(data,
       upper = upper,
       trace = trace,
       control = control,
-      .keep = .keep
+      metadata = metadata
     )
   }
   end_time <- Sys.time()
@@ -340,7 +359,7 @@ modeler <- function(data,
     execution = end_time - init_time,
     response = variable,
     x_var = x$x_var,
-    keep = .keep,
+    keep = metadata,
     fun = fn,
     parallel = list("parallel" = parallel, "workers" = workers),
     fit = objt
@@ -392,7 +411,7 @@ modeler <- function(data,
                           lower,
                           upper,
                           control,
-                          .keep,
+                          metadata,
                           trace) {
   dt <- data[data$uid == id, ]
   initials <- unlist(dt$initials)
@@ -415,7 +434,7 @@ modeler <- function(data,
   )
   # metadata
   rr <- cbind(
-    dt[, c("uid", .keep)],
+    dt[, c("uid", metadata)],
     kkopt |>
       tibble::rownames_to_column(var = "method") |>
       dplyr::rename(sse = value) |>
@@ -459,9 +478,9 @@ modeler <- function(data,
 }
 
 #' @noRd
-max_as_last <- function(data, .keep) {
+max_as_last <- function(data, metadata) {
   dt_can <- data |>
-    group_by(uid, across(all_of(.keep))) |>
+    group_by(uid, across(all_of(metadata))) |>
     mutate(
       loc_max_at = paste(local_min_max(y, x)$days_max, collapse = "_"),
       loc_max = as.numeric(local_min_max(y, x)$days_max[1])
diff --git a/R/utils_S3_plots.R b/R/utils_S3_plots.R
index 9e2fa0c..e52389c 100644
--- a/R/utils_S3_plots.R
+++ b/R/utils_S3_plots.R
@@ -373,7 +373,7 @@ plot.explorer <- function(x,
                           base_size = 13,
                           return_gg = FALSE,
                           add_avg = FALSE, ...) {
-  .keep <- x$metadata
+  metadata <- x$metadata
   colours <- c("#db4437", "white", "#4285f4")
   flt <- x$summ_vars |>
     filter(`miss%` <= 0.2) |> #  & SD > 0
@@ -399,7 +399,7 @@ plot.explorer <- function(x,
     }
     var_by_x <- data |>
       pivot_wider(names_from = var, values_from = y) |>
-      select(-c(uid, all_of(.keep))) |>
+      select(-c(uid, all_of(metadata))) |>
       nest_by(x) |>
       mutate(
         mat = list(
@@ -453,7 +453,7 @@ plot.explorer <- function(x,
   if (type == "x_by_var" || type == 2) {
     x_by_var <- data |>
       pivot_wider(names_from = x, values_from = y) |>
-      select(-c(uid, all_of(.keep))) |>
+      select(-c(uid, all_of(metadata))) |>
       nest_by(var) |>
       mutate(
         mat = list(
diff --git a/man/dot-fitter_curve.Rd b/man/dot-fitter_curve.Rd
index 90d319d..a5b3521 100644
--- a/man/dot-fitter_curve.Rd
+++ b/man/dot-fitter_curve.Rd
@@ -4,7 +4,7 @@
 \alias{.fitter_curve}
 \title{General-purpose optimization}
 \usage{
-.fitter_curve(data, id, fn, method, lower, upper, control, .keep, trace)
+.fitter_curve(data, id, fn, method, lower, upper, control, metadata, trace)
 }
 \arguments{
 \item{data}{A nested data.frame with columns <plot, genotype, row, range, data, initials, fx_params>.}
diff --git a/man/modeler.Rd b/man/modeler.Rd
index 4991c93..bc2981c 100644
--- a/man/modeler.Rd
+++ b/man/modeler.Rd
@@ -35,8 +35,9 @@ modeler(
 \item{fn}{A string specifying the name of the function to be used for the curve fitting. Default is \code{"fn_linear_sat"}.}
 
 \item{parameters}{Can be a named numeric vector specifying the initial values for the parameters to be optimized,
-or a data frame with columns \code{uid}, and the initial parameter values for each group id. Used for providing specific
-initial values per group id. Default is \code{NULL}.}
+If a data frame it needs to have a column \code{uid}, and the initial parameter values for each group id.
+If list it needs to be a named list with the initial parameters and numeric or string values can be used (e.g. list(k = "max(y)", t1 = 40)).
+Used for providing specific initial values per group id. Default is \code{NULL}.}
 
 \item{lower}{Numeric vector specifying the lower bounds for the parameters. Default is \code{-Inf} for all parameters.}