diff --git a/R/adaptation.R b/R/adaptation.R
index 2b81ced..ec120b8 100644
--- a/R/adaptation.R
+++ b/R/adaptation.R
@@ -9,8 +9,8 @@
 #' @param kappa Decay rate exponent in `[0.5, 1]` for adaptation learning rate.
 #'
 #' @return List of functions with entries
-#' * `initialize`, a function for initializing adapter state at beginning of
-#'   chain,
+#' * `initialize`, a function for initializing adapter state and proposal
+#'   parameters at beginning of chain,
 #' * `update` a function for updating adapter state and proposal parameters on
 #'   each chain iteration,
 #' * `finalize` a function for performing any final updates to adapter state and
@@ -27,24 +27,22 @@
 #'   grad_log_density = function(x) -x
 #' )
 #' proposal <- barker_proposal(target_distribution)
-#' adapter <- scale_adapter(
-#'   proposal,
-#'   initial_scale = 1., target_accept_prob = 0.4
-#' )
+#' adapter <- scale_adapter(initial_scale = 1., target_accept_prob = 0.4)
+#' adapter$initialize(proposal, chain_state(c(0, 0)))
 scale_adapter <- function(
-    proposal, initial_scale = NULL, target_accept_prob = NULL, kappa = 0.6) {
+    initial_scale = NULL, target_accept_prob = NULL, kappa = 0.6) {
   log_scale <- NULL
-  if (is.null(target_accept_prob)) {
-    target_accept_prob <- proposal$default_target_accept_prob()
-  }
-  initialize <- function(initial_state) {
+  initialize <- function(proposal, initial_state) {
     if (is.null(initial_scale)) {
       initial_scale <- proposal$default_initial_scale(initial_state$dimension())
     }
     log_scale <<- log(initial_scale)
     proposal$update(scale = initial_scale)
   }
-  update <- function(sample_index, state_and_statistics) {
+  update <- function(proposal, sample_index, state_and_statistics) {
+    if (is.null(target_accept_prob)) {
+      target_accept_prob <- proposal$default_target_accept_prob()
+    }
     gamma <- sample_index^(-kappa)
     accept_prob <- state_and_statistics$statistics$accept_prob
     log_scale <<- log_scale + gamma * (accept_prob - target_accept_prob)
@@ -53,7 +51,7 @@ scale_adapter <- function(
   list(
     initialize = initialize,
     update = update,
-    finalize = function() {},
+    finalize = NULL,
     state = function() list(log_scale = log_scale)
   )
 }
@@ -74,15 +72,16 @@ scale_adapter <- function(
 #'   grad_log_density = function(x) -x
 #' )
 #' proposal <- barker_proposal(target_distribution)
-#' adapter <- variance_adapter(proposal)
-variance_adapter <- function(proposal, kappa = 0.6) {
+#' adapter <- variance_adapter()
+#' adapter$initialize(proposal, chain_state(c(0, 0)))
+variance_adapter <- function(kappa = 0.6) {
   mean_estimate <- NULL
   variance_estimate <- NULL
-  initialize <- function(initial_state) {
+  initialize <- function(proposal, initial_state) {
     mean_estimate <<- initial_state$position()
     variance_estimate <<- rep(1., initial_state$dimension())
   }
-  update <- function(sample_index, state_and_statistics) {
+  update <- function(proposal, sample_index, state_and_statistics) {
     gamma <- sample_index^(-kappa)
     position <- state_and_statistics$state$position()
     mean_estimate <<- mean_estimate + gamma * (position - mean_estimate)
@@ -124,20 +123,23 @@ variance_adapter <- function(proposal, kappa = 0.6) {
 #'   grad_log_density = function(x) -x
 #' )
 #' proposal <- barker_proposal(target_distribution)
-#' adapter <- robust_shape_adapter(
-#'   proposal,
-#'   initial_scale = 1.,
-#'   target_accept_prob = 0.4
-#' )
+#' adapter <- robust_shape_adapter(initial_scale = 1., target_accept_prob = 0.4)
+#' adapter$initialize(proposal, chain_state(c(0, 0)))
 robust_shape_adapter <- function(
-    proposal, initial_scale, target_accept_prob = 0.4, kappa = 0.6) {
+    initial_scale = NULL, target_accept_prob = NULL, kappa = 0.6) {
   rlang::check_installed("ramcmc", reason = "to use this function")
   shape <- NULL
-  initialize <- function(initial_state) {
+  initialize <- function(proposal, initial_state) {
+    if (is.null(initial_scale)) {
+      initial_scale <- proposal$default_initial_scale(initial_state$dimension())
+    }
     shape <<- initial_scale * diag(initial_state$dimension())
     proposal$update(shape = shape)
   }
-  update <- function(sample_index, state_and_statistics) {
+  update <- function(proposal, sample_index, state_and_statistics) {
+    if (is.null(target_accept_prob)) {
+      target_accept_prob <- proposal$default_target_accept_prob()
+    }
     momentum <- state_and_statistics$proposed_state$momentum()
     accept_prob <- state_and_statistics$statistics$accept_prob
     shape <<- ramcmc::adapt_S(
diff --git a/R/chains.R b/R/chains.R
index b164924..7abbf80 100644
--- a/R/chains.R
+++ b/R/chains.R
@@ -163,7 +163,7 @@ chain_loop <- function(
     statistic_names) {
   progress_bar <- get_progress_bar(use_progress_bar, n_iteration, stage_name)
   for (adapter in adapters) {
-    adapter$initialize(state)
+    adapter$initialize(proposal, state)
   }
   if (record_traces_and_statistics) {
     trace_names <- names(unlist(trace_function(state)))
@@ -176,25 +176,25 @@ chain_loop <- function(
     traces <- NULL
     statistics <- NULL
   }
-  for (s in seq_len(n_iteration)) {
+  for (chain_iteration in seq_len(n_iteration)) {
     state_and_statistics <- sample_metropolis_hastings(
       state, target_distribution, proposal
     )
     for (adapter in adapters) {
-      adapter$update(s + 1, state_and_statistics)
+      adapter$update(proposal, chain_iteration + 1, state_and_statistics)
     }
     state <- state_and_statistics$state
     if (record_traces_and_statistics) {
-      traces[s, ] <- unlist(trace_function(state))
+      traces[chain_iteration, ] <- unlist(trace_function(state))
       adapter_states <- lapply(adapters, function(a) a$state())
-      statistics[s, ] <- unlist(
+      statistics[chain_iteration, ] <- unlist(
         c(state_and_statistics$statistics, adapter_states)
       )
     }
     if (!is.null(progress_bar)) progress_bar$tick()
   }
   for (adapter in adapters) {
-    if (!is.null(adapter$finalize)) adapter$finalize()
+    if (!is.null(adapter$finalize)) adapter$finalize(proposal)
   }
   list(final_state = state, traces = traces, statistics = statistics)
 }
diff --git a/README.Rmd b/README.Rmd
index 424d19a..ad1f428 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -59,7 +59,7 @@ results <- sample_chain(
   initial_state = rnorm(dimension),
   n_warm_up_iteration = 1000,
   n_main_iteration = 1000,
-  adapters = list(scale_adapter(proposal), variance_adapter(proposal))
+  adapters = list(scale_adapter(), variance_adapter())
 )
 mean_accept_prob <- mean(results$statistics[, "accept_prob"])
 adapted_shape <- proposal$parameters()$shape
diff --git a/README.md b/README.md
index 6c07526..6352590 100644
--- a/README.md
+++ b/README.md
@@ -56,7 +56,7 @@ results <- sample_chain(
   initial_state = rnorm(dimension),
   n_warm_up_iteration = 1000,
   n_main_iteration = 1000,
-  adapters = list(scale_adapter(proposal), variance_adapter(proposal))
+  adapters = list(scale_adapter(), variance_adapter())
 )
 mean_accept_prob <- mean(results$statistics[, "accept_prob"])
 adapted_shape <- proposal$parameters()$shape
diff --git a/man/robust_shape_adapter.Rd b/man/robust_shape_adapter.Rd
index 9f8cac4..ea0fa9e 100644
--- a/man/robust_shape_adapter.Rd
+++ b/man/robust_shape_adapter.Rd
@@ -6,16 +6,12 @@
 Metropolis algorithm of Vihola (2012).}
 \usage{
 robust_shape_adapter(
-  proposal,
-  initial_scale,
-  target_accept_prob = 0.4,
+  initial_scale = NULL,
+  target_accept_prob = NULL,
   kappa = 0.6
 )
 }
 \arguments{
-\item{proposal}{Proposal object to adapt. Must define an \code{update} function
-which accepts a parameter \code{scale} for setting scale parameter of proposal.}
-
 \item{initial_scale}{Initial value to use for scale parameter. If not set
 explicitly a proposal and dimension dependent default will be used.}
 
@@ -27,8 +23,8 @@ chain. If not set a proposal dependent default will be used.}
 \value{
 List of functions with entries
 \itemize{
-\item \code{initialize}, a function for initializing adapter state at beginning of
-chain,
+\item \code{initialize}, a function for initializing adapter state and proposal
+parameters at beginning of chain,
 \item \code{update} a function for updating adapter state and proposal parameters on
 each chain iteration,
 \item \code{finalize} a function for performing any final updates to adapter state and
@@ -47,11 +43,8 @@ target_distribution <- list(
   grad_log_density = function(x) -x
 )
 proposal <- barker_proposal(target_distribution)
-adapter <- robust_shape_adapter(
-  proposal,
-  initial_scale = 1.,
-  target_accept_prob = 0.4
-)
+adapter <- robust_shape_adapter(initial_scale = 1., target_accept_prob = 0.4)
+adapter$initialize(proposal, chain_state(c(0, 0)))
 }
 \references{
 Vihola, M. (2012). Robust adaptive Metropolis algorithm with
diff --git a/man/scale_adapter.Rd b/man/scale_adapter.Rd
index 9943a85..76fc84f 100644
--- a/man/scale_adapter.Rd
+++ b/man/scale_adapter.Rd
@@ -4,17 +4,9 @@
 \alias{scale_adapter}
 \title{Create object to adapt proposal scale to coerce average acceptance rate.}
 \usage{
-scale_adapter(
-  proposal,
-  initial_scale = NULL,
-  target_accept_prob = NULL,
-  kappa = 0.6
-)
+scale_adapter(initial_scale = NULL, target_accept_prob = NULL, kappa = 0.6)
 }
 \arguments{
-\item{proposal}{Proposal object to adapt. Must define an \code{update} function
-which accepts a parameter \code{scale} for setting scale parameter of proposal.}
-
 \item{initial_scale}{Initial value to use for scale parameter. If not set
 explicitly a proposal and dimension dependent default will be used.}
 
@@ -22,12 +14,15 @@ explicitly a proposal and dimension dependent default will be used.}
 chain. If not set a proposal dependent default will be used.}
 
 \item{kappa}{Decay rate exponent in \verb{[0.5, 1]} for adaptation learning rate.}
+
+\item{proposal}{Proposal object to adapt. Must define an \code{update} function
+which accepts a parameter \code{scale} for setting scale parameter of proposal.}
 }
 \value{
 List of functions with entries
 \itemize{
-\item \code{initialize}, a function for initializing adapter state at beginning of
-chain,
+\item \code{initialize}, a function for initializing adapter state and proposal
+parameters at beginning of chain,
 \item \code{update} a function for updating adapter state and proposal parameters on
 each chain iteration,
 \item \code{finalize} a function for performing any final updates to adapter state and
@@ -46,8 +41,6 @@ target_distribution <- list(
   grad_log_density = function(x) -x
 )
 proposal <- barker_proposal(target_distribution)
-adapter <- scale_adapter(
-  proposal,
-  initial_scale = 1., target_accept_prob = 0.4
-)
+adapter <- scale_adapter(initial_scale = 1., target_accept_prob = 0.4)
+adapter$initialize(proposal, chain_state(c(0, 0)))
 }
diff --git a/man/variance_adapter.Rd b/man/variance_adapter.Rd
index b6cee22..fadc395 100644
--- a/man/variance_adapter.Rd
+++ b/man/variance_adapter.Rd
@@ -5,19 +5,19 @@
 \title{Create object to adapt proposal with per dimension scales based on estimates
 of target distribution variances.}
 \usage{
-variance_adapter(proposal, kappa = 0.6)
+variance_adapter(kappa = 0.6)
 }
 \arguments{
+\item{kappa}{Decay rate exponent in \verb{[0.5, 1]} for adaptation learning rate.}
+
 \item{proposal}{Proposal object to adapt. Must define an \code{update} function
 which accepts a parameter \code{shape} for setting shape parameter of proposal.}
-
-\item{kappa}{Decay rate exponent in \verb{[0.5, 1]} for adaptation learning rate.}
 }
 \value{
 List of functions with entries
 \itemize{
-\item \code{initialize}, a function for initializing adapter state at beginning of
-chain,
+\item \code{initialize}, a function for initializing adapter state and proposal
+parameters at beginning of chain,
 \item \code{update} a function for updating adapter state and proposal parameters on
 each chain iteration,
 \item \code{finalize} a function for performing any final updates to adapter state and
@@ -37,5 +37,6 @@ target_distribution <- list(
   grad_log_density = function(x) -x
 )
 proposal <- barker_proposal(target_distribution)
-adapter <- variance_adapter(proposal)
+adapter <- variance_adapter()
+adapter$initialize(proposal, chain_state(c(0, 0)))
 }
diff --git a/tests/testthat/test-adaptation.R b/tests/testthat/test-adaptation.R
index 9eed6c8..a689db5 100644
--- a/tests/testthat/test-adaptation.R
+++ b/tests/testthat/test-adaptation.R
@@ -24,7 +24,8 @@ dummy_proposal_with_scale_parameter <- function(scale = NULL) {
 dummy_proposal_with_shape_parameter <- function(shape = NULL) {
   list(
     update = function(shape) shape <<- shape,
-    parameters = function() list(shape = shape)
+    parameters = function() list(shape = shape),
+    default_initial_scale = function(dimension) 1 / sqrt(dimension)
   )
 }
 
@@ -40,23 +41,22 @@ for (target_accept_prob in c(0.2, 0.4, 0.6)) {
           target_accept_prob, initial_scale, kappa
         ),
         {
-          proposal <- dummy_proposal_with_scale_parameter()
           adapter <- scale_adapter(
-            proposal = proposal,
             initial_scale = initial_scale,
             target_accept_prob = target_accept_prob,
             kappa = kappa
           )
           check_adapter(adapter)
-          adapter$initialize(initial_state = chain_state(rep(0, dimension)))
+          proposal <- dummy_proposal_with_scale_parameter()
+          adapter$initialize(proposal, chain_state(rep(0, dimension)))
           adapter_state <- adapter$state()
           expect_named(adapter_state, "log_scale")
           expect_length(adapter_state$log_scale, 1)
           old_scale <- initial_scale
           # If accept probability higher than target scale should be increased
-          for (s in 1:2) {
+          for (sample_index in 1:2) {
             adapter$update(
-              s, list(statistics = list(accept_prob = target_accept_prob + 0.1))
+              proposal, sample_index, list(statistics = list(accept_prob = target_accept_prob + 0.1))
             )
             expect_type(adapter$state(), "list")
             scale <- proposal$parameters()$scale
@@ -64,9 +64,9 @@ for (target_accept_prob in c(0.2, 0.4, 0.6)) {
             old_scale <- scale
           }
           # If accept probability lower than target scale should be decreased
-          for (s in 3:4) {
+          for (sample_index in 3:4) {
             adapter$update(
-              s, list(statistics = list(accept_prob = target_accept_prob - 0.1))
+              proposal, sample_index, list(statistics = list(accept_prob = target_accept_prob - 0.1))
             )
             scale <- proposal$parameters()$scale
             expect_lt(scale, old_scale)
@@ -75,10 +75,10 @@ for (target_accept_prob in c(0.2, 0.4, 0.6)) {
           # For a smooth decreasing relation between accept probability and
           # scale should adapt over long run to give close to target accept
           # probability
-          for (s in 5:2000) {
+          for (sample_index in 5:2000) {
             accept_prob <- exp(-scale)
             adapter$update(
-              s, list(statistics = list(accept_prob = accept_prob))
+              proposal, sample_index, list(statistics = list(accept_prob = accept_prob))
             )
             scale <- proposal$parameters()$scale
           }
@@ -96,10 +96,10 @@ for (dimension in c(1L, 2L, 5L)) {
       dimension
     ),
     {
-      proposal <- dummy_proposal_with_scale_parameter()
-      adapter <- scale_adapter(proposal)
+      adapter <- scale_adapter()
       check_adapter(adapter)
-      adapter$initialize(initial_state = chain_state(rep(0, dimension)))
+      proposal <- dummy_proposal_with_scale_parameter()
+      adapter$initialize(proposal, chain_state(rep(0, dimension)))
       adapter_state <- adapter$state()
       expect_named(adapter_state, "log_scale")
       expect_length(adapter_state$log_scale, 1)
@@ -121,13 +121,13 @@ for (dimension in c(1L, 2L, 5L)) {
         ),
         {
           proposal <- dummy_proposal_with_shape_parameter()
-          adapter <- variance_adapter(proposal = proposal, kappa = kappa)
+          adapter <- variance_adapter(kappa = kappa)
           check_adapter(adapter)
           withr::local_seed(default_seed())
           target_scales <- exp(2 * rnorm(dimension))
           position <- rnorm(dimension) * target_scales
           state <- chain_state(position = position)
-          adapter$initialize(state)
+          adapter$initialize(proposal, state)
           adapter_state <- adapter$state()
           expect_named(
             adapter_state,
@@ -146,7 +146,7 @@ for (dimension in c(1L, 2L, 5L)) {
                 + sqrt(1 - correlation^2) * target_scales * rnorm(dimension)
             )
             state$update(position = position)
-            adapter$update(s, list(state = state))
+            adapter$update(proposal, s, list(state = state))
           }
           expect_equal(
             proposal$parameters()$shape,
@@ -178,14 +178,13 @@ for (dimension in c(1L, 2L, 3L)) {
         )
         proposal <- random_walk_proposal(target_distribution)
         adapter <- robust_shape_adapter(
-          proposal = proposal,
           initial_scale = 1.,
           kappa = 2 / 3,
           target_accept_prob = target_accept_prob
         )
         check_adapter(adapter)
         state <- chain_state(position = rnorm(dimension))
-        adapter$initialize(state)
+        adapter$initialize(proposal, state)
         adapter_state <- adapter$state()
         expect_named(adapter_state, "shape")
         expect_nrow(adapter_state$shape, dimension)
@@ -195,7 +194,7 @@ for (dimension in c(1L, 2L, 3L)) {
           state_and_statistics <- sample_metropolis_hastings(
             state, target_distribution, proposal
           )
-          adapter$update(sample_index, state_and_statistics)
+          adapter$update(proposal, sample_index, state_and_statistics)
           state <- state_and_statistics$state
           mean_accept_prob <- mean_accept_prob + (
             state_and_statistics$statistics$accept_prob - mean_accept_prob
@@ -215,3 +214,23 @@ for (dimension in c(1L, 2L, 3L)) {
     )
   }
 }
+
+for (dimension in c(1L, 2L, 5L)) {
+  test_that(
+    sprintf(
+      "Robust shape adapter with only proposal specified works in dimension %i",
+      dimension
+    ),
+    {
+      adapter <- robust_shape_adapter()
+      check_adapter(adapter)
+      proposal <- dummy_proposal_with_shape_parameter()
+      adapter$initialize(proposal, chain_state(rep(0, dimension)))
+      adapter_state <- adapter$state()
+      expect_named(adapter_state, "shape")
+      expect_nrow(adapter_state$shape, dimension)
+      expect_ncol(adapter_state$shape, dimension)
+      expect_equal(adapter_state$shape, diag(dimension) / sqrt(dimension))
+    }
+  )
+}
diff --git a/tests/testthat/test-chains.R b/tests/testthat/test-chains.R
index 1e1cebc..727f8fc 100644
--- a/tests/testthat/test-chains.R
+++ b/tests/testthat/test-chains.R
@@ -23,9 +23,7 @@ for (n_warm_up_iteration in c(0, 1, 10)) {
                 target_distribution <- standard_normal_target_distribution()
                 barker_proposal(target_distribution)
                 proposal <- barker_proposal(target_distribution)
-                adapters <- list(
-                  scale_adapter(proposal, initial_scale = 1.)
-                )
+                adapters <- list(scale_adapter(initial_scale = 1.))
                 withr::with_seed(default_seed(), {
                   position <- rnorm(dimension)
                 })