diff --git a/.JuliaFormatter.toml b/.JuliaFormatter.toml
new file mode 100644
index 00000000..1e72b507
--- /dev/null
+++ b/.JuliaFormatter.toml
@@ -0,0 +1 @@
+style="blue"
diff --git a/.github/workflows/Format.yml b/.github/workflows/Format.yml
new file mode 100644
index 00000000..40d66912
--- /dev/null
+++ b/.github/workflows/Format.yml
@@ -0,0 +1,28 @@
+name: Format
+
+on:
+  push:
+    branches:
+      - master
+  pull_request:
+
+jobs:
+  format:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - uses: julia-actions/setup-julia@latest
+        with:
+          version: 1
+      - name: Format code
+        run: |
+          using Pkg
+          Pkg.add(; name="JuliaFormatter", uuid="98e50ef6-434e-11e9-1051-2b60c6c9e899")
+          using JuliaFormatter
+          format("."; verbose=true)
+        shell: julia --color=yes {0}
+      - uses: reviewdog/action-suggester@v1
+        if: github.event_name == 'pull_request'
+        with:
+          tool_name: JuliaFormatter
+          fail_on_error: true
diff --git a/README.md b/README.md
index 6fe8d052..e0b5da75 100644
--- a/README.md
+++ b/README.md
@@ -3,6 +3,7 @@
 [![Build Status](https://github.com/TuringLang/AdvancedPS.jl/workflows/CI/badge.svg?branch=master)](https://github.com/TuringLang/AdvancedPS.jl/actions?query=workflow%3ACI%20branch%3Amaster)
 [![Coverage](https://codecov.io/gh/TuringLang/AdvancedPS.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/TuringLang/AdvancedPS.jl)
 [![Coverage](https://coveralls.io/repos/github/TuringLang/AdvancedPS.jl/badge.svg?branch=master)](https://coveralls.io/github/TuringLang/AdvancedPS.jl?branch=master)
+[![Code Style: Blue](https://img.shields.io/badge/code%20style-blue-4495d1.svg)](https://github.com/invenia/BlueStyle)
 
 ### Reference
 
diff --git a/src/AdvancedPS.jl b/src/AdvancedPS.jl
index 95d0974b..ce2ecf18 100644
--- a/src/AdvancedPS.jl
+++ b/src/AdvancedPS.jl
@@ -1,10 +1,10 @@
 module AdvancedPS
 
-import AbstractMCMC
-import Distributions
-import Libtask
-import Random
-import StatsFuns
+using AbstractMCMC: AbstractMCMC
+using Distributions: Distributions
+using Libtask: Libtask
+using Random: Random
+using StatsFuns: StatsFuns
 
 include("resampling.jl")
 include("container.jl")
diff --git a/src/container.jl b/src/container.jl
index 7fdaf230..bb0fe76f 100644
--- a/src/container.jl
+++ b/src/container.jl
@@ -6,7 +6,7 @@ end
 const Particle = Trace
 
 function Trace(f)
-    ctask = let f=f
+    ctask = let f = f
         Libtask.CTask() do
             res = f()
             Libtask.produce(nothing)
@@ -34,7 +34,7 @@ reset_model(f) = deepcopy(f)
 delete_retained!(f) = nothing
 
 # Task copying version of fork for Trace.
-function fork(trace::Trace, isref::Bool = false)
+function fork(trace::Trace, isref::Bool=false)
     newtrace = copy(trace)
     isref && delete_retained!(newtrace.f)
 
@@ -48,7 +48,7 @@ end
 # Create new task and copy randomness
 function forkr(trace::Trace)
     newf = reset_model(trace.f)
-    ctask = let f=trace.ctask.task.code
+    ctask = let f = trace.ctask.task.code
         Libtask.CTask() do
             res = f()
             Libtask.produce(nothing)
@@ -105,7 +105,7 @@ end
 function Base.push!(pc::ParticleContainer, p::Particle)
     push!(pc.vals, p)
     push!(pc.logWs, 0.0)
-    pc
+    return pc
 end
 
 # clones a theta-particle
@@ -116,7 +116,7 @@ function Base.copy(pc::ParticleContainer)
     # copy weights
     logWs = copy(pc.logWs)
 
-    ParticleContainer(vals, logWs)
+    return ParticleContainer(vals, logWs)
 end
 
 """
@@ -183,9 +183,9 @@ of the particle `weights`. For Particle Gibbs sampling, one can provide a refere
 function resample_propagate!(
     rng::Random.AbstractRNG,
     pc::ParticleContainer,
-    randcat = resample_systematic,
-    ref::Union{Particle, Nothing} = nothing;
-    weights = getweights(pc)
+    randcat=resample_systematic,
+    ref::Union{Particle,Nothing}=nothing;
+    weights=getweights(pc),
 )
     # check that weights are not NaN
     @assert !any(isnan, weights)
@@ -232,24 +232,24 @@ function resample_propagate!(
     pc.vals = children
     reset_logweights!(pc)
 
-    pc
+    return pc
 end
 
 function resample_propagate!(
     rng::Random.AbstractRNG,
     pc::ParticleContainer,
     resampler::ResampleWithESSThreshold,
-    ref::Union{Particle,Nothing} = nothing;
-    weights = getweights(pc)
+    ref::Union{Particle,Nothing}=nothing;
+    weights=getweights(pc),
 )
     # Compute the effective sample size ``1 / ∑ wᵢ²`` with normalized weights ``wᵢ``
     ess = inv(sum(abs2, weights))
 
     if ess ≤ resampler.threshold * length(pc)
-        resample_propagate!(rng, pc, resampler.resampler, ref; weights = weights)
+        resample_propagate!(rng, pc, resampler.resampler, ref; weights=weights)
     end
 
-    pc
+    return pc
 end
 
 """
@@ -290,9 +290,13 @@ function reweight!(pc::ParticleContainer)
 
     # The posterior for models with random number of observations is not well-defined.
     if numdone != 0
-        error("mis-aligned execution traces: # particles = ", n,
-              " # completed trajectories = ", numdone,
-              ". Please make sure the number of observations is NOT random.")
+        error(
+            "mis-aligned execution traces: # particles = ",
+            n,
+            " # completed trajectories = ",
+            numdone,
+            ". Please make sure the number of observations is NOT random.",
+        )
     end
 
     return false
diff --git a/src/contrib/AdvancedSMCExtensions.jl b/src/contrib/AdvancedSMCExtensions.jl
index 921662b6..243c217e 100644
--- a/src/contrib/AdvancedSMCExtensions.jl
+++ b/src/contrib/AdvancedSMCExtensions.jl
@@ -26,12 +26,12 @@ Arguments:
 - `parameters_algs::Tuple{MH}` : An [`MH`](@ref) algorithm, which includes a
 sample space specification.
 """
-mutable struct PMMH{space, A<:Tuple} <: InferenceAlgorithm
+mutable struct PMMH{space,A<:Tuple} <: InferenceAlgorithm
     n_iters::Int               # number of iterations
     algs::A                 # Proposals for state & parameters
 end
 function PMMH(n_iters::Int, algs::Tuple, space::Tuple)
-    return PMMH{space, typeof(algs)}(n_iters, algs)
+    return PMMH{space,typeof(algs)}(n_iters, algs)
 end
 function PMMH(n_iters::Int, smc_alg::SMC, parameter_algs...)
     return PMMH(n_iters, tuple(parameter_algs..., smc_alg), ())
@@ -40,7 +40,7 @@ end
 PIMH(n_iters::Int, smc_alg::SMC) = PMMH(n_iters, tuple(smc_alg), ())
 
 function Sampler(alg::PMMH, model::Model, s::Selector)
-    info = Dict{Symbol, Any}()
+    info = Dict{Symbol,Any}()
     spl = Sampler(alg, info, s)
 
     alg_str = "PMMH"
@@ -51,15 +51,15 @@ function Sampler(alg::PMMH, model::Model, s::Selector)
 
     for i in 1:n_samplers
         sub_alg = alg.algs[i]
-        if isa(sub_alg, Union{SMC, MH})
+        if isa(sub_alg, Union{SMC,MH})
             samplers[i] = Sampler(sub_alg, model, Selector(Symbol(typeof(sub_alg))))
         else
             error("[$alg_str] unsupport base sampling algorithm $alg")
         end
         if typeof(sub_alg) == MH && sub_alg.n_iters != 1
             warn(
-                "[$alg_str] number of iterations greater than 1" * 
-                "is useless for MH since it is only used for its proposal"
+                "[$alg_str] number of iterations greater than 1" *
+                "is useless for MH since it is only used for its proposal",
             )
         end
         space = union(space, sub_alg.space)
@@ -80,10 +80,13 @@ function step(model, spl::Sampler{<:PMMH}, vi::VarInfo, is_first::Bool)
     old_θ = copy(vi[spl])
 
     @debug "Propose new parameters from proposals..."
-    for local_spl in spl.info[:samplers][1:end-1]
+    for local_spl in spl.info[:samplers][1:(end - 1)]
         @debug "$(typeof(local_spl)) proposing $(local_spl.alg.space)..."
         propose(model, local_spl, vi)
-        if local_spl.info[:violating_support] violating_support=true; break end
+        if local_spl.info[:violating_support]
+            violating_support = true
+            break
+        end
         new_prior_prob += local_spl.info[:prior_prob]
         proposal_ratio += local_spl.info[:proposal_ratio]
     end
@@ -98,9 +101,9 @@ function step(model, spl::Sampler{<:PMMH}, vi::VarInfo, is_first::Bool)
 
         @debug "Decide whether to accept..."
         accepted = mh_accept(
-          spl.info[:old_likelihood_estimate] + spl.info[:old_prior_prob],
-          new_likelihood_estimate + new_prior_prob,
-          proposal_ratio,
+            spl.info[:old_likelihood_estimate] + spl.info[:old_prior_prob],
+            new_likelihood_estimate + new_prior_prob,
+            proposal_ratio,
         )
     end
 
@@ -114,13 +117,13 @@ function step(model, spl::Sampler{<:PMMH}, vi::VarInfo, is_first::Bool)
     return vi, accepted
 end
 
-function sample(  model::Model,
-                  alg::PMMH;
-                  save_state=false,         # flag for state saving
-                  resume_from=nothing,      # chain to continue
-                  reuse_spl_n=0             # flag for spl re-using
-                )
-
+function sample(
+    model::Model,
+    alg::PMMH;
+    save_state=false,         # flag for state saving
+    resume_from=nothing,      # chain to continue
+    reuse_spl_n=0,             # flag for spl re-using
+)
     spl = Sampler(alg, model)
     if resume_from !== nothing
         spl.selector = resume_from.info[:spl].selector
@@ -134,8 +137,8 @@ function sample(  model::Model,
     time_total = zero(Float64)
     samples = Array{Sample}(undef, sample_n)
     weight = 1 / sample_n
-    for i = 1:sample_n
-        samples[i] = Sample(weight, Dict{Symbol, Any}())
+    for i in 1:sample_n
+        samples[i] = Sample(weight, Dict{Symbol,Any}())
     end
 
     # Init parameters
@@ -148,22 +151,24 @@ function sample(  model::Model,
 
     # PMMH steps
     accept_his = Bool[]
-    PROGRESS[] && (spl.info[:progress] = ProgressMeter.Progress(n, 1, "[$alg_str] Sampling...", 0))
-    for i = 1:n
-      @debug "$alg_str stepping..."
-      time_elapsed = @elapsed vi, is_accept = step(model, spl, vi, i==1)
-
-      if is_accept # accepted => store the new predcits
-          samples[i].value = Sample(vi, spl).value
-      else         # rejected => store the previous predcits
-          samples[i] = samples[i - 1]
-      end
-
-      time_total += time_elapsed
-      push!(accept_his, is_accept)
-      if PROGRESS[]
-        haskey(spl.info, :progress) && ProgressMeter.update!(spl.info[:progress], spl.info[:progress].counter + 1)
-      end
+    PROGRESS[] &&
+        (spl.info[:progress] = ProgressMeter.Progress(n, 1, "[$alg_str] Sampling...", 0))
+    for i in 1:n
+        @debug "$alg_str stepping..."
+        time_elapsed = @elapsed vi, is_accept = step(model, spl, vi, i == 1)
+
+        if is_accept # accepted => store the new predcits
+            samples[i].value = Sample(vi, spl).value
+        else         # rejected => store the previous predcits
+            samples[i] = samples[i - 1]
+        end
+
+        time_total += time_elapsed
+        push!(accept_his, is_accept)
+        if PROGRESS[]
+            haskey(spl.info, :progress) &&
+                ProgressMeter.update!(spl.info[:progress], spl.info[:progress].counter + 1)
+        end
     end
 
     println("[$alg_str] Finished with")
@@ -172,18 +177,17 @@ function sample(  model::Model,
     println("  Accept rate         = $accept_rate;")
 
     if resume_from !== nothing   # concat samples
-      pushfirst!(samples, resume_from.info[:samples]...)
+        pushfirst!(samples, resume_from.info[:samples]...)
     end
     c = Chain(-Inf, samples)       # wrap the result by Chain
 
     if save_state               # save state
-      c = save(c, spl, model, vi, samples)
+        c = save(c, spl, model, vi, samples)
     end
 
-    c
+    return c
 end
 
-
 ####
 #### IMCMC Sampler.
 ####
@@ -212,41 +216,45 @@ Arguments:
 
 A paper on this can be found [here](https://arxiv.org/abs/1602.05128).
 """
-mutable struct IPMCMC{T, F} <: InferenceAlgorithm
-  n_particles::Int         # number of particles used
-  n_iters::Int         # number of iterations
-  n_nodes::Int         # number of nodes running SMC and CSMC
-  n_csmc_nodes::Int         # number of nodes CSMC
-  resampler::F           # function to resample
-  space::Set{T}      # sampling space, emtpy means all
+mutable struct IPMCMC{T,F} <: InferenceAlgorithm
+    n_particles::Int         # number of particles used
+    n_iters::Int         # number of iterations
+    n_nodes::Int         # number of nodes running SMC and CSMC
+    n_csmc_nodes::Int         # number of nodes CSMC
+    resampler::F           # function to resample
+    space::Set{T}      # sampling space, emtpy means all
 end
 IPMCMC(n1::Int, n2::Int) = IPMCMC(n1, n2, 32, 16, resample_systematic, Set())
-IPMCMC(n1::Int, n2::Int, n3::Int) = IPMCMC(n1, n2, n3, Int(ceil(n3/2)), resample_systematic, Set())
-IPMCMC(n1::Int, n2::Int, n3::Int, n4::Int) = IPMCMC(n1, n2, n3, n4, resample_systematic, Set())
+function IPMCMC(n1::Int, n2::Int, n3::Int)
+    return IPMCMC(n1, n2, n3, Int(ceil(n3 / 2)), resample_systematic, Set())
+end
+function IPMCMC(n1::Int, n2::Int, n3::Int, n4::Int)
+    return IPMCMC(n1, n2, n3, n4, resample_systematic, Set())
+end
 function IPMCMC(n1::Int, n2::Int, n3::Int, n4::Int, space...)
-  _space = isa(space, Symbol) ? Set([space]) : Set(space)
-  IPMCMC(n1, n2, n3, n4, resample_systematic, _space)
+    _space = isa(space, Symbol) ? Set([space]) : Set(space)
+    return IPMCMC(n1, n2, n3, n4, resample_systematic, _space)
 end
 
 function Sampler(alg::IPMCMC, s::Selector)
-  info = Dict{Symbol, Any}()
-  spl = Sampler(alg, info, s)
-  # Create SMC and CSMC nodes
-  samplers = Array{Sampler}(undef, alg.n_nodes)
-  # Use resampler_threshold=1.0 for SMC since adaptive resampling is invalid in this setting
-  default_CSMC = CSMC(alg.n_particles, 1, alg.resampler, alg.space)
-  default_SMC = SMC(alg.n_particles, alg.resampler, 1.0, false, alg.space)
-
-  for i in 1:alg.n_csmc_nodes
-    samplers[i] = Sampler(default_CSMC, Selector(Symbol(typeof(default_CSMC))))
-  end
-  for i in (alg.n_csmc_nodes+1):alg.n_nodes
-    samplers[i] = Sampler(default_SMC, Selector(Symbol(typeof(default_CSMC))))
-  end
-
-  info[:samplers] = samplers
-
-  return spl
+    info = Dict{Symbol,Any}()
+    spl = Sampler(alg, info, s)
+    # Create SMC and CSMC nodes
+    samplers = Array{Sampler}(undef, alg.n_nodes)
+    # Use resampler_threshold=1.0 for SMC since adaptive resampling is invalid in this setting
+    default_CSMC = CSMC(alg.n_particles, 1, alg.resampler, alg.space)
+    default_SMC = SMC(alg.n_particles, alg.resampler, 1.0, false, alg.space)
+
+    for i in 1:(alg.n_csmc_nodes)
+        samplers[i] = Sampler(default_CSMC, Selector(Symbol(typeof(default_CSMC))))
+    end
+    for i in (alg.n_csmc_nodes + 1):(alg.n_nodes)
+        samplers[i] = Sampler(default_SMC, Selector(Symbol(typeof(default_CSMC))))
+    end
+
+    info[:samplers] = samplers
+
+    return spl
 end
 
 function step(model, spl::Sampler{<:IPMCMC}, VarInfos::Array{VarInfo}, is_first::Bool)
@@ -254,16 +262,16 @@ function step(model, spl::Sampler{<:IPMCMC}, VarInfos::Array{VarInfo}, is_first:
     log_zs = zeros(spl.alg.n_nodes)
 
     # Run SMC & CSMC nodes
-    for j in 1:spl.alg.n_nodes
+    for j in 1:(spl.alg.n_nodes)
         reset_num_produce!(VarInfos[j])
         VarInfos[j] = step(model, spl.info[:samplers][j], VarInfos[j])[1]
         log_zs[j] = spl.info[:samplers][j].info[:logevidence][end]
     end
 
     # Resampling of CSMC nodes indices
-    conditonal_nodes_indices = collect(1:spl.alg.n_csmc_nodes)
-    unconditonal_nodes_indices = collect(spl.alg.n_csmc_nodes+1:spl.alg.n_nodes)
-    for j in 1:spl.alg.n_csmc_nodes
+    conditonal_nodes_indices = collect(1:(spl.alg.n_csmc_nodes))
+    unconditonal_nodes_indices = collect((spl.alg.n_csmc_nodes + 1):(spl.alg.n_nodes))
+    for j in 1:(spl.alg.n_csmc_nodes)
         # Select a new conditional node by simulating cj
         log_ksi = vcat(log_zs[unconditonal_nodes_indices], log_zs[j])
         ksi = exp.(log_ksi .- maximum(log_ksi))
@@ -276,51 +284,53 @@ function step(model, spl::Sampler{<:IPMCMC}, VarInfos::Array{VarInfo}, is_first:
     end
     nodes_permutation = vcat(conditonal_nodes_indices, unconditonal_nodes_indices)
 
-    VarInfos[nodes_permutation]
+    return VarInfos[nodes_permutation]
 end
 
 function sample(model::Model, alg::IPMCMC)
+    spl = Sampler(alg)
 
-  spl = Sampler(alg)
-
-  # Number of samples to store
-  sample_n = alg.n_iters * alg.n_csmc_nodes
-
-  # Init samples
-  time_total = zero(Float64)
-  samples = Array{Sample}(undef, sample_n)
-  weight = 1 / sample_n
-  for i = 1:sample_n
-    samples[i] = Sample(weight, Dict{Symbol, Any}())
-  end
-
-  # Init parameters
-  vi = empty!(VarInfo(model))
-  VarInfos = Array{VarInfo}(undef, spl.alg.n_nodes)
-  for j in 1:spl.alg.n_nodes
-    VarInfos[j] = deepcopy(vi)
-  end
-  n = spl.alg.n_iters
-
-  # IPMCMC steps
-  if PROGRESS[] spl.info[:progress] = ProgressMeter.Progress(n, 1, "[IPMCMC] Sampling...", 0) end
-  for i = 1:n
-    @debug "IPMCMC stepping..."
-    time_elapsed = @elapsed VarInfos = step(model, spl, VarInfos, i==1)
-
-    # Save each CSMS retained path as a sample
-    for j in 1:spl.alg.n_csmc_nodes
-      samples[(i-1)*alg.n_csmc_nodes+j].value = Sample(VarInfos[j], spl).value
+    # Number of samples to store
+    sample_n = alg.n_iters * alg.n_csmc_nodes
+
+    # Init samples
+    time_total = zero(Float64)
+    samples = Array{Sample}(undef, sample_n)
+    weight = 1 / sample_n
+    for i in 1:sample_n
+        samples[i] = Sample(weight, Dict{Symbol,Any}())
     end
 
-    time_total += time_elapsed
+    # Init parameters
+    vi = empty!(VarInfo(model))
+    VarInfos = Array{VarInfo}(undef, spl.alg.n_nodes)
+    for j in 1:(spl.alg.n_nodes)
+        VarInfos[j] = deepcopy(vi)
+    end
+    n = spl.alg.n_iters
+
+    # IPMCMC steps
     if PROGRESS[]
-      haskey(spl.info, :progress) && ProgressMeter.update!(spl.info[:progress], spl.info[:progress].counter + 1)
+        spl.info[:progress] = ProgressMeter.Progress(n, 1, "[IPMCMC] Sampling...", 0)
     end
-  end
+    for i in 1:n
+        @debug "IPMCMC stepping..."
+        time_elapsed = @elapsed VarInfos = step(model, spl, VarInfos, i == 1)
+
+        # Save each CSMS retained path as a sample
+        for j in 1:(spl.alg.n_csmc_nodes)
+            samples[(i - 1) * alg.n_csmc_nodes + j].value = Sample(VarInfos[j], spl).value
+        end
 
-  println("[IPMCMC] Finished with")
-  println("  Running time    = $time_total;")
+        time_total += time_elapsed
+        if PROGRESS[]
+            haskey(spl.info, :progress) &&
+                ProgressMeter.update!(spl.info[:progress], spl.info[:progress].counter + 1)
+        end
+    end
+
+    println("[IPMCMC] Finished with")
+    println("  Running time    = $time_total;")
 
-  Chain(0.0, samples) # wrap the result by Chain
+    return Chain(0.0, samples) # wrap the result by Chain
 end
diff --git a/src/resampling.jl b/src/resampling.jl
index 4bb21046..b2797461 100644
--- a/src/resampling.jl
+++ b/src/resampling.jl
@@ -7,13 +7,13 @@
 #  - http://people.isy.liu.se/rt/schon/Publications/HolSG2006.pdf
 # Code adapted from: http://uk.mathworks.com/matlabcentral/fileexchange/24968-resampling-methods-for-particle-filtering
 
-struct ResampleWithESSThreshold{R, T<:Real}
+struct ResampleWithESSThreshold{R,T<:Real}
     resampler::R
     threshold::T
 end
 
-function ResampleWithESSThreshold(resampler = resample_systematic)
-    ResampleWithESSThreshold(resampler, 0.5)
+function ResampleWithESSThreshold(resampler=resample_systematic)
+    return ResampleWithESSThreshold(resampler, 0.5)
 end
 
 # More stable, faster version of rand(Categorical)
@@ -30,9 +30,7 @@ function randcat(rng::Random.AbstractRNG, p::AbstractVector{<:Real})
 end
 
 function resample_multinomial(
-    rng::Random.AbstractRNG,
-    w::AbstractVector{<:Real},
-    num_particles::Integer = length(w),
+    rng::Random.AbstractRNG, w::AbstractVector{<:Real}, num_particles::Integer=length(w)
 )
     return rand(rng, Distributions.sampler(Distributions.Categorical(w)), num_particles)
 end
@@ -40,7 +38,7 @@ end
 function resample_residual(
     rng::Random.AbstractRNG,
     w::AbstractVector{<:Real},
-    num_particles::Integer = length(weights),
+    num_particles::Integer=length(weights),
 )
     # Pre-allocate array for resampled particles
     indices = Vector{Int}(undef, num_particles)
@@ -67,7 +65,6 @@ function resample_residual(
     return indices
 end
 
-
 """
     resample_stratified(rng, weights, n)
 
@@ -81,9 +78,7 @@ i.e., `xᵢ = j` if and only if
 ``uᵢ \\in [\\sum_{s=1}^{j-1} weights_{s}, \\sum_{s=1}^{j} weights_{s})``.
 """
 function resample_stratified(
-    rng::Random.AbstractRNG,
-    weights::AbstractVector{<:Real},
-    n::Integer = length(weights),
+    rng::Random.AbstractRNG, weights::AbstractVector{<:Real}, n::Integer=length(weights)
 )
     # check input
     m = length(weights)
@@ -130,9 +125,7 @@ normalized `weights`, i.e., `xᵢ = j` if and only if
 ``uᵢ \\in [\\sum_{s=1}^{j-1} weights_{s}, \\sum_{s=1}^{j} weights_{s})``.
 """
 function resample_systematic(
-    rng::Random.AbstractRNG,
-    weights::AbstractVector{<:Real},
-    n::Integer = length(weights),
+    rng::Random.AbstractRNG, weights::AbstractVector{<:Real}, n::Integer=length(weights)
 )
     # check input
     m = length(weights)
diff --git a/src/smc.jl b/src/smc.jl
index da5523ae..1beab676 100644
--- a/src/smc.jl
+++ b/src/smc.jl
@@ -31,17 +31,14 @@ function AbstractMCMC.sample(model::AbstractMCMC.AbstractModel, sampler::SMC; kw
 end
 
 function AbstractMCMC.sample(
-    rng::Random.AbstractRNG,
-    model::AbstractMCMC.AbstractModel,
-    sampler::SMC;
-    kwargs...
+    rng::Random.AbstractRNG, model::AbstractMCMC.AbstractModel, sampler::SMC; kwargs...
 )
     if !isempty(kwargs)
         @warn "keyword arguments $(keys(kwargs)) are not supported by `SMC`"
     end
 
     # Create a set of particles.
-    particles = ParticleContainer([Trace(model) for _ in 1:sampler.nparticles])
+    particles = ParticleContainer([Trace(model) for _ in 1:(sampler.nparticles)])
 
     # Perform particle sweep.
     logevidence = sweep!(rng, particles, sampler.resampler)
@@ -83,13 +80,10 @@ struct PGSample{T,L}
 end
 
 function AbstractMCMC.step(
-    rng::Random.AbstractRNG,
-    model::AbstractMCMC.AbstractModel,
-    sampler::PG;
-    kwargs...,
+    rng::Random.AbstractRNG, model::AbstractMCMC.AbstractModel, sampler::PG; kwargs...
 )
     # Create a new set of particles.
-    particles = ParticleContainer([Trace(model) for _ in 1:sampler.nparticles])
+    particles = ParticleContainer([Trace(model) for _ in 1:(sampler.nparticles)])
 
     # Perform a particle sweep.
     logevidence = sweep!(rng, particles, sampler.resampler)
@@ -105,7 +99,7 @@ function AbstractMCMC.step(
     model::AbstractMCMC.AbstractModel,
     sampler::PG,
     state::PGState;
-    kwargs...
+    kwargs...,
 )
     # Create a new set of particles.
     nparticles = sampler.nparticles
diff --git a/test/container.jl b/test/container.jl
index 2b2f83c8..7313ac79 100644
--- a/test/container.jl
+++ b/test/container.jl
@@ -27,8 +27,8 @@
 
         # Initial state.
         @test pc.logWs == zeros(3)
-        @test AdvancedPS.getweights(pc) == fill(1/3, 3)
-        @test all(AdvancedPS.getweight(pc, i) == 1/3 for i in 1:3)
+        @test AdvancedPS.getweights(pc) == fill(1 / 3, 3)
+        @test all(AdvancedPS.getweight(pc, i) == 1 / 3 for i in 1:3)
         @test AdvancedPS.logZ(pc) ≈ log(3)
         @test AdvancedPS.effectiveSampleSize(pc) == 3
 
@@ -36,21 +36,26 @@
         AdvancedPS.reweight!(pc)
         @test pc.logWs == logps
         @test AdvancedPS.getweights(pc) ≈ exp.(logps) ./ sum(exp, logps)
-        @test all(AdvancedPS.getweight(pc, i) ≈ exp(logps[i]) / sum(exp, logps) for i in 1:3)
+        @test all(
+            AdvancedPS.getweight(pc, i) ≈ exp(logps[i]) / sum(exp, logps) for i in 1:3
+        )
         @test AdvancedPS.logZ(pc) ≈ log(sum(exp, logps))
 
         # Reweight particles.
         AdvancedPS.reweight!(pc)
         @test pc.logWs == 2 .* logps
         @test AdvancedPS.getweights(pc) == exp.(2 .* logps) ./ sum(exp, 2 .* logps)
-        @test all(AdvancedPS.getweight(pc, i) ≈ exp(2 * logps[i]) / sum(exp, 2 .* logps) for i in 1:3)
+        @test all(
+            AdvancedPS.getweight(pc, i) ≈ exp(2 * logps[i]) / sum(exp, 2 .* logps) for
+            i in 1:3
+        )
         @test AdvancedPS.logZ(pc) ≈ log(sum(exp, 2 .* logps))
 
         # Resample and propagate particles.
         AdvancedPS.resample_propagate!(Random.GLOBAL_RNG, pc)
         @test pc.logWs == zeros(3)
-        @test AdvancedPS.getweights(pc) == fill(1/3, 3)
-        @test all(AdvancedPS.getweight(pc, i) == 1/3 for i in 1:3)
+        @test AdvancedPS.getweights(pc) == fill(1 / 3, 3)
+        @test all(AdvancedPS.getweight(pc, i) == 1 / 3 for i in 1:3)
         @test AdvancedPS.logZ(pc) ≈ log(3)
         @test AdvancedPS.effectiveSampleSize(pc) == 3
 
@@ -58,7 +63,9 @@
         AdvancedPS.reweight!(pc)
         @test pc.logWs ⊆ logps
         @test AdvancedPS.getweights(pc) == exp.(pc.logWs) ./ sum(exp, pc.logWs)
-        @test all(AdvancedPS.getweight(pc, i) ≈ exp(pc.logWs[i]) / sum(exp, pc.logWs) for i in 1:3)
+        @test all(
+            AdvancedPS.getweight(pc, i) ≈ exp(pc.logWs[i]) / sum(exp, pc.logWs) for i in 1:3
+        )
         @test AdvancedPS.logZ(pc) ≈ log(sum(exp, pc.logWs))
 
         # Increase unnormalized logarithmic weights.
diff --git a/test/resampling.jl b/test/resampling.jl
index f72f07bf..8f7eb8cb 100644
--- a/test/resampling.jl
+++ b/test/resampling.jl
@@ -5,12 +5,12 @@
 
     resSystematic = AdvancedPS.resample_systematic(rng, D, num_samples)
     resStratified = AdvancedPS.resample_stratified(rng, D, num_samples)
-    resMultinomial= AdvancedPS.resample_multinomial(rng, D, num_samples)
-    resResidual   = AdvancedPS.resample_residual(rng, D, num_samples)
+    resMultinomial = AdvancedPS.resample_multinomial(rng, D, num_samples)
+    resResidual = AdvancedPS.resample_residual(rng, D, num_samples)
     AdvancedPS.resample_systematic(rng, D)
 
-    @test sum(resSystematic .== 2) ≈ (num_samples * 0.4) atol=1e-3*num_samples
-    @test sum(resStratified .== 2) ≈ (num_samples * 0.4) atol=1e-3*num_samples
-    @test sum(resMultinomial .== 2) ≈ (num_samples * 0.4) atol=1e-2*num_samples
-    @test sum(resResidual .== 2) ≈ (num_samples * 0.4) atol=1e-2*num_samples
+    @test sum(resSystematic .== 2) ≈ (num_samples * 0.4) atol = 1e-3 * num_samples
+    @test sum(resStratified .== 2) ≈ (num_samples * 0.4) atol = 1e-3 * num_samples
+    @test sum(resMultinomial .== 2) ≈ (num_samples * 0.4) atol = 1e-2 * num_samples
+    @test sum(resResidual .== 2) ≈ (num_samples * 0.4) atol = 1e-2 * num_samples
 end
diff --git a/test/runtests.jl b/test/runtests.jl
index 7a3cd3c7..5497a17e 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -6,7 +6,13 @@ using Random
 using Test
 
 @testset "AdvancedPS.jl" begin
-    @testset "Resampling tests" begin include("resampling.jl") end
-    @testset "Container tests" begin include("container.jl") end
-    @testset "SMC and PG tests" begin include("smc.jl") end
+    @testset "Resampling tests" begin
+        include("resampling.jl")
+    end
+    @testset "Container tests" begin
+        include("container.jl")
+    end
+    @testset "SMC and PG tests" begin
+        include("smc.jl")
+    end
 end
diff --git a/test/smc.jl b/test/smc.jl
index 2726b718..1f98eb37 100644
--- a/test/smc.jl
+++ b/test/smc.jl
@@ -7,12 +7,12 @@
         sampler = AdvancedPS.SMC(15, 0.6)
         @test sampler.nparticles == 15
         @test sampler.resampler ===
-            AdvancedPS.ResampleWithESSThreshold(AdvancedPS.resample_systematic, 0.6)
+              AdvancedPS.ResampleWithESSThreshold(AdvancedPS.resample_systematic, 0.6)
 
         sampler = AdvancedPS.SMC(20, AdvancedPS.resample_multinomial, 0.6)
         @test sampler.nparticles == 20
         @test sampler.resampler ===
-            AdvancedPS.ResampleWithESSThreshold(AdvancedPS.resample_multinomial, 0.6)
+              AdvancedPS.ResampleWithESSThreshold(AdvancedPS.resample_multinomial, 0.6)
 
         sampler = AdvancedPS.SMC(25, AdvancedPS.resample_systematic)
         @test sampler.nparticles == 25
@@ -39,7 +39,7 @@
             m.b = b = rand(Normal(a, 1))
 
             # Second observation.
-            AdvancedPS.observe(Normal(b, 2), 1.5)
+            return AdvancedPS.observe(Normal(b, 2), 1.5)
         end
         sample(NormalModel(), AdvancedPS.SMC(100))
 
@@ -87,7 +87,7 @@
             m.c = rand(Beta())
 
             # Second observation.
-            AdvancedPS.observe(Bernoulli(x / 2), 0)
+            return AdvancedPS.observe(Bernoulli(x / 2), 0)
         end
 
         chains_smc = sample(TestModel(), AdvancedPS.SMC(100))
@@ -104,12 +104,12 @@
         sampler = AdvancedPS.PG(60, 0.6)
         @test sampler.nparticles == 60
         @test sampler.resampler ===
-            AdvancedPS.ResampleWithESSThreshold(AdvancedPS.resample_systematic, 0.6)
+              AdvancedPS.ResampleWithESSThreshold(AdvancedPS.resample_systematic, 0.6)
 
         sampler = AdvancedPS.PG(80, AdvancedPS.resample_multinomial, 0.6)
         @test sampler.nparticles == 80
         @test sampler.resampler ===
-            AdvancedPS.ResampleWithESSThreshold(AdvancedPS.resample_multinomial, 0.6)
+              AdvancedPS.ResampleWithESSThreshold(AdvancedPS.resample_multinomial, 0.6)
 
         sampler = AdvancedPS.PG(100, AdvancedPS.resample_systematic)
         @test sampler.nparticles == 100
@@ -141,7 +141,7 @@
             m.c = rand(Beta())
 
             # Second observation.
-            AdvancedPS.observe(Bernoulli(x / 2), 0)
+            return AdvancedPS.observe(Bernoulli(x / 2), 0)
         end
 
         chains_pg = sample(TestModel(), AdvancedPS.PG(10), 100)
@@ -208,4 +208,3 @@ end
 #         check_MoGtest_default(chain2, atol = 0.2)
 #     end
 # end
-