Remove trailing whitespace

benchmarks/benchmarks_suite.jl

@@ -4,14 +4,14 @@ using LinearAlgebra
 const BenchmarkSuite = BenchmarkTools.BenchmarkGroup()
 
 #
-# Add models to benchmarks 
+# Add models to benchmarks
 #
 
 include("models/hlr.jl")
 include("models/lr.jl")
 include("models/sv_nuts.jl")
 
-# constrained 
+# constrained
 BenchmarkSuite["constrained"] = BenchmarkGroup(["constrained"])
 
 data = [0, 1, 0, 1, 1, 1, 1, 1, 1, 1]

src/mcmc/Inference.jl

@@ -318,7 +318,7 @@ Return a named tuple of parameters.
 getparams(model, t) = t.θ
 function getparams(model::DynamicPPL.Model, vi::DynamicPPL.VarInfo)
     # NOTE: In the past, `invlink(vi, model)` + `values_as(vi, OrderedDict)` was used.
-    # Unfortunately, using `invlink` can cause issues in scenarios where the constraints 
+    # Unfortunately, using `invlink` can cause issues in scenarios where the constraints
     # of the parameters change depending on the realizations. Hence we have to use
     # `values_as_in_model`, which re-runs the model and extracts the parameters
     # as they are seen in the model, i.e. in the constrained space. Moreover,

src/mcmc/particle_mcmc.jl

@@ -340,7 +340,7 @@ function trace_local_varinfo_maybe(varinfo)
         trace = AdvancedPS.current_trace()
         return trace.model.f.varinfo
     catch e
-        # NOTE: this heuristic allows Libtask evaluating a model outside a `Trace`. 
+        # NOTE: this heuristic allows Libtask evaluating a model outside a `Trace`.
         if e == KeyError(:__trace) || current_task().storage isa Nothing
             return varinfo
         else

test/mcmc/hmc.jl

@@ -308,7 +308,7 @@ using Turing
             initial_params=[0.5, 1.75, 1.0]
         )
         chain_prior = sample(model, Prior(), num_samples)
-        
+
         # Extract the `x` like this because running `generated_quantities` was how
         # the issue was discovered, hence we also want to make sure that it works.
         results = generated_quantities(model, chain)

test/runtests.jl

@@ -37,7 +37,7 @@ end
         @timeit_include("mcmc/ess.jl")
         @timeit_include("mcmc/is.jl")
     end
-    
+
     @timeit TIMEROUTPUT "inference" begin
         @testset "inference with samplers" begin
             @timeit_include("mcmc/gibbs.jl")

test/skipped/advi_demo.jl

@@ -90,7 +90,7 @@ for seed in seeds
             # Eq. (90) in "Conjugate Bayesian analysis of the Gaussian distribution" by Murphy
             # `scale(post)` = θ
             p_τ = Gamma(post.shape, scale(post))
-            p_σ²_pdf = z -> pdf(p_τ, 1 / z) # τ => 1 / σ² 
+            p_σ²_pdf = z -> pdf(p_τ, 1 / z) # τ => 1 / σ²
 
             # marginal of μ
             # Eq. (91) in "Conjugate Bayesian analysis of the Gaussian distribution" by Murphy
@@ -102,7 +102,7 @@ for seed in seeds
             βₙ = post.rate  # β → rate
 
             # numerically more stable but doesn't seem to have effect; issue is probably internal to
-            # `pdf` which needs to compute ≈ Γ(1000) 
+            # `pdf` which needs to compute ≈ Γ(1000)
             p_μ_pdf =
                 z -> exp(
                     logpdf(

test/variational/advi.jl

@@ -69,7 +69,7 @@ using Turing.Essential: TuringDiagMvNormal
             x ~ Dirichlet([1.0,1.0])
             return x
         end
-        
+
         m = dirichlet()
         b = bijector(m)
         x0 = m()