diff --git a/examples/anasol/runtests.jl b/examples/anasol/runtests.jl
index 50788f3e..ee9dca38 100644
--- a/examples/anasol/runtests.jl
+++ b/examples/anasol/runtests.jl
@@ -41,7 +41,7 @@ if !haskey(ENV, "MADS_NO_GADFLY")
 	Mads.spaghettiplots(md, 2)
 	Mads.rmfile(joinpath(workdir, "w01short-ax-2-spaghetti.svg"))
 	Mads.rmfile(joinpath(workdir, "w01short-vx-2-spaghetti.svg"))
-	Mads.plotmass([1.,2.],[10.,20.],[5.,10.], joinpath(workdir, "plotmass-test.svg"))
+	Mads.plotmass([1.0, 2.0], [10.0, 20.0], [5.0, 10.0], joinpath(workdir, "plotmass-test.svg"))
 	Mads.rmfile(joinpath(workdir, "plotmass-test.svg"))
 	Mads.graphon()
 end
@@ -99,7 +99,7 @@ Mads.wellon!(md, "w1a")
 Mads.savemadsfile(md, joinpath(workdir, "test.mads"))
 Mads.savemadsfile(md, Mads.getparamdict(md))
 Mads.savemadsfile(md, Mads.getparamdict(md), joinpath(workdir, "test.mads"))
-Mads.savemadsfile(md, Mads.getparamdict(md), joinpath(workdir, "test.mads"), explicit=true)
+Mads.savemadsfile(md, Mads.getparamdict(md), joinpath(workdir, "test.mads"); explicit=true)
 Mads.rmfile(joinpath(workdir, "w01shortexp-rerun.mads"))
 Mads.rmfile(joinpath(workdir, "test.mads"))
 Mads.setmadsinputfile("test.mads")
@@ -128,7 +128,7 @@ mdbad = deepcopy(md)
 Mads.setparamsdistnormal!(mdbad, fill(1, length(m1)), fill(2, length(m1)))
 Mads.setparamsdistuniform!(mdbad, fill(1, length(m1)), fill(2, length(m1)))
 
-Mads.computemass("w01lambda", time=50, path=workdir)
+Mads.computemass("w01lambda"; time=50, path=workdir)
 Mads.rmdir(joinpath(workdir, "mass_reduced.svg"))
 
 d = joinpath(workdir, "test_results")
@@ -163,35 +163,35 @@ good_targetkeys = JLD2.load(joinpath(d, "targetkeys.jld2"), "tk")
 
 Test.@testset "Anasol" begin
 	# Test Mads.forward(md; all=true)
-	ssr = 0.
+	ssr = 0.0
 	for obskey in union(Set(keys(forward_results)), Set(keys(good_forward_results)))
 		ssr += (forward_results[obskey] - good_forward_results[obskey])^2
 	end
-	Test.@test isapprox(ssr, 0., atol=1e-8)
+	Test.@test isapprox(ssr, 0.0, atol=1e-8)
 
-	# Test Mads.getparamrandom(md, 5, init_dist=true)
+	# Test Mads.getparamrandom(md, 5; init_dist=true)
 	for obskey in union(Set(keys(paramrandom_true)), Set(keys(good_paramrandom_true)))
 		Test.@test isapprox(paramrandom_true[obskey], good_paramrandom_true[obskey], atol=1e-8)
 	end
 
-	# Test Mads.getparamrandom(md, 5, init_dist=false)
+	# Test Mads.getparamrandom(md, 5; init_dist=false)
 	for obskey in union(Set(keys(paramrandom_false)), Set(keys(good_paramrandom_false)))
 		Test.@test isapprox(paramrandom_false[obskey], good_paramrandom_false[obskey], atol=1e-8)
 	end
 
 	# Test Mads.getparamdict(md)
-	ssr = 0.
+	ssr = 0.0
 	for obskey in union(Set(keys(pd)), Set(keys(good_pd)))
 		ssr += (pd[obskey] - good_pd[obskey])^2
 	end
-	Test.@test isapprox(ssr, 0., atol=1e-8)
+	Test.@test isapprox(ssr, 0.0, atol=1e-8)
 
 	# Test computeconcentrations(paramdict)
-	ssr = 0.
+	ssr = 0.0
 	for obskey in union(Set(keys(forward_preds)), Set(keys(good_forward_preds)))
 		ssr += (forward_preds[obskey] - good_forward_preds[obskey])^2
 	end
-	Test.@test isapprox(ssr, 0., atol=1e-8)
+	Test.@test isapprox(ssr, 0.0, atol=1e-8)
 
 	# Test Mads.of(md)
 	Test.@test isapprox(madsOf, 628963.6972820368, atol=1e-8) #test
diff --git a/examples/model_analysis/bayes_weight_analsis.jl b/examples/model_analysis/bayes_weight_analsis.jl
index edba1a2e..4cfebb4a 100644
--- a/examples/model_analysis/bayes_weight_analsis.jl
+++ b/examples/model_analysis/bayes_weight_analsis.jl
@@ -7,29 +7,29 @@ Mads.mkdir("bayes_results")
 
 @info("Bayesian analysis with different initial parameter sets and observation weights (standard deviation errors)")
 @info("Bayesian analysis for the initial parameter guesses:")
-for w = (1000000, 1000, 1)
+for w in (1000000, 1000, 1)
 	Mads.setobsweights!(md, w)
 	mcmcchain = Mads.bayessampling(md; nsteps=1000000, burnin=100000, thinning=100, seed=2016)
 	Mads.scatterplotsamples(md, permutedims(mcmcchain.value), "bayes_results/bayes_init_w$w.png")
 	o = Mads.forward(md, mcmcchain.value)
-	Mads.spaghettiplot(md, o, filename="bayes_results/bayes_init_w$(w)_spaghetti.png")
-	Printf.@printf "Init: Observation Weight %d StdDev %f ->`o5` prediction: min = %f max = %f\n" w 1/w min(o[:,5]...) max(o[:,5]...)
-	f = Gadfly.plot(x=o[:,5], Gadfly.Guide.xlabel("o5"), Gadfly.Geom.histogram())
+	Mads.spaghettiplot(md, o; filename="bayes_results/bayes_init_w$(w)_spaghetti.png")
+	Printf.@printf "Init: Observation Weight %d StdDev %f ->`o5` prediction: min = %f max = %f\n" w 1 / w min(o[:, 5]...) max(o[:, 5]...)
+	f = Gadfly.plot(; x=o[:, 5], Gadfly.Guide.xlabel("o5"), Gadfly.Geom.histogram())
 	Gadfly.draw(Gadfly.PNG("bayes_results/bayes_init_w$(w)_o5.png", 6Gadfly.inch, 4Gadfly.inch), f)
 end
 pinit = Dict(zip(Mads.getparamkeys(md), Mads.getparamsinit(md)))
 
 n = 100
 @info("Calibration using $n random initial guesses for model parameters")
-r = Mads.calibraterandom(md, n, all=true, seed=2016, save_results=false)
-pnames = collect(keys(r[1,3]))
-p = hcat(map(i->collect(values(r[i,3])), 1:n)...)'
+r = Mads.calibraterandom(md, n; all=true, seed=2016, save_results=false)
+pnames = collect(keys(r[1, 3]))
+p = hcat(map(i -> collect(values(r[i, 3])), 1:n)...)'
 np = length(pnames)
 @info("Identify the 3 different global optima with different model parameter estimates")
 
-ind_n0 = abs(p[:,4]) .< 0.1
+ind_n0 = abs(p[:, 4]) .< 0.1
 in0 = findall(ind_n0 .== true)[1]
-ind_n1 = abs(p[:,4]-1) .< 0.1
+ind_n1 = abs(p[:, 4] - 1) .< 0.1
 in1 = findall(ind_n1 .== true)[1]
 ind_n01 = !(ind_n0 | ind_n1)
 in01 = findall(ind_n01 .== true)[1]
@@ -39,15 +39,15 @@ v = [in0, in1, in01]
 
 @info("Bayesian analysis for the 3 different global optima")
 for i = 1:3
-	Mads.setparamsinit!(md, r[v[i],3])
-	for w = (1000000, 1000, 1)
+	Mads.setparamsinit!(md, r[v[i], 3])
+	for w in (1000000, 1000, 1)
 		Mads.setobsweights!(md, w)
 		mcmcchain = Mads.bayessampling(md; nsteps=1000000, burnin=100000, thinning=100, seed=2016)
 		Mads.scatterplotsamples(md, permutedims(mcmcchain.value), "bayes_results/bayes_opt_$(optnames[i])_w$w.png")
 		o = Mads.forward(md, mcmcchain.value)
-		Mads.spaghettiplot(md, o, filename="bayes_results/bayes_opt_$(optnames[i])_w$(w)_spaghetti.png")
-		Printf.@printf "O%-3s: Observation Weight %d StdDev %f -> `o5` prediction: min = %f max = %f\n" optnames[i] w 1/w min(o[:,5]...) max(o[:,5]...)
-		f = Gadfly.plot(x=o[:,5], Gadfly.Guide.xlabel("o5"), Gadfly.Geom.histogram())
+		Mads.spaghettiplot(md, o; filename="bayes_results/bayes_opt_$(optnames[i])_w$(w)_spaghetti.png")
+		Printf.@printf "O%-3s: Observation Weight %d StdDev %f -> `o5` prediction: min = %f max = %f\n" optnames[i] w 1 / w min(o[:, 5]...) max(o[:, 5]...)
+		f = Gadfly.plot(; x=o[:, 5], Gadfly.Guide.xlabel("o5"), Gadfly.Geom.histogram())
 		Gadfly.draw(Gadfly.PNG("bayes_results/bayes_opt_$(optnames[i])_w$(w)_o5.png", 6Gadfly.inch, 4Gadfly.inch), f)
 	end
 end
diff --git a/examples/model_analysis/emcee_walkers_analsis.jl b/examples/model_analysis/emcee_walkers_analsis.jl
index 946b45b8..7abba4c9 100644
--- a/examples/model_analysis/emcee_walkers_analsis.jl
+++ b/examples/model_analysis/emcee_walkers_analsis.jl
@@ -6,13 +6,13 @@ md = Mads.loadmadsfile(joinpath("models", "internal-polynomial.mads"))
 Mads.mkdir("emcee_results")
 
 @info("AffineInvariantMCMC (EMCEE) Bayesian analysis with different number of walkers:")
-for nw = (100, 50, 20, 10, 5, 1)
+for nw in (100, 50, 20, 10, 5, 1)
 	Mads.setobsweights!(md, 1000000)
 	@time chain, llhoods = Mads.emceesampling(md; numwalkers=nw, nsteps=1000000, burnin=100000, thinning=100, seed=2016)
 	Mads.scatterplotsamples(md, chain', "emcee_results/emcee_init_nw$nw.png")
 	o = Mads.forward(md, chain')
-	Mads.spaghettiplot(md, o, filename="emcee_results/emcee_init_nw$(nw)_spaghetti.png")
-	Printf.@printf "Init: Number of walkers %d ->`o5` prediction: min = %f max = %f\n" nw min(o[:,5]...) max(o[:,5]...)
-	f = Gadfly.plot(x=o[:,5], Gadfly.Guide.xlabel("o5"), Gadfly.Geom.histogram())
+	Mads.spaghettiplot(md, o; filename="emcee_results/emcee_init_nw$(nw)_spaghetti.png")
+	Printf.@printf "Init: Number of walkers %d ->`o5` prediction: min = %f max = %f\n" nw min(o[:, 5]...) max(o[:, 5]...)
+	f = Gadfly.plot(; x=o[:, 5], Gadfly.Guide.xlabel("o5"), Gadfly.Geom.histogram())
 	Gadfly.draw(Gadfly.PNG("emcee_results/emcee_init_nw$(nw)_o5.png", 6Gadfly.inch, 4Gadfly.inch), f)
-end
\ No newline at end of file
+end
diff --git a/examples/model_analysis/emcee_weight_analsis.jl b/examples/model_analysis/emcee_weight_analsis.jl
index 6ac25c42..ab59e4dd 100644
--- a/examples/model_analysis/emcee_weight_analsis.jl
+++ b/examples/model_analysis/emcee_weight_analsis.jl
@@ -7,29 +7,29 @@ Mads.mkdir("emcee_results")
 
 @info("AffineInvariantMCMC (EMCEE) Bayesian analysis with different initial parameter sets and observation weights (standard deviation errors)")
 @info("AffineInvariantMCMC (EMCEE) Bayesian analysis for the initial parameter guesses:")
-for w = (1000000, 1000, 1)
+for w in (1000000, 1000, 1)
 	Mads.setobsweights!(md, w)
 	chain, llhoods = Mads.emceesampling(md; numwalkers=100, nsteps=1000000, burnin=100000, thinning=100, seed=2016)
 	Mads.scatterplotsamples(md, chain', "emcee_results/emcee_init_w$w.png")
 	o = Mads.forward(md, chain')
-	Mads.spaghettiplot(md, o, filename="emcee_results/emcee_init_w$(w)_spaghetti.png")
-	Printf.@printf "Init: Observation Weight %d StdDev %f ->`o5` prediction: min = %f max = %f\n" w 1/w min(o[:,5]...) max(o[:,5]...)
-	f = Gadfly.plot(x=o[:,5], Gadfly.Guide.xlabel("o5"), Gadfly.Geom.histogram())
+	Mads.spaghettiplot(md, o; filename="emcee_results/emcee_init_w$(w)_spaghetti.png")
+	Printf.@printf "Init: Observation Weight %d StdDev %f ->`o5` prediction: min = %f max = %f\n" w 1 / w min(o[:, 5]...) max(o[:, 5]...)
+	f = Gadfly.plot(; x=o[:, 5], Gadfly.Guide.xlabel("o5"), Gadfly.Geom.histogram())
 	Gadfly.draw(Gadfly.PNG("emcee_results/emcee_init_w$(w)_o5.png", 6Gadfly.inch, 4Gadfly.inch), f)
 end
 pinit = Dict(zip(Mads.getparamkeys(md), Mads.getparamsinit(md)))
 
 n = 100
 @info("Calibration using $n random initial guesses for model parameters")
-r = Mads.calibraterandom(md, n, all=true, seed=2016, save_results=false)
-pnames = collect(keys(r[1,3]))
-p = hcat(map(i->collect(values(r[i,3])), 1:n)...)'
+r = Mads.calibraterandom(md, n; all=true, seed=2016, save_results=false)
+pnames = collect(keys(r[1, 3]))
+p = hcat(map(i -> collect(values(r[i, 3])), 1:n)...)'
 np = length(pnames)
 @info("Identify the 3 different global optima with different model parameter estimates")
 
-ind_n0 = abs(p[:,4]) .< 0.1
+ind_n0 = abs(p[:, 4]) .< 0.1
 in0 = findall(ind_n0 .== true)[1]
-ind_n1 = abs(p[:,4]-1) .< 0.1
+ind_n1 = abs(p[:, 4] - 1) .< 0.1
 in1 = findall(ind_n1 .== true)[1]
 ind_n01 = !(ind_n0 | ind_n1)
 in01 = findall(ind_n01 .== true)[1]
@@ -39,15 +39,15 @@ v = [in0, in1, in01]
 
 @info("AffineInvariantMCMC (EMCEE) Bayesian analysis for the 3 different global optima")
 for i = 1:3
-	Mads.setparamsinit!(md, r[v[i],3])
-	for w = (1000000, 1000, 1)
+	Mads.setparamsinit!(md, r[v[i], 3])
+	for w in (1000000, 1000, 1)
 		Mads.setobsweights!(md, w)
 		chain, llhoods = Mads.emceesampling(md; numwalkers=100, nsteps=1000000, burnin=100000, thinning=100, seed=2016)
 		Mads.scatterplotsamples(md, chain', "emcee_results/emcee_opt_$(optnames[i])_w$w.png")
 		o = Mads.forward(md, chain')
-		Mads.spaghettiplot(md, o, filename="emcee_results/emcee_opt_$(optnames[i])_w$(w)_spaghetti.png")
-		Printf.@printf "O%-3s: Observation Weight %d StdDev %f -> `o5` prediction: min = %f max = %f\n" optnames[i] w 1/w min(o[:,5]...) max(o[:,5]...)
-		f = Gadfly.plot(x=o[:,5], Gadfly.Guide.xlabel("o5"), Gadfly.Geom.histogram())
+		Mads.spaghettiplot(md, o; filename="emcee_results/emcee_opt_$(optnames[i])_w$(w)_spaghetti.png")
+		Printf.@printf "O%-3s: Observation Weight %d StdDev %f -> `o5` prediction: min = %f max = %f\n" optnames[i] w 1 / w min(o[:, 5]...) max(o[:, 5]...)
+		f = Gadfly.plot(; x=o[:, 5], Gadfly.Guide.xlabel("o5"), Gadfly.Geom.histogram())
 		Gadfly.draw(Gadfly.PNG("emcee_results/emcee_opt_$(optnames[i])_w$(w)_o5.png", 6Gadfly.inch, 4Gadfly.inch), f)
 	end
 end