Rel 1.1.0 - Remove pk_plot (moved to SRPlots)

src/pk_utilities.jl

@@ -1,21 +1,9 @@
-using StatsPlots
-
 function pk_qualify(pk::Vector{Float64})
     pk_good = sum(pk .<= 0.5)
     pk_ok = length(pk[pk .<= 0.7]) - pk_good
     pk_bad = length(pk[pk .<= 1]) - pk_good - pk_ok
     (good=pk_good, ok=pk_ok, bad=pk_bad, very_bad=sum(pk .> 1))
 end
 
-function pk_plot(pk::Vector{Float64}; title="PSIS diagnostic plot.",
-    leg=:topleft, kwargs...)
-    scatter(pk, xlab="Datapoint", ylab="Pareto shape k",
-        marker=2.5, lab="Pk points", leg=leg)
-  hline!([0.5], lab="pk = 0.5");hline!([0.7], lab="pk = 0.7")
-  hline!([1], lab="pk = 1.0")
-  title!(title)
-end
-
 export
-    pk_qualify,
-    pk_plot
+    pk_qualify

src/waic.jl

@@ -1,6 +1,22 @@
+"""
+    waic(ll::AbstractArray{<:Real}; pointwise=false, log_lik="log_lik, kwargs...)
 
-var2(x) = mean(x.^2) .- mean(x)^2
+Compute the Widely Applicable Information Criterion (WAIC).
 
+# Arguments
+* `loglik::AbstractArray`   : A vector of posterior log likelihoods
+* `pointwise::Bool`         : Compute WAIC pointwise, return a vector
+
+
+# Returns
+* `res::NamedTuple`: (WAIC=waics, lppd=lpd, penalty=pD, std_err=se) where
+
+    WAIC                    : Sum of pointwise waic values (or pointwise vector)
+    lppd                    : Log pointwise predictive density
+    penalty                 : Penalty term ("overfitting penalty")
+    std_err                 : Standard error of pointwise waic values    
+
+"""
 function waic( ll::AbstractArray; pointwise=false , log_lik="log_lik" , kwargs... )
 
     n_samples, n_obs = size(ll)
@@ -31,6 +47,8 @@ function waic( ll::AbstractArray; pointwise=false , log_lik="log_lik" , kwargs..
     (WAIC=waics, lppd=lpd, penalty=pD, std_err=se)
 end
 
+var2(x) = var(x; corrected=false)
+
 export
     var2,
     waic