[Julia] Interface Improvements

julia/MCMC.jl

@@ -1,5 +1,5 @@
 struct HMCDiag
-    model::Bridgestan.StanModel
+    model::BridgeStan.StanModel
     stepsize::Float64
     steps::Int64
     metric::Vector{Float64}
@@ -11,22 +11,23 @@ function HMCDiag(model, stepsize, steps)
         model,
         stepsize,
         steps,
-        ones(Bridgestan.param_unc_num(model)),
-        randn(Bridgestan.param_unc_num(model)))
+        ones(BridgeStan.param_unc_num(model)),
+        randn(BridgeStan.param_unc_num(model)),
+    )
 end
 
 function joint_logp(hmc::HMCDiag, theta, rho)
-    logp, _ = Bridgestan.log_density_gradient(hmc.model, theta)
+    logp, _ = BridgeStan.log_density_gradient(hmc.model, theta)
     return logp - 0.5 * rho' * (hmc.metric .* rho)
 end
 
 function leapfrog(hmc::HMCDiag, theta, rho)
     e = hmc.stepsize .* hmc.metric
-    lp, grad = Bridgestan.log_density_gradient(hmc.model, theta)
+    lp, grad = BridgeStan.log_density_gradient(hmc.model, theta)
     rho_p = rho + 0.5 * hmc.stepsize .* grad
-    for n in 1:hmc.steps
+    for n = 1:hmc.steps
         theta .+= e .* rho_p
-        lp, grad = Bridgestan.log_density_gradient(hmc.model, theta)
+        lp, grad = BridgeStan.log_density_gradient(hmc.model, theta)
         if n != hmc.steps
             rho_p .+= e .* grad
         end
@@ -36,7 +37,7 @@ function leapfrog(hmc::HMCDiag, theta, rho)
 end
 
 function sample(hmc::HMCDiag)
-    rho = randn(Bridgestan.param_unc_num(model))
+    rho = randn(BridgeStan.param_unc_num(model))
     logp = joint_logp(hmc, hmc.theta, rho)
     theta_prop, rho_prop = leapfrog(hmc, hmc.theta, rho)
     logp_prop = joint_logp(hmc, theta_prop, rho_prop)

julia/Project.toml

@@ -1,4 +1,4 @@
-name = "Bridgestan"
+name = "BridgeStan"
 uuid = "c88b6f0a-829e-4b0b-94b7-f06ab5908f5a"
 authors = ["Brian Ward <[email protected]>", "Bob Carpenter <[email protected]", "Edward Roualdes <[email protected]>"]
 version = "0.1.0"

julia/example.jl

@@ -1,16 +1,18 @@
-using Bridgestan
+using BridgeStan
+
+const BS = BridgeStan
 
 # Bernoulli
 # CMDSTAN=/path/to/cmdstan/ make stan/bernoulli/bernoulli
 
 bernoulli_lib = joinpath(@__DIR__, "../stan/bernoulli/bernoulli_model.so")
 bernoulli_data = joinpath(@__DIR__, "../stan/bernoulli/bernoulli.data.json")
 
-smb = Bridgestan.StanModel(bernoulli_lib, bernoulli_data);
-x = rand(Bridgestan.param_unc_num(smb));
+smb = BS.StanModel(bernoulli_lib, bernoulli_data);
+x = rand(BS.param_unc_num(smb));
 q = @. log(x / (1 - x));        # unconstrained scale
 
-lp, grad = Bridgestan.log_density_gradient(smb, q, jacobian = 0)
+lp, grad = BS.log_density_gradient(smb, q, jacobian = 0)
 
 println()
 println("log_density and gradient of Bernoulli model:")
@@ -25,10 +27,10 @@ println()
 multi_lib = joinpath(@__DIR__, "../stan/multi/multi_model.so")
 multi_data = joinpath(@__DIR__, "../stan/multi/multi.data.json")
 
-smm = Bridgestan.StanModel(multi_lib, multi_data)
-x = randn(Bridgestan.param_unc_num(smm));
+smm = BS.StanModel(multi_lib, multi_data)
+x = randn(BS.param_unc_num(smm));
 
-lp, grad = Bridgestan.log_density_gradient(smm, x)
+lp, grad = BS.log_density_gradient(smm, x)
 
 println("log_density and gradient of Multivariate Gaussian model:")
 println((lp, grad))
@@ -39,15 +41,15 @@ println()
 include("./MCMC.jl")
 using Statistics
 
-model = Bridgestan.StanModel(multi_lib, multi_data);
+model = BS.StanModel(multi_lib, multi_data);
 
 stepsize = 0.25
 steps = 10
 hmcd = HMCDiag(model, stepsize, steps);
 
 M = 10_000
-theta = zeros(M, Bridgestan.param_unc_num(model))
-for m in 1:M
+theta = zeros(M, BS.param_unc_num(model))
+for m = 1:M
     theta[m, :] .= sample(hmcd)
 end