Thanks Rob, that fixed the problem.
Solution: Upgrade the "Compat" package using Pkg.free("Compat")
julia> Pkg.free("Compat")
INFO: Freeing Compat
INFO: Upgrading Compat: v0.4.5 => v0.5.1
INFO: Upgrading GZip: v0.2.16 => v0.2.17
INFO: Upgrading PyCall: v0.8.1 => v0.8.2
Now the Stan test runs:
julia> Pkg.test("Stan")
INFO: Testing Stan
Running tests for Stan-j0.3-v0.2.0:
* test_utilities.jl *
`echo 2`
`echo 3 4`
`echo 5`
1
2
3
4
5
hello
hello
hello
hello
hello
hello
* test_cmdtype.jl *
File /home/gquinn/.julia/v0.3/Stan/test/tmp/bernoulli.stan will be updated.
name = "bernoulli"
nchains = 4
update = 1000
adapt = 1000
thin = 1
monitors = ASCIIString[]
model_file = "bernoulli.stan"
data_file = ""
output = Output()
file = ""
diagnostics_file = ""
refresh = 100
Optimize(Lbfgs(0.001,1.0e-8,1.0e-8,1.0e-8,5), 2000, false)
name = "bernoulli"
nchains = 4
update = 1000
adapt = 1000
thin = 1
monitors = ASCIIString[]
model_file = "bernoulli.stan"
data_file = ""
output = Output()
file = ""
diagnostics_file = ""
refresh = 100
method = Optimize()
algorithm = Lbfgs()
init_alpha = 0.001
tol_obj = 1.0e-8
tol_grad = 1.0e-8
tol_param = 1.0e-8
history_size = 5
iterations = 2000
save_iterations = false
Sample(1000, 1000, false, 1, Adapt(true,0.05,0.8,0.75,10.0,75,50,25), Hmc(Nuts(10),diag_e(),1.0,1.0))
method = Sample()
num_samples = 1000
num_warmup = 1000
save_warmup = false
thin = 1
algorithm = HMC()
engine = NUTS()
max_depth = 10
metric = diag_e
stepsize = 1.0
stepsize_jitter = 1.0
adapt = Adapt()
gamma = 0.05
delta = 0.8
kappa = 0.75
t0 = 10.0
init_buffer = 75
term_buffer = 50
window = 25
Optimize(Bfgs(0.001,1.0e-9,1.0e-8,1.0e-8), 2000, true)
method = Optimize()
algorithm = Bfgs()
init_alpha = 0.001
tol_obj = 1.0e-9
tol_grad = 1.0e-8
tol_param = 1.0e-8
iterations = 2000
save_iterations = true
Diagnose(Gradient(1.0e-6,1.0e-7))
method = Diagnose()