test_that("summary.hsstan",

{

    expect_error(summary(hs.gauss, prob=c(0.2, 0.8)),

                 "'prob' must be a single value between 0 and 1")

    expect_error(summary(hs.gauss, prob=1),

                 "'prob' must be a single value between 0 and 1")

    out <- summary(hs.gauss)

    expect_is(out, "matrix")

    expect_equal(colnames(out),

                 c("mean", "sd", "2.5%", "97.5%", "n_eff", "Rhat"))

    expect_equal(nrow(out),

                 P + 1 + 1) # intercept and extra factor level for X1

    out <- summary(hs.gauss, pars="X1")

    expect_equal(rownames(out),

                 c("X1b", "X1c", "X10"))

    expect_equal(summary(hs.gauss, max.rows=0),

                 summary(hs.gauss))

    expect_equal(nrow(summary(hs.gauss, max.rows=5)), 5)

    out <- summary(hs.gauss, sort="n_eff", decreasing=FALSE)

    expect_true(all(diff(out[, "n_eff"]) > 0))

})

test_that("print.hsstan",

{

    expect_output(print(hs.gauss))

})

test_that("posterior_summary",

{

    out <- posterior_summary(1:100)

    expect_is(out,

              "matrix")

    expect_equal(nrow(out), 1)

    expect_equal(colnames(out),

                 c("mean", "sd", "2.5%", "97.5%"))

    expect_equal(as.numeric(out),

                 c(50.5, 29.01149198, 3.475, 97.525))

    out <- posterior_summary(hs.binom)

    expect_equal(rownames(out),

                 names(c(hs.binom$betas$unpenalized, hs.binom$betas$penalized)))

    expect_equal(out[, "mean"],

                 c(hs.binom$betas$unpenalized, hs.binom$betas$penalized))

})

test_that("sampler.stats",

{

    out <- sampler.stats(hs.gauss)

    expect_is(out, "matrix")

    expect_equal(colnames(out),

                 c("accept.stat", "stepsize", "divergences", "treedepth",

                   "gradients", "warmup", "sample"))

    expect_equal(rownames(out),

                 c(paste0("chain:", 1:chains), "all"))

})

test_that("nsamples",

{

    expect_equal(nsamples(hs.gauss), iters * chains / 2)

})