![[Experimental]](figures/lifecycle-experimental.svg)
Value
A tibble with one row per model parameter, ordered and labeled
the same way as summary(): change points first, then mu, then the
other distributional parameters, then ar/ma components - each with
segment and dpar columns.
Details
Shows the effective, resolved prior distributions on the familiar SD/scale
parameterization rather than JAGS precision. Symbolic expressions in bounds
(e.g. min(x) or max(x)) may be retained in symbolic form in compact output,
while verbose = TRUE displays the underlying rule, description, source, and kind.
Examples
prior_summary(demo_fit) # Show the effective priors and bounds
#> # A tibble: 7 × 5
#> parameter segment dpar prior bounds
#> <chr> <int> <chr> <chr> <chr>
#> 1 cp_1 2 cp dirichlet(alpha = 1) [min(…
#> 2 cp_2 3 cp dirichlet(alpha = 1) [cp_1…
#> 3 Intercept_1 1 mu student_t(df = 3, location = 14.2, scale = 6) none
#> 4 time_2 2 mu student_t(df = 3, location = 0, scale = 0.06… none
#> 5 Intercept_3 3 mu student_t(df = 3, location = 14.2, scale = 6) none
#> 6 time_3 3 mu student_t(df = 3, location = 0, scale = 0.06… none
#> 7 sigma_1 1 sigma student_t(df = 3, location = 0, scale = 6) [0.00…
prior_summary(demo_fit, verbose = TRUE) # Include their rules and sources
#> # A tibble: 7 × 9
#> parameter segment dpar prior bounds rule description source kind
#> <chr> <int> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 cp_1 2 cp dirichlet(alp… [min(… diri… Uniform or… defau… dist…
#> 2 cp_2 3 cp dirichlet(alp… [cp_1… diri… Uniform or… defau… dist…
#> 3 Intercept_1 1 mu student_t(df … none stud… Robustly c… defau… dist…
#> 4 time_2 2 mu student_t(df … none stud… Regularizi… defau… dist…
#> 5 Intercept_3 3 mu student_t(df … none stud… Robustly c… defau… dist…
#> 6 time_3 3 mu student_t(df … none stud… Regularizi… defau… dist…
#> 7 sigma_1 1 sigma student_t(df … [0.00… stud… Positive r… defau… dist…