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model-api/src/model-builder/code-blocks/prior/_with-covariates-template_

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+    ## on the coefficients for each covariate.
+    for(m in 1:length(X[1, ])) {
+        Beta[m] ~ dnorm(0, VAGUE_COEF_PRIOR_SLOPE)
+    }

model-api/src/model-builder/code-blocks/prior/beta

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+    ## on sigma for each site and/or stratum.
+    for(k in 1:y.n.strata) {
+        for(j in 1:y.n.site[k]) {
+            VARIANCES_SPEC_SWITCH
+        }
+        VARIANCES_SPEC_HYPERS_SWITCH
+    }

model-api/src/model-builder/code-blocks/prior/beta-binomial

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+    ## on sigma for each site and/or stratum.
+    for(k in 1:y.n.strata) {
+        for(j in 1:y.n.site[k]) {
+            VARIANCES_SPEC_SWITCH
+        }
+        VARIANCES_SPEC_HYPERS_SWITCH
+    }

model-api/src/model-builder/code-blocks/prior/gamma

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+    ### on sigma.y for each stratum.
+    #for(k in 1:y.n.strata) {
+    #    sigma.y[k] ~ dunif(0, 500)
+    #}
+
+    ### on sigma for each site and/or stratum.
+    for(k in 1:y.n.strata) {
+        for(j in 1:y.n.site[k]) {
+            VARIANCES_SPEC_SWITCH
+        }
+        VARIANCES_SPEC_HYPERS_SWITCH
+    }

model-api/src/model-builder/code-blocks/prior/gen-pois

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+    # Prior for delta parameter of GP distribution.
+    for(k in 1:y.n.strata) {
+        delta[k] ~ dunif(-1, 1)
+    }
+    C <- 10000

model-api/src/model-builder/code-blocks/prior/hurdle-ordinal-latent-beta-g0

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+    ## on the coefficients for the likelihood associated with the hurdle.
+    for(k in 1:y.n.strata) {
+        mu.G0[k] ~ dnorm(0, pow(1.5, -2))  # flat prior on proportion
+        sigma.G0[k] ~ dgamma(0.01, 0.01)
+        tau.G0[k] <- 1 / max(1E-12, sigma.G0[k]^2)
+        G1[k] ~ dnorm(0, pow(1.5, -2))
+        VARIANCES_SPEC_HYPERS_SWITCH
+        for(j in 1:y.n.site[k]) {
+            G0[j, k] ~ dnorm(mu.G0[k], tau.G0[k])
+            G[j, 1, k] <- G0[j, k]
+            G[j, 2, k] <- G1[k]
+            VARIANCES_SPEC_SWITCH
+        }
+    }

model-api/src/model-builder/code-blocks/prior/hurdle-ordinal-latent-beta-g0-b-drop

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+    ## on the coefficients for the likelihood associated with the hurdle.
+    for(k in 1:y.n.strata) {
+        mu.G0[k] ~ dnorm(0, pow(1.5, -2))  # flat prior on proportion
+        sigma.G0[k] ~ dgamma(0.01, 0.01)
+        tau.G0[k] <- 1 / max(1E-12, sigma.G0[k]^2)
+        G1[k] ~ dnorm(0, pow(1.5, -2))
+        VARIANCES_SPEC_HYPERS_SWITCH
+        for(j in 1:y.n.site[k]) {
+            G0[j, k] ~ dnorm(mu.G0[k], tau.G0[k])
+            G[j, 1, k] <- G0[j, k]
+            G[j, 2, k] <- G1[k]
+            VARIANCES_SPEC_SWITCH
+        }
+    }

model-api/src/model-builder/code-blocks/prior/hurdle-ordinal-latent-beta-g0-bg-drop

@@ -0,0 +1,12 @@
+    ## on the coefficients for the likelihood associated with the hurdle.
+    for(k in 1:y.n.strata) {
+        mu.G0[k] ~ dnorm(0, pow(1.5, -2))  # flat prior on proportion
+        sigma.G0[k] ~ dgamma(0.01, 0.01)
+        tau.G0[k] <- 1 / max(1E-12, sigma.G0[k]^2)
+        VARIANCES_SPEC_HYPERS_SWITCH
+        for(j in 1:y.n.site[k]) {
+            G0[j, k] ~ dnorm(mu.G0[k], tau.G0[k])
+            G[j, 1, k] <- G0[j, k]
+            VARIANCES_SPEC_SWITCH
+        }
+    }

model-api/src/model-builder/code-blocks/prior/hurdle-ordinal-latent-beta-g0-bg-drop-fixed-g0

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+    ## on the coefficients for the likelihood associated with the hurdle.
+    for(k in 1:y.n.strata) {
+        mu.G0[k] ~ dnorm(0, 1 / 1.5^2)
+        VARIANCES_SPEC_HYPERS_SWITCH
+        for(j in 1:y.n.site[k]) {
+            G0[j, k] <- mu.G0[k]
+            G[j, 1, k] <- G0[j, k]
+            VARIANCES_SPEC_SWITCH
+        }
+    }

model-api/src/model-builder/code-blocks/prior/hurdle-ordinal-latent-beta-g0-g-drop

@@ -0,0 +1,12 @@
+    ## on the coefficients for the likelihood associated with the hurdle.
+    for(k in 1:y.n.strata) {
+        mu.G0[k] ~ dnorm(0, pow(1.5, -2))  # flat prior on proportion
+        sigma.G0[k] ~ dgamma(0.01, 0.01)
+        tau.G0[k] <- 1 / max(1E-12, sigma.G0[k]^2)
+        VARIANCES_SPEC_HYPERS_SWITCH
+        for(j in 1:y.n.site[k]) {
+            G0[j, k] ~ dnorm(mu.G0[k], tau.G0[k])
+            G[j, 1, k] <- G0[j, k]
+            VARIANCES_SPEC_SWITCH
+        }
+    }

model-api/src/model-builder/code-blocks/prior/hurdle-ordinal-latent-beta-g0-g-drop-fixed-g0

@@ -0,0 +1,10 @@
+    ## on the coefficients for the likelihood associated with the hurdle.
+    for(k in 1:y.n.strata) {
+        mu.G0[k] ~ dnorm(0, 1 / 1.5^2)
+        VARIANCES_SPEC_HYPERS_SWITCH
+        for(j in 1:y.n.site[k]) {
+            G0[j, k] <- mu.G0[k]
+            G[j, 1, k] <- G0[j, k]
+            VARIANCES_SPEC_SWITCH
+        }
+    }

model-api/src/model-builder/code-blocks/prior/hurdle-ordinal-latent-beta-g0-g1

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+    ## on slope and intercept parameters and their covariance matrix for each
+    ## site within each stratum.
+    for(k in 1:y.n.strata) {
+        VARIANCES_SPEC_HYPERS_SWITCH
+        for(j in 1:y.n.site[k]) {
+            G[j, 1:2, k] ~ dmnorm(G.hat[j, 1:2, k], Tau.G[, 1:2, k])
+            G.hat[j, 1, k] <- mu.G0[k]
+            G.hat[j, 2, k] <- mu.G1[k]
+            VARIANCES_SPEC_SWITCH
+        }
+        mu.G0[k] ~ dnorm(0, pow(1.5, -2))
+        mu.G1[k] ~ dnorm(0, pow(1.5, -2))
+        Tau.G[1:2, 1:2, k] <- inverse(Sigma.G[1:2, 1:2, k])
+        Sigma.G[1, 1, k] <- sigma.G0[k]^2
+        sigma.G0[k] ~ dgamma(0.01, 0.01)
+        Sigma.G[2, 2, k] <- sigma.G1[k]^2
+        sigma.G1[k] ~ dgamma(0.01, 0.01)
+        Sigma.G[1, 2, k] <- rho.G[k] * sigma.G0[k] * sigma.G1[k]
+        Sigma.G[2, 1, k] <- Sigma.G[1, 2, k]
+        rho.G[k] ~ dunif(-1, 1)
+    }

model-api/src/model-builder/code-blocks/prior/hurdle-ordinal-latent-beta-g0-g1-b-drop

@@ -0,0 +1,21 @@
+    ## on slope and intercept parameters and their covariance matrix for each
+    ## site within each stratum.
+    for(k in 1:y.n.strata) {
+        VARIANCES_SPEC_HYPERS_SWITCH
+        for(j in 1:y.n.site[k]) {
+            G[j, 1:2, k] ~ dmnorm(G.hat[j, 1:2, k], Tau.G[, 1:2, k])
+            G.hat[j, 1, k] <- mu.G0[k]
+            G.hat[j, 2, k] <- mu.G1[k]
+            VARIANCES_SPEC_SWITCH
+        }
+        mu.G0[k] ~ dnorm(0, pow(1.5, -2))
+        mu.G1[k] ~ dnorm(0, pow(1.5, -2))
+        Tau.G[1:2, 1:2, k] <- inverse(Sigma.G[1:2, 1:2, k])
+        Sigma.G[1, 1, k] <- sigma.G0[k]^2
+        sigma.G0[k] ~ dgamma(0.01, 0.01)
+        Sigma.G[2, 2, k] <- sigma.G1[k]^2
+        sigma.G1[k] ~ dgamma(0.01, 0.01)
+        Sigma.G[1, 2, k] <- rho.G[k] * sigma.G0[k] * sigma.G1[k]
+        Sigma.G[2, 1, k] <- Sigma.G[1, 2, k]
+        rho.G[k] ~ dunif(-1, 1)
+    }

model-api/src/model-builder/code-blocks/prior/lognormal

@@ -0,0 +1,7 @@
+    ### on sigma for each site and/or stratum.
+    for(k in 1:y.n.strata) {
+        for(j in 1:y.n.site[k]) {
+            VARIANCES_SPEC_SWITCH
+        }
+        VARIANCES_SPEC_HYPERS_SWITCH
+    }

model-api/src/model-builder/code-blocks/prior/max-r

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+    ## max `response` for asymptotic functions.
+    maxR ~ dunif(0, 100)

model-api/src/model-builder/code-blocks/prior/negative-binomial

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+    ## on the variance of observations.
+    for(k in 1:y.n.strata) {
+      for(j in 1:y.n.site[k]) {
+          VARIANCES_SPEC_SWITCH
+      }
+      VARIANCES_SPEC_HYPERS_SWITCH
+    }

model-api/src/model-builder/code-blocks/prior/negative-binomial-simple

@@ -0,0 +1,4 @@
+    ## on kappa for each stratum.
+    for(k in 1:y.n.strata) {
+        kappa[k] ~ dunif(0, 10000)
+    }

model-api/src/model-builder/code-blocks/prior/ordinal-latent-normal

@@ -0,0 +1,12 @@
+    for(k in 1:y.n.strata) {
+        VARIANCES_SPEC_HYPERS_SWITCH
+        # This code allows for site- or strata-level variances: .01 is rate and
+        # shape parameter in flat gamma.
+        for(j in 1:y.n.site[k]) {
+            VARIANCES_SPEC_SWITCH
+        }
+    }
+    # Priors on cutpoints, 1 and nYlevels-1 are fixed (Kruschke 2nd ed., pg. 688)
+    for(k in 2:(nYlevels - 2)) {
+        theta[k] ~  dnorm(k + .05, 1/2^2)
+    }

model-api/src/model-builder/code-blocks/prior/with-covariates-hurdle-ordinal-latent-beta

@@ -0,0 +1,5 @@
+    ## on the coefficients for each covariate.
+    for(q in 1:length(X[1, ])) {
+        Beta[q] ~ dnorm(0, VAGUE_COEF_PRIOR_SLOPE)
+        Gamma[q] ~ dnorm(0, VAGUE_COEF_PRIOR_SLOPE)
+    }

model-api/src/model-builder/code-blocks/prior/with-covariates-hurdle-ordinal-latent-beta-b-drop

@@ -0,0 +1,4 @@
+    ## on the coefficients for each covariate.
+    for(q in 1:length(X[1, ])) {
+        Gamma[q] ~ dnorm(0, pow(1.5, -2))
+    }

model-api/src/model-builder/code-blocks/prior/with-covariates-hurdle-ordinal-latent-beta-g-drop

@@ -0,0 +1,4 @@
+    ## on the coefficients for each covariate.
+    for(q in 1:length(X[1, ])) {
+        Beta[q] ~ dnorm(0, VAGUE_COEF_PRIOR_SLOPE)
+    }

model-api/src/model-builder/code-blocks/prior/zero-inflated-beta-binomial

@@ -0,0 +1,8 @@
+        ## on `p.zero` for each stratum and sigma for each site and/or stratum.
+        for(k in 1:y.n.strata) {
+            p.zero[k] ~ dunif(0, 1)
+            for(j in 1:y.n.site[k]) {
+                VARIANCES_SPEC_SWITCH
+            }
+            VARIANCES_SPEC_HYPERS_SWITCH
+        }

model-api/src/model-builder/code-blocks/prior/zero-inflated-binomial

@@ -0,0 +1,4 @@
+        ## on `p.zero` for each stratum.
+        for(k in 1:y.n.strata) {
+            p.zero[k] ~ dunif(0, 1)
+        }

model-api/src/model-builder/code-blocks/prior/zero-inflated-negative-binomial

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+    ## on `kappa`, `p.zero`, and `tau.epsilon` for each stratum.
+    for(k in 1:y.n.strata) {
+        kappa[k] ~ dunif(0, 10000)
+        p.zero[k] ~ dunif(0, 1)
+    }
+    tau.epsilon <- 1

model-api/src/model-builder/code-blocks/prior/zero-inflated-poisson

@@ -0,0 +1,4 @@
+        ## on `p.zero` for each stratum.
+        for(k in 1:y.n.strata) {
+            p.zero[k] ~ dunif(0, 1)
+        }