Merge branch 'main' of https://github.com/ropensci/dynamite

ropensci · May 8, 2024 · 06a8d48 · 06a8d48
2 parents ca8bec3 + 5a0fd2c
commit 06a8d48
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diff --git a/R/dynamiteformula.R b/R/dynamiteformula.R
@@ -619,12 +619,14 @@ get_dag <- function(x, project = FALSE, covariates = FALSE,
       lag_dep_pa <- lag_dep[lag_dep$resp == resp[i], ]
       lag_dep_ch <- lag_dep[lag_dep$var == resp[i], ]
       lag_dep_new <- vector(mode = "list", length = nrow(lag_dep_ch))
-      for (j in seq_len(nrow(lag_dep_ch))) {
-        lag_dep_new[[j]] <- data.frame(
-          var = c(contemp_pa, lag_dep_pa$var),
-          order = c(rep(0L, k), lag_dep_pa$order) + lag_dep_ch$order[j],
-          resp = lag_dep_ch$resp[j]
-        )
+      if (nrow(lag_dep_pa) > 0L) {
+        for (j in seq_len(nrow(lag_dep_ch))) {
+          lag_dep_new[[j]] <- data.frame(
+            var = c(contemp_pa, lag_dep_pa$var),
+            order = c(rep(0L, k), lag_dep_pa$order) + lag_dep_ch$order[j],
+            resp = lag_dep_ch$resp[j]
+          )
+        }
       }
       lag_dep <- rbind(
         lag_dep[lag_dep$resp != resp[i] & lag_dep$var != resp[i], ],

diff --git a/R/plot.R b/R/plot.R
@@ -545,8 +545,8 @@ plot_varying <- function(coefs, level, alpha, scales, n_params) {
     )
   }
   title <- glue::glue(
-    "Posterior means and {100 * (1 - 2 * level)} %",
-    "intervals of the {title_spec}"
+    "Posterior means and {100 * (1 - 2 * level)} ",
+    "% intervals of the {title_spec}"
   )
   # avoid NSE notes from R CMD check
   time <- mean <- category <- parameter <- NULL

diff --git a/R/prepare_stan_input.R b/R/prepare_stan_input.R
@@ -479,8 +479,6 @@ initialize_multivariate_channel <- function(y, y_cg, y_name, cg_idx,
   list(channel = channel, sampling = sampling)
 }
 
-
-
 #' Default channel preparation
 #'
 #' Computes default channel-specific variables for Stan sampling,
@@ -1254,7 +1252,6 @@ prepare_channel_student <- function(y, Y, channel, sampling,
   out
 }
 
-
 #' Raise an error if factor type is not supported by a family
 #'
 #' @param y \[`character(1)`]\cr Response variable the error is related to.

diff --git a/R/priors.R b/R/priors.R
@@ -71,19 +71,20 @@ extract_vectorizable_priors <- function(priors, y) {
 prepare_common_priors <- function(priors, M, shrinkage, P,
                                   correlated_nu, correlated_lf) {
   common_priors <- NULL
-  if (shrinkage) {
-    common_priors <- ifelse_(
-      is.null(priors),
-      data.frame(
-        parameter = "xi",
-        response = "",
-        prior = "normal(0, 1)",
-        type = "xi",
-        category = ""
-      ),
-      priors[priors$type == "xi", ]
-    )
-  }
+  # Shrinkage feature removed for now
+  #if (shrinkage) {
+  #  common_priors <- ifelse_(
+  #    is.null(priors),
+  #    data.frame(
+  #      parameter = "xi",
+  #      response = "",
+  #      prior = "normal(0, 1)",
+  #      type = "xi",
+  #      category = ""
+  #    ),
+  #    priors[priors$type == "xi", ]
+  #  )
+  #}
   if (M > 1L && correlated_nu) {
     common_priors <- ifelse_(
       is.null(priors),

diff --git a/R/stanblocks_families.R b/R/stanblocks_families.R
@@ -704,13 +704,6 @@ loglik_lines_gaussian <- function(y, obs, idt, default, ...) {
 
 loglik_lines_multinomial <- function(idt, cvars, cgvars, backend,
                                      threading, ...) {
-  stopifnot_(
-    stan_version(backend) >= "2.24",
-    c(
-      "Multinomial family is not supported for this version of {.pkg {backend}}.",
-      `i` = "Please install a newer version of {.pkg {backend}}."
-    )
-  )
   cgvars$categories <- cgvars$y
   cgvars$y <- cgvars$y_cg
   cgvars$multinomial <- TRUE
@@ -2142,9 +2135,12 @@ model_lines_categorical <- function(y, idt, obs, family, priors,
       onlyif(has_fixed || has_varying, c("J_{y}", "K_{y}")),
       onlyif(has_X, "X")
     )
-    likelihood <- glue::glue(
-      "target += reduce_sum({distr}_loglik_{y}_lpmf, {seq1T}, grainsize, ",
-      "{fun_args});"
+    likelihood <- paste_rows(
+      paste0(
+        "target += reduce_sum({distr}_loglik_{y}_lpmf, {seq1T}, grainsize, ",
+        "{fun_args});"
+      ),
+      .indent = idt(1)
     )
   } else {
     likelihood <- loglik_lines_categorical(
@@ -2211,7 +2207,16 @@ model_lines_gaussian <- function(y, obs, idt, priors,
   paste_rows(priors, model_text, .parse = FALSE)
 }
 
-model_lines_multinomial <- function(cvars, cgvars, idt, threading, ...) {
+model_lines_multinomial <- function(cvars, cgvars, idt, backend,
+                                    threading, ...) {
+  stopifnot_(
+    stan_version(backend) >= "2.24",
+    c(
+      "Multinomial family is not supported for
+       this version of {.pkg {backend}}.",
+      `i` = "Please install a newer version of {.pkg {backend}}."
+    )
+  )
   cgvars$priors <- lapply(
     cgvars$y[-1L],
     function(s) {

diff --git a/README.Rmd b/README.Rmd
@@ -43,7 +43,7 @@ The `dynamite` package is developed with the support of the Research Council of
 
 ## Installation
 
-You can install the most recent stable version of `dynmite` from [CRAN](https://cran.r-project.org/package=dynamite) or the development version from [R-universe](https://r-universe.dev/search/) by running one the following lines:
+You can install the most recent stable version of `dynamite` from [CRAN](https://cran.r-project.org/package=dynamite) or the development version from [R-universe](https://r-universe.dev/search/) by running one the following lines:
 
 ```{r, eval = FALSE}
 install.packages("dynamite")
@@ -73,7 +73,8 @@ gaussian_example_fit <- dynamite(
 ```{r, echo = FALSE}
 set.seed(1)
 library("dynamite")
-gaussian_example_fit <- update(gaussian_example_fit,
+gaussian_example_fit <- update(
+  gaussian_example_fit,
   iter = 2000, warmup = 1000, thin = 1,
   chains = 2, cores = 2, refresh = 0
 )
@@ -99,7 +100,7 @@ Traceplots and density plots for time-invariant parameters:
 plot(gaussian_example_fit, plot_type = "trace", types = "beta")
 ```
 
-Posterior predictive samples for the first 4 groups (samples based on the posterior distribution of model parameters and observed data on first time point):
+Posterior predictive samples for the first 4 groups (using the samples based on the posterior distribution of the model parameters and observed data on the first time point):
 
 ```{r, warning=FALSE, fig.width = 9, fig.height = 4}
 library("ggplot2")

diff --git a/README.md b/README.md
@@ -54,7 +54,7 @@ on DMPMs and the `dynamite` package, see the related
 
 ## Installation
 
-You can install the most recent stable version of `dynmite` from
+You can install the most recent stable version of `dynamite` from
 [CRAN](https://cran.r-project.org/package=dynamite) or the development
 version from [R-universe](https://r-universe.dev/search/) by running one
 the following lines:
@@ -105,8 +105,8 @@ print(gaussian_example_fit)
 #> 
 #> Elapsed time (seconds):
 #>         warmup sample
-#> chain:1  5.801  3.542
-#> chain:2  5.658  3.544
+#> chain:1  5.546  3.396
+#> chain:2  5.533  3.524
 #> 
 #> Summary statistics of the time- and group-invariant parameters:
 #> # A tibble: 6 × 10
@@ -144,9 +144,9 @@ plot(gaussian_example_fit, plot_type = "trace", types = "beta")
 
 <img src="man/figures/README-unnamed-chunk-9-1.png" style="display: block; margin: auto;" />
 
-Posterior predictive samples for the first 4 groups (samples based on
-the posterior distribution of model parameters and observed data on
-first time point):
+Posterior predictive samples for the first 4 groups (using the samples
+based on the posterior distribution of the model parameters and observed
+data on the first time point):
 
 ``` r
 library("ggplot2")