shorten string length in example

CRAN-RELEASE

@@ -1,2 +1,2 @@
-This package was submitted to CRAN on 2021-05-18.
-Once it is accepted, delete this file and tag the release (commit 037b6a9).
+This package was submitted to CRAN on 2021-05-25.
+Once it is accepted, delete this file and tag the release (commit d85e775).

R/dfa_cv.R

@@ -21,26 +21,34 @@
 #' set.seed(42)
 #' s <- sim_dfa(num_trends = 1, num_years = 20, num_ts = 3)
 #' obs <- c(s$y_sim[1, ], s$y_sim[2, ], s$y_sim[3, ])
-#' long <- data.frame("obs" = obs, "ts" = sort(rep(1:3, 20)), "time" = rep(1:20, 3))
-#' m <- fit_dfa(y = long, iter = 50, chains = 1, data_shape = "long", sample = FALSE)
+#' long <- data.frame("obs" = obs, "ts" = sort(rep(1:3, 20)),
+#' "time" = rep(1:20, 3))
+#' m <- fit_dfa(y = long, iter = 50, chains = 1, data_shape = "long",
+#' sample = FALSE)
 #' # random folds
 #' fit_cv <- dfa_cv(m, cv_method = "loocv", n_folds = 5, iter = 50, chains = 1)
 #'
 #' # folds can also be passed in
 #' fold_ids <- sample(1:5, size = nrow(long), replace = TRUE)
-#' m <- fit_dfa(y = long, iter = 50, chains = 1, data_shape = "long", sample = FALSE)
-#' fit_cv <- dfa_cv(m, cv_method = "loocv", n_folds = 5, iter = 50, chains = 1, fold_ids = fold_ids)
+#' m <- fit_dfa(y = long, iter = 50, chains = 1, data_shape = "long",
+#' sample = FALSE)
+#' fit_cv <- dfa_cv(m, cv_method = "loocv", n_folds = 5, iter = 50, chains = 1,
+#' fold_ids = fold_ids)
 #'
 #' # do an example of leave-time-out cross validation where years are dropped
 #' fold_ids <- long$time
-#' m <- fit_dfa(y = long, iter = 50, chains = 1, data_shape = "long", sample = FALSE)
-#' fit_cv <- dfa_cv(m, cv_method = "loocv", iter = 100, chains = 1, fold_ids = fold_ids)
+#' m <- fit_dfa(y = long, iter = 50, chains = 1, data_shape = "long",
+#' sample = FALSE)
+#' fit_cv <- dfa_cv(m, cv_method = "loocv", iter = 100, chains = 1,
+#' fold_ids = fold_ids)
 #'
 #' # example with covariates and long format data
-#' obs_covar <- expand.grid("time" = 1:20, "timeseries" = 1:3, "covariate" = 1:2)
+#' obs_covar <- expand.grid("time" = 1:20, "timeseries" = 1:3,
+#' "covariate" = 1:2)
 #' obs_covar$value <- rnorm(nrow(obs_covar), 0, 0.1)
 #' obs <- c(s$y_sim[1, ], s$y_sim[2, ], s$y_sim[3, ])
-#' m <- fit_dfa(y = long, iter = 50, chains = 1, obs_covar = obs_covar, data_shape = "long", sample = FALSE)
+#' m <- fit_dfa(y = long, iter = 50, chains = 1, obs_covar = obs_covar,
+#' data_shape = "long", sample = FALSE)
 #' fit_cv <- dfa_cv(m, cv_method = "loocv", n_folds = 5, iter = 50, chains = 1)
 #' }
 #'

docs/pkgdown.yml

@@ -7,5 +7,5 @@ articles:
   compositional: compositional.html
   covariates: covariates.html
   estimate_process_sigma: estimate_process_sigma.html
-last_built: 2021-05-25T13:09Z
+last_built: 2021-05-25T16:39Z