Merge pull request #166 from lindeloev/v0.3.4

mcp 0.3.4 (make CRAN happy)

.github/workflows/check-standard.yaml

@@ -5,6 +5,7 @@ on:
     branches: [main, master]
   pull_request:
     branches: [main, master]
+  workflow_dispatch:
 
 name: R-CMD-check
 

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: mcp
 Title: Regression with Multiple Change Points
-Version: 0.3.3
-Date: 2023-03-19
+Version: 0.3.4
+Date: 2024-03-14
 URL: https://lindeloev.github.io/mcp/
 BugReports: https://github.com/lindeloev/mcp/issues
 Authors@R: 
@@ -15,7 +15,7 @@ License: GPL-2
 Encoding: UTF-8
 Language: en-US
 LazyData: true
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.1
 Roxygen: list(markdown = TRUE)
 Depends: R (>= 3.5.0)
 Imports:
@@ -27,7 +27,7 @@ Imports:
 	loo (>= 2.1.0),
 	bayesplot (>= 1.7.0),
 	tidybayes (>= 3.0.0),
-	dplyr (>= 1.0.0),
+	dplyr (>= 1.1.1),
 	magrittr (>= 1.5),
 	tidyr (>= 1.0.0),
 	tidyselect (>= 0.2.5),

NEWS.md

@@ -1,6 +1,17 @@
+# mcp 0.3.4
+
+This is a bug fix release.
+
+## Bug fixes
+
+ * Now respects the `cores` argument to `mcp()`.
+ * Document all function arguments and remove documentation for removed arguments.
+
+
+
 # mcp 0.3.3
 
-This is a bug fix release
+This is a bug fix release.
 
 ## Bug fixes
 

R/get_formula.R

@@ -219,10 +219,7 @@ get_formula_str = function(ST, par_x, ytype = "ct", init = FALSE) {
 #' @keywords internal
 #' @inheritParams mcp
 #' @param formula_str string. Returned by `get_formula`.
-#' @param par_trials String. For binomial models: name of trials column.
-#' @param pars_pop List of population parameters which the user must provide.
-#' @param pars_varying List varying parameters. They will default to zero
-#'   (optional for the user).
+#' @param pars List of user-provided parameters, in the format of fit$pars.
 #' @param nsegments Positive integer. Number of segments, typically `nrow(ST)`.
 #' @return A string with R code for the fit$simulate() function corresponding to the model.
 #' @encoding UTF-8

R/mcp-package.R

@@ -1,26 +1,2 @@
-#' Regression with Multiple Change Points
-#'
-#' @docType package
-#' @name mcp-package
-#' @aliases mcp-package
-#'
-#' @description
-#' The \pkg{mcp} package provides an interface to fit regression models with
-#' multiple change points between generalized linear segments, optionally with
-#' per-segment variance and autocorrelation structures.
-#'
-#' The main function of \pkg{mcp} is the `mcp()` function, which uses a formula
-#' syntax to specify a wide range of change point models. Based on the supplied
-#' data, formulas, and additional information, it writes JAGS code on the fly
-#' and use \pkg{rstan} to fit the model, optionally in parallel to speed up
-#' sampling. You will need to install JAGS for `mcp()` to work.
-#'
-#' A large number of post-processing methods can be applied. These include
-#'
-#' * Summarise fits using `summary()`, `fixef()`, and `ranef()`.
-#' * Visualize fits using `plot()` and individual parameters using `plot_pars()`.
-#' * Test hypotheses using `hypothesis()` and `loo()`.
-#'
-#' Extensive documentation with worked examples is available at the
-#' [mcp website](https://lindeloev.github.io/mcp/).
-NULL
+#' @keywords internal
+"_PACKAGE"

R/mcpfit_methods.R

@@ -150,7 +150,7 @@ get_summary = function(fit, width, varying = FALSE, prior = FALSE) {
 
     # Add simulation to the beginning of the list
     estimates = estimates %>%
-      dplyr::left_join(simulated, by = "name", multiple = "all") %>%
+      dplyr::left_join(simulated, by = "name", relationship = "one-to-one") %>%
       dplyr::mutate(match = ifelse(.data$sim > .data$lower & .data$sim < .data$upper, yes = "OK", no = "")) %>%
       dplyr::select("name", "match", "sim", "mean", "lower", "upper")
   }
@@ -536,7 +536,7 @@ tidy_samples = function(
 #' @param scale One of
 #'   * "response": return on the observed scale, i.e., after applying the inverse link function.
 #'   * "linear": return on the parameter scale (where the linear trends are modelled).
-#' @param ... Currently ignored.
+#' @param ... Currently unused
 #' @return
 #'   * If `summary = TRUE`: A `tibble` with the posterior mean for each row in `newdata`,
 #'     If `newdata` is `NULL`, the data in `fit$data` is used.
@@ -574,7 +574,8 @@ pp_eval = function(
   arma = TRUE,
   nsamples = NULL,
   samples_format = "tidy",
-  scale = 'response'
+  scale = 'response',
+  ...
 ) {
   # Recodings
   fit = object
@@ -660,7 +661,7 @@ pp_eval = function(
       newdata,
       tidy_samples(fit, population = TRUE, varying = varying, prior = prior, nsamples = nsamples),
       by = unique(varying_info$cols),
-      multiple = "all"
+      relationship = "many-to-many"
     ) %>%
       dplyr::mutate(!!returnvar := rlang::exec(fit$simulate, !!!., type = type_for_simulate, rate = rate, which_y = which_y, arma = arma, add_attr = FALSE, scale = scale))
   } else {
@@ -686,7 +687,7 @@ pp_eval = function(
 
       # Apply original order and put newdata as the first columns
       dplyr::arrange(.data$data_row) %>%
-      dplyr::left_join(newdata, by = "data_row") %>%
+      dplyr::left_join(newdata, by = "data_row", relationship = "one-to-one") %>%
       dplyr::select(dplyr::one_of(colnames(newdata)), !!returnvar, "error", -"data_row")
 
 
@@ -697,7 +698,7 @@ pp_eval = function(
         dplyr::mutate(quantile = 100 * .data$quantile) %>%
         tidyr::pivot_wider(names_from = "quantile", names_prefix = "Q", values_from = "y")
 
-      df_return = dplyr::left_join(df_return, quantiles_fit, by = as.character(xvar))
+      df_return = dplyr::left_join(df_return, quantiles_fit, by = as.character(xvar), relationship = "many-to-one")
     }
     return(data.frame(df_return))
   } else if (samples_format == "tidy") {

R/misc.R

@@ -29,11 +29,14 @@ get_arma_order = function(pars_arma) {
 # Ask reminder questions for CRAN export
 release_questions = function() {
   c(
-    "Have you run the test of fits? options(test_mcp_fits = TRUE)",
-    "Have you built the README plots and checked them? source('vignettes/figures/make_README_plots.R')",
-    "Have you re-built the site using pkgdown::build_site() AFTER deleting caches of articles?",
-    "Have you checked all articles and plots after re-building the site?",
-    "Have you run the script to insert the correct logo.png in the HTML meta?"
+    "TEST: Have you run the extensive tests? options(test_mcp_allmodels = TRUE)",
+    "TEST: Have you run the test of fits? options(test_mcp_fits = TRUE)",
+    "TEST: Have you run `revdepcheck::revdep_check()`?",
+
+    "DOC: Have you built the README plots and checked them? source('vignettes/figures/make_README_plots.R')",
+    "DOC: Have you re-built the site using pkgdown::build_site() AFTER deleting caches of articles in 'vignettes/*_cache/'?",
+    "DOC: Have you checked all articles and plots after re-building the site?",
+    "DOC: Have you run the script to insert the correct logo.png in the HTML meta?"
   )
 }
 

R/plot.R

@@ -286,9 +286,6 @@ plot.mcpfit = function(x,
 #' @keywords internal
 #' @param fit An `mcpfit` object
 #' @param facet_by `NULL` or a a string, like `plot.mcpfit(..., facet_by = "id").`
-#' @param include Boolean. If `TRUE` and `!is.null(facet_by)`, only return
-#'   densities for the change points "affected" by `facet_by`. If `FALSE` and `!is.null(facet_by)`,
-#'   return all densities except those "affected" by `facet_by`. Has no effect if `is.null(facet_by)`
 #' @return A `ggplot2::stat_density` geom representing the change point densities.
 geom_cp_density = function(fit, facet_by, limits_y) {
   dens_scale = 0.2  # Proportion of plot height

R/run_jags.R

@@ -91,7 +91,7 @@ run_jags = function(data,
   } else if (cores == "all" || cores > 1) {
     # PARALLEL using the future package and one chain per worker
     message("Parallel sampling in progress...")
-    future::plan(future::multisession, .skip = TRUE)
+    future::plan(future::multisession, workers = cores, .skip = TRUE)
     samples = future.apply::future_lapply(
       sample(1:1000, n.chains),  # Random seed to JAGS
       n.chains = 1,

cran-comments.md

@@ -1,3 +1,26 @@
+# mcp 0.3.4
+
+## Notes for the reviewer
+
+ * This fixes NOTES about arguments with missing documentation.
+
+## R CMD check results
+There were no ERRORs or WARNINGs except the usual artificial ones (see "Expected 
+NOTEs and ERRORs" in the bottom of this file).
+
+## Test environments
+Github Actions: oldrel, release, and devel on Ubuntu. 
+Github Actions: release on MacOS and Windows.
+
+## Downstream dependencies
+We checked 1 reverse dependencies, comparing R CMD check results across CRAN and dev versions of this package.
+
+ * We saw 0 new problems
+ * We failed to check 0 packages
+
+
+
+
 # mcp 0.3.3
 
 ## Notes for the reviewer