Prepare for release 1.4.0

DESCRIPTION

@@ -1,11 +1,12 @@
 Package: rbmi
 Title: Reference Based Multiple Imputation
-Version: 1.3.1
+Version: 1.4.0
 Authors@R: c(
     person("Craig", "Gower-Page", email = "[email protected]", role = c("aut", "cre")),
     person("Alessandro", "Noci", email = "[email protected]", role = c("aut")),
     person("Marcel", "Wolbers", email = "[email protected]", role = "ctb"),
     person("Isaac", "Gravestock", email = "[email protected]", role = "aut"),
+    person("Daniel", "Sabanes Bove", email = "[email protected]", role = c("aut")),
     person("F. Hoffmann-La Roche AG", role = c("cph", "fnd"))
     )
 Description: Implements standard and reference based multiple imputation methods for continuous

NEWS.md

@@ -1,17 +1,20 @@
 
 
-# rbmi Development Version
-
-* Fixed bug where `lsmeans(.weights = "proportional_em")` would error if there was only a single categorical variable in the dataset. (#412)
-* Removed native pipes `|>` and lambda functions `\(x)` from code base to ensure package is backwards compatible with older versions of R. (#474)
+# rbmi 1.4.0
 
 ## Breaking Changes
 
-* Deprecated the `burn_in` and `burn_between` arguments in `method_bayes()` in favour of using the `warmup` and `thin` arguments, respectively, in the new `control` list produced by `control_bayes`. This is to align with the `rstan` package.
+* Deprecated the `burn_in` and `burn_between` arguments in `method_bayes()` in favour of using the `warmup` and `thin` arguments, respectively, in the new `control` list produced by `control_bayes`. This is to align with the `rstan` package. (#477)
 
 ## New Features
 
-* Added `control_bayes()` function to allow expert users to specify additional control arguments for the MCMC computations using `rstan`.
+* Added `control_bayes()` function to allow expert users to specify additional control arguments for the MCMC computations using `rstan`. (#477)
+
+## Bug Fixes
+
+* Fixed bug where `lsmeans(.weights = "proportional_em")` would error if there was only a single categorical variable in the dataset. (#412)
+* Removed native pipes `|>` and lambda functions `\(x)` from code base to ensure package is backwards compatible with older versions of R. (#474)
+
 
 # rbmi 1.3.1
 

cran-comments.md

@@ -1,6 +1,6 @@
 ## Summary of Submission
 
-This version of the package fixes an issue that was causing the unit tests to crash on CRAN. The issue was due to the package not correctly clearing out the cache of previously compiled `rstan` models.
+This version of the package removes references to native pipes `|>` and lambda functions `\(x)` to ensure the package is backwards compatible with older versions of R. This update also exposes the control options for `rstan` allowing the user greater control over the MCMC computations.
 
 
 ## R CMD check results
@@ -20,4 +20,6 @@ The package was tested in the following environments:
 
 ## Downstream dependencies
 
-There are currently no downstream dependencies for this package
+The following revdeps were checked:
+
+- term.rbmi - no issues detected

vignettes/CondMean_Inference.html

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@@ -714,7 +715,7 @@ <h1><span class="header-section-number">6</span> Custom imputation strategies</h
 <span id="cb6-17"><a href="#cb6-17" tabindex="-1"></a><span class="co">#&gt;     pars &lt;- list(mu = mu, sigma = sigma)</span></span>
 <span id="cb6-18"><a href="#cb6-18" tabindex="-1"></a><span class="co">#&gt;     return(pars)</span></span>
 <span id="cb6-19"><a href="#cb6-19" tabindex="-1"></a><span class="co">#&gt; }</span></span>
-<span id="cb6-20"><a href="#cb6-20" tabindex="-1"></a><span class="co">#&gt; &lt;bytecode: 0x7fd7e885e158&gt;</span></span>
+<span id="cb6-20"><a href="#cb6-20" tabindex="-1"></a><span class="co">#&gt; &lt;bytecode: 0x7faeda3ba7c0&gt;</span></span>
 <span id="cb6-21"><a href="#cb6-21" tabindex="-1"></a><span class="co">#&gt; &lt;environment: namespace:rbmi&gt;</span></span></code></pre></div>
 <p>To further illustrate this for a simple example, assume that a new strategy is to be implemented as follows:
 - The marginal mean of the imputation distribution is equal to the marginal mean trajectory for the subject according to their assigned group and covariates up to the ICE.