469 cran

DESCRIPTION

@@ -1,9 +1,10 @@
 Type: Package
 Package: mmrm
 Title: Mixed Models for Repeated Measures
-Version: 0.3.12.9003
+Version: 0.3.13
 Authors@R: c(
-    person("Daniel", "Sabanes Bove", , "daniel.sabanes_bove@rconis.com", role = c("aut", "cre")),
+    person("Daniel", "Sabanes Bove", , "daniel.sabanes_bove@rconis.com", role = c("aut", "cre"),
+           comment = c(ORCID = "0000-0002-0176-9239")),
     person("Liming", "Li", , "liming.li@roche.com", role = "aut"),
     person("Julia", "Dedic", , "julia.dedic@roche.com", role = "aut"),
     person("Doug", "Kelkhoff", , "doug.kelkhoff@roche.com", role = "aut"),
@@ -21,7 +22,8 @@ Authors@R: c(
     person("Gilead Sciences, Inc.", role = c("cph", "fnd")),
     person("F. Hoffmann-La Roche AG", role = c("cph", "fnd")),
     person("Merck Sharp & Dohme, Inc.", role = c("cph", "fnd")),
-    person("AstraZeneca plc", role = c("cph", "fnd"))
+    person("AstraZeneca plc", role = c("cph", "fnd")),
+    person("inferential.biostatistics GmbH", role = c("cph", "fnd"))
   )
 Description: Mixed models for repeated measures (MMRM) are a popular
     choice for analyzing longitudinal continuous outcomes in randomized

NAMESPACE

@@ -44,19 +44,15 @@ export(refit_multiple_optimizers)
 export(std_start)
 export(tidy)
 import(checkmate)
-importFrom(Matrix,.bdiag)
 importFrom(Rcpp,evalCpp)
 importFrom(Rdpack,reprompt)
 importFrom(generics,augment)
 importFrom(generics,glance)
 importFrom(generics,tidy)
-importFrom(lifecycle,deprecated)
 importFrom(methods,is)
 importFrom(nlme,VarCorr)
-importFrom(parallel,clusterApply)
 importFrom(stats,AIC)
 importFrom(stats,BIC)
-importFrom(stats,acf)
 importFrom(stats,coef)
 importFrom(stats,deviance)
 importFrom(stats,drop.terms)
@@ -69,7 +65,4 @@ importFrom(stats,residuals)
 importFrom(stats,simulate)
 importFrom(stats,terms)
 importFrom(stats,vcov)
-importFrom(stringr,boundary)
-importFrom(tibble,as_tibble)
-importFrom(utils,modifyList)
 useDynLib(mmrm, .registration = TRUE)

NEWS.md

@@ -1,21 +1,21 @@
-# mmrm 0.3.12.9003
+# mmrm 0.3.13
 
 ### Bug Fixes
 
-- Previously `emmeans` will return `NA` for spatial covariance structure. This is fixed now.
-- Previously `car::Anova` will give incorrect results if an interaction term is included and the order of the covariate of interest is not the first categorical variable. This is fixed now.
-- Previously `car::Anova` will fail if the model does not contain intercept. This is fixed now.
-- Previously, `mmrm` will ignore contrasts defined for covariates in the input data set. This is fixed now.
-- Previously, `predict` will always require the response to be valid, even for unconditional predictions. This is fixed now and unconditional prediction do not require the response to be valid anymore.
 - When running with `TMB` package versions below 1.9.15, MMRM fit results are not completely reproducible. While this may not be relevant for most applications, because the numerical differences are very small, we now issue a warning to the user if this is the case. We advise users to upgrade their `TMB` package versions to 1.9.15 or higher to ensure reproducibility.
+- Previously, `mmrm` ignored contrasts defined for covariates in the input data set. This is fixed now.
+- Previously, `predict` always required the response to be valid, even for unconditional predictions. This is fixed now and unconditional prediction does not require the response to be valid or present any longer.
+- `model.frame` has been updated to ensure that the `na.action` works correctly.
+- Previously `emmeans::emmeans` returned `NA` for spatial covariance structures. This is fixed now.
+- Previously `car::Anova` gave incorrect results if an interaction term is included and the covariate of interest was not the first categorical variable. This is fixed now.
+- Previously `car::Anova` failed if the model did not contain an intercept. This is fixed now.
 
 ### Miscellaneous
 
 - Upon fitting an MMRM, it is checked whether a not reproducible optimization feature of `TMB` is turned on. If so, a warning is issued to the user once per session.
-- `model.matrix` is updated to ensure that the `NA` values are dropped. Additionally, an argument `use_response` is added to decide whether records with `NA` values in the response should be discarded.
-- `model.frame` is updated to ensure that the `na.action` works.
-- `predict` is updated to allow duplicated subject IDs for unconditional prediction.
 - `mmrm` now checks on the positive definiteness of the covariance matrix `theta_vcov`. If it is not positive definite, non-convergence is messaged appropriately.
+- `model.matrix` has been updated to ensure that the `NA` values are dropped. Additionally, an argument `use_response` is added to decide whether records with `NA` values in the response should be discarded.
+- `predict` has been updated to allow duplicated subject IDs for unconditional prediction.
 
 # mmrm 0.3.12
 

R/component.R

@@ -1,6 +1,6 @@
 #' Component Access for `mmrm_tmb` Objects
 #'
-#' @description `r lifecycle::badge("experimental")`
+#' @description `r lifecycle::badge("stable")`
 #'
 #' @param object (`mmrm_tmb`)\cr the fitted MMRM.
 #' @param name (`character`)\cr the component(s) to be retrieved.

R/cov_struct.R

@@ -47,7 +47,7 @@ COV_TYPES <- local({ # nolint
 
 #' Covariance Types
 #'
-#' @description `r lifecycle::badge("maturing")`
+#' @description `r lifecycle::badge("stable")`
 #'
 #' @param form (`character`)\cr covariance structure type name form. One or
 #'   more of `"name"`, `"abbr"` (abbreviation), or `"habbr"` (heterogeneous
@@ -207,7 +207,7 @@ tmb_cov_type <- function(cov) {
 
 #' Define a Covariance Structure
 #'
-#' @description `r lifecycle::badge("maturing")`
+#' @description `r lifecycle::badge("stable")`
 #'
 #' @param type (`string`)\cr the name of the covariance structure type to use.
 #'   For available options, see `cov_types()`. If a type abbreviation is used
@@ -359,7 +359,7 @@ print.cov_struct <- function(x, ...) {
 
 #' Coerce into a Covariance Structure Definition
 #'
-#' @description `r lifecycle::badge("maturing")`
+#' @description `r lifecycle::badge("stable")`
 #'
 #' @details
 #' A covariance structure can be parsed from a model definition formula or call.

R/data.R

@@ -2,7 +2,7 @@
 
 #' Example Data on FEV1
 #'
-#' @description `r lifecycle::badge("experimental")`
+#' @description `r lifecycle::badge("stable")`
 #'
 #' @note Measurements of FEV1 (forced expired volume in one second) is a measure
 #' of how quickly the lungs can be emptied. Low levels of FEV1 may indicate
@@ -27,7 +27,7 @@
 
 #' Example Data on BCVA
 #'
-#' @description `r lifecycle::badge("experimental")`
+#' @description `r lifecycle::badge("stable")`
 #'
 #' @note Measurements of BCVA (best corrected visual acuity) is a measure of how
 #'   how many letters a person can read off of an eye chart using corrective
@@ -47,7 +47,7 @@
 
 #' Cache Data for `mmrm` Model Comparison
 #'
-#' @description `r lifecycle::badge("experimental")`
+#' @description `r lifecycle::badge("stable")`
 #'
 #' @note The cached data for comparison is used for the vignettes generation.
 #' Please make sure that this data is refreshed before each package release

R/fit.R

@@ -1,6 +1,6 @@
 #' Fitting an MMRM with Single Optimizer
 #'
-#' @description `r lifecycle::badge("experimental")`
+#' @description `r lifecycle::badge("stable")`
 #'
 #' This function helps to fit an MMRM using `TMB` with a single optimizer,
 #' while capturing messages and warnings.
@@ -127,7 +127,7 @@ h_summarize_all_fits <- function(all_fits) {
 
 #' Refitting MMRM with Multiple Optimizers
 #'
-#' @description `r lifecycle::badge("experimental")`
+#' @description `r lifecycle::badge("stable")`
 #'
 #' @param fit (`mmrm_fit`)\cr original model fit from [fit_single_optimizer()].
 #' @param ... Additional arguments passed to [mmrm_control()].
@@ -194,7 +194,7 @@ refit_multiple_optimizers <- function(fit,
 
 #' Control Parameters for Fitting an MMRM
 #'
-#' @description `r lifecycle::badge("experimental")`
+#' @description `r lifecycle::badge("stable")`
 #' Fine-grained specification of the MMRM fit details is possible using this
 #' control function.
 #'
@@ -313,7 +313,7 @@ mmrm_control <- function(n_cores = 1L,
 
 #' Fit an MMRM
 #'
-#' @description `r lifecycle::badge("experimental")`
+#' @description `r lifecycle::badge("stable")`
 #'
 #' This is the main function fitting the MMRM.
 #'

R/interop-emmeans.R

@@ -1,6 +1,6 @@
 #' Support for `emmeans`
 #'
-#' @description `r lifecycle::badge("experimental")`
+#' @description `r lifecycle::badge("stable")`
 #'
 #' This package includes methods that allow `mmrm` objects to be used
 #' with the `emmeans` package. `emmeans` computes estimated marginal means

R/mmrm-methods.R

@@ -1,6 +1,6 @@
 #' Methods for `mmrm` Objects
 #'
-#' @description `r lifecycle::badge("experimental")`
+#' @description `r lifecycle::badge("stable")`
 #'
 #' @param object (`mmrm`)\cr the fitted MMRM including Jacobian and call etc.
 #' @param ... not used.

R/mmrm-package.R

@@ -8,15 +8,8 @@
 #' @useDynLib mmrm, .registration = TRUE
 #' @importFrom Rcpp evalCpp
 #' @import checkmate
-#' @importFrom lifecycle deprecated
-#' @importFrom Matrix .bdiag
 #' @importFrom methods is
-#' @importFrom stats acf
-#' @importFrom stringr boundary
-#' @importFrom parallel clusterApply
 #' @importFrom Rdpack reprompt
-#' @importFrom utils modifyList
-#' @importFrom tibble as_tibble
 NULL
 
 #' @importFrom generics tidy

R/testing.R

@@ -1,6 +1,6 @@
 #' Calculation of Degrees of Freedom for One-Dimensional Contrast
 #'
-#' @description `r lifecycle::badge("experimental")`
+#' @description `r lifecycle::badge("stable")`
 #' Calculates the estimate, adjusted standard error, degrees of freedom,
 #' t statistic and p-value for one-dimensional contrast.
 #'
@@ -35,7 +35,7 @@ df_1d <- function(object, contrast) {
 
 #' Calculation of Degrees of Freedom for Multi-Dimensional Contrast
 #'
-#' @description `r lifecycle::badge("experimental")`
+#' @description `r lifecycle::badge("stable")`
 #' Calculates the estimate, standard error, degrees of freedom,
 #' t statistic and p-value for one-dimensional contrast, depending on the method
 #' used in [mmrm()].

R/tidiers.R

@@ -1,6 +1,6 @@
 #' Tidying Methods for `mmrm` Objects
 #'
-#' @description `r lifecycle::badge("experimental")`
+#' @description `r lifecycle::badge("stable")`
 #'
 #' These methods tidy the estimates from an `mmrm` object into a
 #' summary.

R/tmb-methods.R

@@ -1,6 +1,6 @@
 #' Methods for `mmrm_tmb` Objects
 #'
-#' @description `r lifecycle::badge("experimental")`
+#' @description `r lifecycle::badge("stable")`
 #'
 #' @param object (`mmrm_tmb`)\cr the fitted MMRM object.
 #' @param x (`mmrm_tmb`)\cr same as `object`.

R/tmb.R

@@ -467,7 +467,7 @@ h_mmrm_tmb_fit <- function(tmb_object,
 
 #' Low-Level Fitting Function for MMRM
 #'
-#' @description `r lifecycle::badge("experimental")`
+#' @description `r lifecycle::badge("stable")`
 #'
 #' This is the low-level function to fit an MMRM. Note that this does not
 #' try different optimizers or adds Jacobian information etc. in contrast to

R/utils.R

@@ -91,7 +91,7 @@ h_split_control <- function(control, ...) {
   assert_class(control, "mmrm_control")
   l <- length(control$optimizers)
   lapply(seq_len(l), function(i) {
-    ret <- modifyList(control, list(...))
+    ret <- utils::modifyList(control, list(...))
     ret$optimizers <- control$optimizers[i]
     ret
   })
@@ -196,10 +196,14 @@ h_partial_fun_args <- function(fun, ..., additional_attr = list()) {
   }
   do.call(
     structure,
-    args = modifyList(list(
-      .Data = fun, args = modifyList(args, a_args),
-      class = c("partial", "function")
-    ), additional_attr)
+    args = utils::modifyList(
+      list(
+        .Data = fun,
+        args = utils::modifyList(args, a_args),
+        class = c("partial", "function")
+      ),
+      additional_attr
+    )
   )
 }
 

inst/WORDLIST

@@ -12,6 +12,7 @@ DPD
 DeclareMathOperator
 Dohme
 FEV
+GmbH
 Gower
 Hoffmann
 Indexible
@@ -40,6 +41,7 @@ adh
 ansi
 ar
 ast
+biostatistics
 blockdiag
 boldsymbol
 cdots