Fix #72

DESCRIPTION

@@ -66,5 +66,5 @@ Config/testthat/edition: 3
 Encoding: UTF-8
 Language: en-US
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.3.0
+RoxygenNote: 7.3.1
 LazyData: true

R/brm_package.R

@@ -8,17 +8,18 @@
 #'   it supports a simplified interfaced to reduce difficulty
 #'   and align with the best practices of the life sciences.
 #' @references
-#'   * Bürkner PC (2017). brms: An R Package for Bayesian
-#'     Multilevel Models Using Stan.
-#'.    J Stat Softw, 80(1), 1-28.
-#'     doi:10.18637/jss.v080.i01
-#'   * Mallinckrodt CH, Lane PW, Schnell D, et al (2008).
-#'     Recommendations for the Primary Analysis of Continuous Endpoints
-#'     in Longitudinal Clinical Trials.
+#'   * Bürkner, P.-C. (2017), "brms: An R package for Bayesian
+#'     multilevel models using Stan,"
+#'     Journal of Statistical Software, 80, 1–28.
+#'     https://doi.org/10.18637/jss.v080.i01.
+#'   * Mallinckrodt, C. H., Lane, P. W., Schnell, D., and others (2008),
+#'     "Recommendations for the primary analysis of continuous endpoints
+#'     in longitudinal clinical trials,"
 #'     Ther Innov Regul Sci, 42, 303–319.
-#'     doi:10.1177/009286150804200402
-#'   * Mallinckrodt C and Lipkovich I (2017). Analyzing Longitudinal Trial
-#'   Data -- A Practical Guide. Chapman `&` Hall/ CRC Biostatistics Series.
+#'     https://doi.org/10.1177/009286150804200402.
+#'   * Mallinckrodt, C. H., and Lipkovich, I. (2017),
+#'     Analyzing longitudinal clinical trial data: A practical guide,
+#'     CRC Press, Taylor & Francis Group.
 #' @family help
 #' @importFrom brms brm brmsformula get_prior prior unstr
 #' @importFrom coda as.mcmc

inst/bibliography.bib

@@ -22,6 +22,14 @@ @article{
   doi={10.1177/009286150804200402}
 }
 
+@book{
+  mallinckrodt2017,
+  title={Analyzing Longitudinal Clinical Trial Data: A Practical Guide},
+  author={Mallinckrodt, C.H. and Lipkovich, Ilya},
+  year={2017},
+  publisher={CRC Press, Taylor & Francis Group}
+}
+
 @article{
   lenth2016,
   title={Least-Squares Means: The R Package lsmeans},

vignettes/methods.Rmd

@@ -12,7 +12,7 @@ vignette: >
   %\VignetteEncoding{UTF-8}
 ---
 
-The `brms.mmrm` R package implements a mixed model of repeated measures (MMRM) [@mallinckrodt2008], a popular and flexible model to analyze continuous longitudinal outcomes. `brms.mmrm` focuses on marginal MMRMs for randomized controlled parallel studies with discrete time points, where each subject shares the same set of time points. Whereas the [`mmrm`](https://openpharma.github.io/mmrm/) package is frequentist, `brms.mmrm` fits models in Bayesian fashion using [`brms`](https://paul-buerkner.github.io/brms/) [@burkner2017].
+The `brms.mmrm` R package implements a mixed model of repeated measures (MMRM), a popular and flexible model to analyze continuous longitudinal outcomes (@mallinckrodt2008, @mallinckrodt2017). `brms.mmrm` focuses on marginal MMRMs for randomized controlled parallel studies with discrete time points, where each subject shares the same set of time points. Whereas the [`mmrm`](https://openpharma.github.io/mmrm/) package is frequentist, `brms.mmrm` fits models in Bayesian fashion using [`brms`](https://paul-buerkner.github.io/brms/) [@burkner2017].
 
 # Model