adding old usage of roxygen

AnestisTouloumis · Aug 12, 2020 · 3853ed2 · 3853ed2 · lintr-bot · Aug 12, 2020
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diff --git a/DESCRIPTION b/DESCRIPTION
@@ -28,4 +28,5 @@ BugReports: http://github.com/AnestisTouloumis/SimCorMultRes/issues
 License: GPL-3
 VignetteBuilder: knitr
 RoxygenNote: 7.1.1
+Roxygen: list(old_usage = TRUE)
 Encoding: UTF-8
diff --git a/README.md b/README.md
@@ -117,9 +117,9 @@ browseVignettes("SimCorMultRes")
 
 To cite R package SimCorMultRes in publications, please use:
 
-  Touloumis, A. (2016). Simulating Correlated Binary and
-  Multinomial Responses under Marginal Model Specification: The
-  SimCorMultRes Package. The R Journal 8:2, 79-91.
+  Touloumis, A. (2016). Simulating Correlated Binary and Multinomial
+  Responses under Marginal Model Specification: The SimCorMultRes
+  Package. The R Journal 8:2, 79-91.
 
 A BibTeX entry for LaTeX users is
 

diff --git a/man/rbin.Rd b/man/rbin.Rd
diff --git a/man/rmult.acl.Rd b/man/rmult.acl.Rd
diff --git a/man/rmult.bcl.Rd b/man/rmult.bcl.Rd
diff --git a/man/rmult.clm.Rd b/man/rmult.clm.Rd
diff --git a/man/rmult.crm.Rd b/man/rmult.crm.Rd
diff --git a/vignettes/SimCorMultRes.Rmd b/vignettes/SimCorMultRes.Rmd
@@ -99,8 +99,9 @@ simulated_nominal_dataset <- rmult.bcl(clsize = cluster_size,
 suppressPackageStartupMessages(library("multgee"))
 # fitting a GEE model
 nominal_gee_model <- nomLORgee(y ~ x1 + x2,
-                               data = simulated_nominal_dataset$simdata, id = id,
-                               repeated = time, LORstr="time.exch")
+                               data = simulated_nominal_dataset$simdata,
+                               id = id, repeated = time,
+                               LORstr = "time.exch")
 # checking regression coefficients
 round(coef(nominal_gee_model), 2)
 ```
@@ -213,7 +214,8 @@ x <- rnorm(sample_size * cluster_size)
 categories_no <- 5
 # correlation matrix for the NORTA method
 latent_correlation_matrix <- diag(1, (categories_no - 1) * cluster_size) +
-  kronecker(toeplitz(c(0, rep(0.24, categories_no - 2))), matrix(1, cluster_size, cluster_size))
+  kronecker(toeplitz(c(0, rep(0.24, categories_no - 2))),
+            matrix(1, cluster_size, cluster_size))
 # simulation of ordinal responses
 simulated_ordinal_dataset <- rmult.crm(clsize = cluster_size,
                                        intercepts = beta_intercepts,