From 59693f11bb3d76c75316bd5da72ef009309f5b56 Mon Sep 17 00:00:00 2001
From: AnestisTouloumis <A.Touloumis@brighton.ac.uk>
Date: Wed, 6 Nov 2019 09:53:42 +0000
Subject: [PATCH] minor changes

---
 DESCRIPTION                 |  6 +++---
 R/SimCorMultRes_internals.R | 15 ++++++++-------
 R/rnorta.R                  |  4 ++--
 R/rsmvnorm.R                |  2 +-
 4 files changed, 14 insertions(+), 13 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index db6496f..b5714a3 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -4,18 +4,18 @@ Title: Simulates Correlated Multinomial Responses
 Description: Simulates correlated multinomial responses conditional on a marginal model specification.
 Version: 1.7.2
 Depends: R(>= 2.15.0)
-Imports: 
+Imports:
     evd,
     methods,
     stats
 Suggests: 
     bookdown,
+    covr,
     gee,
     knitr,
     markdown,
     multgee (>= 1.2),
-    testthat,
-    covr
+    testthat
 Authors@R:
     person(given = "Anestis",
            family = "Touloumis",
diff --git a/R/SimCorMultRes_internals.R b/R/SimCorMultRes_internals.R
index 5f9d22a..a4d0423 100644
--- a/R/SimCorMultRes_internals.R
+++ b/R/SimCorMultRes_internals.R
@@ -64,12 +64,13 @@ check_correlation_matrix <- function(correlation_matrix, cluster_size, rfctn,
 }
 
 check_xformula <- function(xformula) {
-  linear_predictor_formula <- as.formula(xformula)
-  if (length(paste0(attr(terms(linear_predictor_formula), "variables"))) == 1) {
+  linear_predictor_formula <- stats::as.formula(xformula)
+  if (length(paste0(attr(stats::terms(linear_predictor_formula),
+                         "variables"))) == 1) {
     stop("No covariates were found in 'formula' ")
-  }
-  if (attr(terms(linear_predictor_formula), "intercept") == 0) {
-    linear_predictor_formula <- update(linear_predictor_formula, ~ . + 1)
+    }
+  if (attr(stats::terms(linear_predictor_formula), "intercept") == 0) {
+    linear_predictor_formula <- stats::update(linear_predictor_formula, ~ . + 1)
   }
   linear_predictor_formula
 }
@@ -165,7 +166,7 @@ check_betas <- function(betas, cluster_size) {
 create_linear_predictor <- function(betas, cluster_size,
                                     linear_predictor_formula, xdata, rfctn,
                                     categories_no = NULL) {
-  xmat <- model.matrix(linear_predictor_formula, data = xdata)
+  xmat <- stats::model.matrix(linear_predictor_formula, data = xdata)
   if (rfctn == "rmult.bcl") {
     xmat <- apply(xmat, 2, function(x) rep(x, each = categories_no))
     if (length(betas) != (cluster_size * categories_no * ncol(xmat))) {
@@ -272,7 +273,7 @@ create_output <- function(simulated_responses, sample_size, cluster_size,
         sep = ""
       )
     }
-  sim_model_frame <- model.frame(
+  sim_model_frame <- stats::model.frame(
     formula = linear_predictor_formula, data = xdata
   )
   simdata <- data.frame(y, sim_model_frame, id, time)
diff --git a/R/rnorta.R b/R/rnorta.R
index 3f569ec..a9a3651 100644
--- a/R/rnorta.R
+++ b/R/rnorta.R
@@ -105,7 +105,7 @@ rnorta <- function(R = R, cor.matrix = cor.matrix, distr = distr, # nolint
       )
     }
   }
-  ans <- pnorm(ans)
+  ans <- stats::pnorm(ans)
   for (i in seq_len(ncol(cor.matrix))) {
     quantile_function <- get(quantile_functions[i], mode = "function")
     if (!is.function(quantile_function)) {
@@ -115,7 +115,7 @@ rnorta <- function(R = R, cor.matrix = cor.matrix, distr = distr, # nolint
     if (!is.null(qparameters)) {
       formals(quantile_function)[pmatch(
         names(qparameters[[i]]),
-        formalArgs(quantile_function)
+        methods::formalArgs(quantile_function)
       )] <- qparameters[[i]]
     }
     ans[, i] <- quantile_function(ans[, i])
diff --git a/R/rsmvnorm.R b/R/rsmvnorm.R
index 72c0daa..be4c933 100644
--- a/R/rsmvnorm.R
+++ b/R/rsmvnorm.R
@@ -50,6 +50,6 @@ rsmvnorm <- function(R = R, cor.matrix = cor.matrix) { # nolint
     stop("'cor.matrix' must be a positive definite matrix")
   }
   p <- ncol(correlation_matrix)
-  ans <- matrix(rnorm(R * p), R, p) %*% chol(correlation_matrix)
+  ans <- matrix(stats::rnorm(R * p), R, p) %*% chol(correlation_matrix)
   ans
 }