preparing for release

AnestisTouloumis · Jul 25, 2019 · 4c627e9 · 4c627e9
1 parent 8f68085
commit 4c627e9
Show file tree

Hide file tree

Showing 18 changed files with 106 additions and 117 deletions.
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -2,7 +2,7 @@ Package: SimCorMultRes
 Type: Package
 Title: Simulates Correlated Multinomial Responses
 Description: Simulates correlated multinomial responses conditional on a marginal model specification.
-Version: 1.6.9
+Version: 1.7.0
 Depends: R(>= 2.15.0)
 Imports: 
     evd,

diff --git a/R/rbin.R b/R/rbin.R
@@ -125,7 +125,8 @@
 #'   dep = sqrt(1 - 0.9),
 #'   model = "log", d = cluster_size
 #' )
-#' simulated_latent_variables <- simulated_latent_variables1 - simulated_latent_variables2 # nolintr
+#' simulated_latent_variables <- simulated_latent_variables1 -
+#'   simulated_latent_variables2
 #' simulated_binary_dataset <- rbin(
 #'   clsize = cluster_size,
 #'   intercepts = beta_intercepts, betas = beta_coefficients,

diff --git a/R/rmult.acl.R b/R/rmult.acl.R
@@ -105,21 +105,16 @@
 #' xdata <- data.frame(x1, x2)
 #' identity_matrix <- diag(4)
 #' equicorrelation_matrix <- toeplitz(c(1, rep(0.95, cluster_size - 1)))
-#' latent_correlation_matrix <- kronecker(
-#'   equicorrelation_matrix,
-#'   identity_matrix
-#' )
-#' simulated_ordinal_dataset <- rmult.acl(
-#'   clsize = cluster_size,
+#' latent_correlation_matrix <- kronecker(equicorrelation_matrix,
+#'   identity_matrix)
+#' simulated_ordinal_dataset <- rmult.acl(clsize = cluster_size,
 #'   intercepts = beta_intercepts, betas = beta_coefficients,
 #'   xformula = ~ x1 + x2, xdata = xdata,
-#'   cor.matrix = latent_correlation_matrix
-#' )
+#'   cor.matrix = latent_correlation_matrix)
 #' suppressPackageStartupMessages(library("multgee"))
 #' ordinal_gee_model <- ordLORgee(y ~ x1 + x2,
 #'   data = simulated_ordinal_dataset$simdata, id = id, repeated = time,
-#'   LORstr = "time.exch", link = "acl"
-#' )
+#'   LORstr = "time.exch", link = "acl")
 #' round(coef(ordinal_gee_model), 2)
 #' @export
 rmult.acl <- function(clsize = clsize, intercepts = intercepts, betas = betas, # nolint

diff --git a/R/rmult.bcl.R b/R/rmult.bcl.R
@@ -103,17 +103,15 @@
 #' xdata <- data.frame(x1, x2)
 #' equicorrelation_matrix <- toeplitz(c(1, rep(0.95, cluster_size - 1)))
 #' identity_matrix <- diag(categories_no)
-#' latent_correlation_matrix <- kronecker(equicorrelation_matrix, identity_matrix) # nolint
-#' simulated_nominal_dataset <- rmult.bcl(
-#'   clsize = cluster_size,
+#' latent_correlation_matrix <- kronecker(equicorrelation_matrix,
+#'   identity_matrix)
+#' simulated_nominal_dataset <- rmult.bcl(clsize = cluster_size,
 #'   ncategories = categories_no, betas = betas, xformula = ~ x1 + x2,
-#'   xdata = xdata, cor.matrix = latent_correlation_matrix
-#' )
+#'   xdata = xdata, cor.matrix = latent_correlation_matrix)
 #' suppressPackageStartupMessages(library("multgee"))
 #' nominal_gee_model <- nomLORgee(y ~ x1 + x2,
 #'   data = simulated_nominal_dataset$simdata, id = id, repeated = time,
-#'   LORstr = "time.exch"
-#' )
+#'   LORstr = "time.exch")
 #' round(coef(nominal_gee_model), 2)
 #' @export
 rmult.bcl <- function(clsize = clsize, ncategories = ncategories, betas = betas, # nolint

diff --git a/R/rmult.clm.R b/R/rmult.clm.R
@@ -104,27 +104,21 @@
 #' beta_coefficients <- matrix(c(1, 2, 3, 4), 4, 1)
 #' x <- rep(rnorm(sample_size), each = cluster_size)
 #' latent_correlation_matrix <- toeplitz(c(1, 0.85, 0.5, 0.15))
-#' simulated_ordinal_dataset <- rmult.clm(
-#'   clsize = cluster_size,
+#' simulated_ordinal_dataset <- rmult.clm(clsize = cluster_size,
 #'   intercepts = beta_intercepts, betas = beta_coefficients, xformula = ~x,
-#'   cor.matrix = latent_correlation_matrix, link = "probit"
-#' )
+#'   cor.matrix = latent_correlation_matrix, link = "probit")
 #' head(simulated_ordinal_dataset$simdata, n = 8)
 #'
 #' ## Same sampling scheme except that the parameter vector is time-stationary.
 #' set.seed(12345)
-#' simulated_ordinal_dataset <- rmult.clm(
-#'   clsize = cluster_size, betas = 1,
+#' simulated_ordinal_dataset <- rmult.clm(clsize = cluster_size, betas = 1,
 #'   xformula = ~x, cor.matrix = latent_correlation_matrix,
-#'   intercepts = beta_intercepts, link = "probit"
-#' )
+#'   intercepts = beta_intercepts, link = "probit")
 #' ## Fit a GEE model (Touloumis et al., 2013) to estimate the regression
 #' ## coefficients.
 #' library(multgee)
-#' ordinal_gee_model <- ordLORgee(y ~ x,
-#'   id = id, repeated = time,
-#'   link = "probit", data = simulated_ordinal_dataset$simdata
-#' )
+#' ordinal_gee_model <- ordLORgee(y ~ x, id = id, repeated = time,
+#'   link = "probit", data = simulated_ordinal_dataset$simdata)
 #' coef(ordinal_gee_model)
 #' @export
 rmult.clm <- function(clsize = clsize, intercepts = intercepts, betas = betas, # nolint

diff --git a/R/rmult.crm.R b/R/rmult.crm.R
@@ -110,11 +110,9 @@
 #' ones_matrix <- matrix(1, cluster_size, cluster_size)
 #' latent_correlation_matrix <- identity_matrix +
 #'   kronecker(equicorrelation_matrix, ones_matrix)
-#' simulated_ordinal_dataset <- rmult.crm(
-#'   clsize = cluster_size,
+#' simulated_ordinal_dataset <- rmult.crm(clsize = cluster_size,
 #'   intercepts = beta_intercepts, betas = beta_coefficients, xformula = ~x,
-#'   cor.matrix = latent_correlation_matrix, link = "probit"
-#' )
+#'   cor.matrix = latent_correlation_matrix, link = "probit")
 #' head(simulated_ordinal_dataset$Ysim)
 #' @export
 rmult.crm <- function(clsize = clsize, intercepts = intercepts, betas = betas, # nolint

diff --git a/R/rnorta.R b/R/rnorta.R
@@ -50,44 +50,35 @@
 #' latent_correlation_matrix <- toeplitz(c(1, rep(0.8, 2)))
 #' latent_correlation_matrix
 #' common_marginal_distribution <- rep("qlogis", 3)
-#' simulated_logistic_responses <- rnorta(
-#'   R = sample_size,
+#' simulated_logistic_responses <- rnorta(R = sample_size,
 #'   cor.matrix = latent_correlation_matrix,
-#'   distr = common_marginal_distribution
-#' )
+#'   distr = common_marginal_distribution)
 #'
 #' ## The following lines exemplify the NORTA method.
 #' set.seed(1)
-#' simulated_normal_responses <- rsmvnorm(
-#'   R = sample_size,
-#'   cor.matrix = latent_correlation_matrix
-#' )
-#' all(simulated_logistic_responses == qlogis(pnorm(simulated_normal_responses))) # nolintr
+#' simulated_normal_responses <- rsmvnorm(R = sample_size,
+#'   cor.matrix = latent_correlation_matrix)
+#' norta_simulated <- qlogis(pnorm(simulated_normal_responses))
+#' all(simulated_logistic_responses == norta_simulated)
 #'
 #' ## Change the marginal distributions to standard normal, standard
 #' ## logistic and standard extreme value distribution.
 #' set.seed(1)
 #' different_marginal_distributions <- c("qnorm", "qlogis", "qgumbel")
-#' simulated_logistic_responses <- rnorta(
-#'   R = sample_size,
+#' simulated_logistic_responses <- rnorta(R = sample_size,
 #'   cor.matrix = latent_correlation_matrix,
-#'   distr = different_marginal_distributions
-#' )
+#'   distr = different_marginal_distributions)
 #' cor(simulated_logistic_responses)
 #' colMeans(simulated_logistic_responses)
 #' apply(simulated_logistic_responses, 2, sd)
 #'
 #' ## Same as above but using parameter values other than the default ones.
 #' set.seed(1)
-#' qpars <- list(
-#'   c(mean = 1, sd = 9), c(location = 2, scale = 1),
-#'   c(loc = 3, scale = 1)
-#' )
-#' simulated_logistic_responses <- rnorta(
-#'   R = sample_size,
+#' qpars <- list(c(mean = 1, sd = 9), c(location = 2, scale = 1),
+#'   c(loc = 3, scale = 1))
+#' simulated_logistic_responses <- rnorta(R = sample_size,
 #'   cor.matrix = latent_correlation_matrix,
-#'   distr = different_marginal_distributions, qparameters = qpars
-#' )
+#'   distr = different_marginal_distributions, qparameters = qpars)
 #' cor(simulated_logistic_responses)
 #' colMeans(simulated_logistic_responses)
 #' apply(simulated_logistic_responses, 2, sd)

diff --git a/R/rsmvnorm.R b/R/rsmvnorm.R
@@ -19,10 +19,8 @@
 #' set.seed(1)
 #' sample_size <- 10000
 #' correlation_matrix <- toeplitz(c(1, 0.4))
-#' simulated_normal_responses <- rsmvnorm(
-#'   R = sample_size,
-#'   cor.matrix = correlation_matrix
-#' )
+#' simulated_normal_responses <- rsmvnorm(R = sample_size,
+#'   cor.matrix = correlation_matrix)
 #' colMeans(simulated_normal_responses)
 #' apply(simulated_normal_responses, 2, sd)
 #' cor(simulated_normal_responses)

diff --git a/README.md b/README.md
@@ -4,7 +4,7 @@
 # SimCorMultRes: Simulates Correlated Multinomial Responses
 
 [![Github
-version](https://img.shields.io/badge/GitHub%20-1.6.9-orange.svg)](%22commits/master%22)
+version](https://img.shields.io/badge/GitHub%20-1.7.0-orange.svg)](%22commits/master%22)
 [![Travis-CI Build
 Status](https://travis-ci.org/AnestisTouloumis/SimCorMultRes.svg?branch=master)](https://travis-ci.org/AnestisTouloumis/SimCorMultRes)
 [![Project Status: Active The project has reached a stable, usable state

diff --git a/inst/NEWS b/inst/NEWS
@@ -1,3 +1,6 @@
+Version 1.7.0 [2019-07-25]
+ * Reseased on CRAN.
+
 Version 1.6.9 [2019-07-24]
  * Cosmetic changes.
 

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