Update main function examples

.Rbuildignore

@@ -5,3 +5,4 @@
 ^\.github$
 ^codecov\.yml$
 ^.lintr
+^cran-comments\.md$

R/simulate_zero_inflated_beta_data.R

@@ -26,7 +26,6 @@
 #' @importFrom stats rnorm rbinom rbeta
 #' @export
 #' @examples
-#' \dontrun{
 #' simulate_zero_inflated_beta_random_effect_data(
 #'   subject_n = 100, time_n = 5,
 #'   X = as.matrix(c(rep(0, 50 * 5), rep(1, 50 * 5))),
@@ -36,7 +35,6 @@
 #'   v = 5,
 #'   sim_seed = 100
 #' )
-#' }
 simulate_zero_inflated_beta_random_effect_data <- function(subject_n = 50, time_n = 5, v = 2,
                                                            alpha = as.matrix(c(0, 0.5, -1)),
                                                            beta = as.matrix(c(-0.5, -0.5, 0.5)),

R/zibr.R

@@ -22,7 +22,6 @@
 #'
 #' @export
 #' @examples
-#' \dontrun{
 #' ## simulate some data
 #' sim <- simulate_zero_inflated_beta_random_effect_data(
 #'   subject_n = 100, time_n = 5,
@@ -34,13 +33,14 @@
 #'   v = 5,
 #'   sim_seed = 100
 #' )
+#'
 #' ## run zibr on the simulated data
 #' zibr_fit <- zibr(
 #'   logistic_cov = sim$X, beta_cov = sim$Z, Y = sim$Y,
 #'   subject_ind = sim$subject_ind, time_ind = sim$time_ind
 #' )
+#'
 #' zibr_fit
-#' }
 zibr <- function(logistic_cov,
                  beta_cov,
                  Y,

README.md

@@ -20,7 +20,14 @@ Accordingly, we can test three biologically relevant null hypotheses:
 - H0: α_j = 0 and β_j = 0 for each covariate X_j and Z_j. This is to joinly test the coefficients in both logistic and Beta components, if the covariates affect the taxon both in terms of presence/absence and its non-zero abundance.  
 
 ## Installation
-You can install our ZIBR package from Github
+You can install our ZIBR package from CRAN
+
+```r
+install.packages("ZIBR")
+```
+
+Or get the dev version from GitHub
+
 ```r
 #install.packages("devtools")
 devtools::install_github("PennChopMicrobiomeProgram/ZIBR")