diff --git a/R/io.R b/R/io.R
index a482479..b2de032 100644
--- a/R/io.R
+++ b/R/io.R
@@ -378,6 +378,19 @@ format.mtmutObj <- function(x, ...) {
 #' @param ... other parameters passed to \code{\link[base]{format}} or \code{\link[base]{print}}.
 #' @return a string
 #' @export
+#' @examples
+#' ## Use the example data
+#' f <- system.file("extdata", "mini_dataset.tsv.gz", package = "scMitoMut")
+#' ## Create a temporary h5 file
+#' ## In real case, we keep the h5 in project folder for future use
+#' f_h5_tmp <- tempfile(fileext = ".h5")
+#' ## Load the data with parse_table function
+#' f_h5 <- parse_table(f, sep = "\t", h5_file = f_h5_tmp)
+#' f_h5
+#' ## open the h5 file and create a mtmutObj object
+#' x <- open_h5_file(f_h5)
+#' x
+#' print(x)
 print.mtmutObj <- function(x, ...) {
     format(x, ...)
 }
@@ -561,7 +574,7 @@ filter_loc <- function(mtmutObj, min_cell = 5, model = "bb", p_threshold = 0.05,
         res_2 <- parallel::mclapply(res$loc, function(xi) {
             d <- read_locus(mtmutObj, xi)
             pval <- get_pval(mtmutObj, xi, model = model, method = p_adj_method)
-            n <- sum((d$coverage - d$alt_depth) >= alt_count_threshold & pval <= p_threshold, na.rm = T)
+            n <- sum((d$coverage - d$alt_depth) >= alt_count_threshold & pval <= p_threshold, na.rm = TRUE)
             data.frame(loc = xi, mut_cell_n = n)
         }) %>% rbindlist()
         res <- res_2[mut_cell_n >= min_cell]
diff --git a/R/stat_ensemble.R b/R/stat_ensemble.R
index f2d2cea..b3d5622 100644
--- a/R/stat_ensemble.R
+++ b/R/stat_ensemble.R
@@ -41,7 +41,7 @@ get_bm_pval <- function(x, method = "none") {
 #' res
 #'
 #' @export
-process_locus_bmbb <- function(mtmutObj, loc, dom_allele = NULL, return_data = FALSE, bb_over_bm = T, bb_over_bm_p = 0.05, bb_over_bm_adj = "fdr", ...) {
+process_locus_bmbb <- function(mtmutObj, loc, dom_allele = NULL, return_data = FALSE, bb_over_bm = TRUE, bb_over_bm_p = 0.05, bb_over_bm_adj = "fdr", ...) {
 
     if (!is(mtmutObj, "mtmutObj")) {
         stop("mtmutObj must be a mtmutObj object.")
@@ -117,7 +117,7 @@ process_locus_bmbb <- function(mtmutObj, loc, dom_allele = NULL, return_data = F
 #' x <- filter_loc(x, min_cell = 5, model = "bb", p_threshold = 0.05, p_adj_method = "fdr")
 #' x
 #' @export
-run_model_fit <- function(mtmutObj, mc.cores = getOption("mc.cores", 1L), bb_over_bm = T, bb_over_bm_p = 0.05, bb_over_bm_adj = "fdr") {
+run_model_fit <- function(mtmutObj, mc.cores = getOption("mc.cores", 1L), bb_over_bm = TRUE, bb_over_bm_p = 0.05, bb_over_bm_adj = "fdr") {
 
     if (!is(mtmutObj, "mtmutObj")) {
         stop("mtmutObj must be a mtmutObj object.")
diff --git a/man/print.mtmutObj.Rd b/man/print.mtmutObj.Rd
index c3673d1..ba08ee6 100644
--- a/man/print.mtmutObj.Rd
+++ b/man/print.mtmutObj.Rd
@@ -23,3 +23,17 @@ a string
 \description{
 The print method for mtmutObj object.
 }
+\examples{
+## Use the example data
+f <- system.file("extdata", "mini_dataset.tsv.gz", package = "scMitoMut")
+## Create a temporary h5 file
+## In real case, we keep the h5 in project folder for future use
+f_h5_tmp <- tempfile(fileext = ".h5")
+## Load the data with parse_table function
+f_h5 <- parse_table(f, sep = "\t", h5_file = f_h5_tmp)
+f_h5
+## open the h5 file and create a mtmutObj object
+x <- open_h5_file(f_h5)
+x
+print(x)
+}
diff --git a/man/process_locus_bmbb.Rd b/man/process_locus_bmbb.Rd
index efd151e..160a6c8 100644
--- a/man/process_locus_bmbb.Rd
+++ b/man/process_locus_bmbb.Rd
@@ -9,7 +9,7 @@ process_locus_bmbb(
   loc,
   dom_allele = NULL,
   return_data = FALSE,
-  bb_over_bm = T,
+  bb_over_bm = TRUE,
   bb_over_bm_p = 0.05,
   bb_over_bm_adj = "fdr",
   ...
diff --git a/man/run_model_fit.Rd b/man/run_model_fit.Rd
index 4dcc142..55d4013 100644
--- a/man/run_model_fit.Rd
+++ b/man/run_model_fit.Rd
@@ -7,7 +7,7 @@
 run_model_fit(
   mtmutObj,
   mc.cores = getOption("mc.cores", 1L),
-  bb_over_bm = T,
+  bb_over_bm = TRUE,
   bb_over_bm_p = 0.05,
   bb_over_bm_adj = "fdr"
 )