diff --git a/DESCRIPTION b/DESCRIPTION
index 09771901..923a71cf 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,8 +1,8 @@
 Package: rgeoda
 Type: Package
 Title: R Library for Spatial Data Analysis
-Version: 0.0.10-4
-Date: 2023-07-01
+Version: 0.0.11-1
+Date: 2024-12-18
 Authors@R: 
     c(person(given = "Xun", family = "Li", email="lixun910@gmail.com", role=c("aut","cre")),
       person(given = "Luc", family = "Anselin", email="anselin@uchicago.edu", role="aut"))
@@ -33,8 +33,8 @@ Imports:
     Rcpp (>= 1.0.1)
 LinkingTo:
     Rcpp,
-    BH
-RoxygenNote: 7.1.1
+    BH (>= 1.87.0-1)
+RoxygenNote: 7.3.2
 Encoding: UTF-8
 Suggests: 
     wkb,
diff --git a/NAMESPACE b/NAMESPACE
index 878a1987..5058f683 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -59,6 +59,10 @@ export(min_distthreshold)
 export(min_neighbors)
 export(natural_breaks)
 export(neighbor_match_test)
+export(p_GeoDa)
+export(p_GeoDaTable)
+export(p_GeoDaWeight)
+export(p_LISA)
 export(percentile_breaks)
 export(quantile_breaks)
 export(queen_weights)
diff --git a/R/clustering.R b/R/clustering.R
index 8f82a405..c597bd91 100644
--- a/R/clustering.R
+++ b/R/clustering.R
@@ -9,8 +9,8 @@
 #' @param bound_variable (optional) A data frame with selected bound variable
 #' @param min_bound (optional) A minimum bound value that applies to all
 #' clusters
-#' @param scale_method One of the scaling methods {'raw', 'standardize',
-#' 'demean', 'mad', 'range_standardize', 'range_adjust'} to apply on input data.
+#' @param scale_method One of the scaling methods ('raw', 'standardize',
+#' 'demean', 'mad', 'range_standardize', 'range_adjust') to apply on input data.
 #' Default is 'standardize' (Z-score normalization).
 #' @param distance_method (optional) The distance method used to compute the
 #' distance betwen observation i and j. Defaults to "euclidean". Options are
@@ -89,7 +89,7 @@ skater <- function(k, w, df, bound_variable=data.frame(), min_bound=0,
 #' @param method {"single", "complete", "average","ward"}
 #' @param bound_variable (optional) A data frame with selected bound variabl
 #' @param min_bound (optional) A minimum bound value that applies to all clusters
-#' @param scale_method One of the scaling methods {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'} to apply on input data. Default is 'standardize' (Z-score normalization).
+#' @param scale_method One of the scaling methods ('raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust') to apply on input data. Default is 'standardize' (Z-score normalization).
 #' @param distance_method (optional) The distance method used to compute the distance betwen observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan"
 #' @param rdist (optional) The distance matrix (lower triangular matrix, column wise storage)
 #' @return A names list with names "Clusters", "Total sum of squares", "Within-cluster sum of squares", "Total within-cluster sum of squares", and "The ratio of between to total sum of squares".
@@ -125,7 +125,7 @@ schc <- function(k, w, df, method="average", bound_variable=data.frame(), min_bo
 
   scale_methods <- c('raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust')
   if (!(scale_method %in% scale_methods)) {
-    stop("The scale_method has to be one of {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'}")
+    stop("The scale_method has to be one of ('raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust')")
   }
 
   method_cands <- c("single", "complete", "average","ward")
@@ -166,7 +166,7 @@ schc <- function(k, w, df, method="average", bound_variable=data.frame(), min_bo
 #' @param method {"firstorder-singlelinkage", "fullorder-completelinkage", "fullorder-averagelinkage","fullorder-singlelinkage", "fullorder-wardlinkage"}
 #' @param bound_variable (optional) A data frame with selected bound variabl
 #' @param min_bound (optional) A minimum bound value that applies to all clusters
-#' @param scale_method (optional) One of the scaling methods {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'} to apply on input data. Default is 'standardize' (Z-score normalization).
+#' @param scale_method (optional) One of the scaling methods ('raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust') to apply on input data. Default is 'standardize' (Z-score normalization).
 #' @param distance_method (optional) The distance method used to compute the distance betwen observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan"
 #' @param random_seed (int,optional) The seed for random number generator. Defaults to 123456789.
 #' @param cpu_threads (optional) The number of cpu threads used for parallel computation
@@ -238,7 +238,7 @@ redcap <- function(k, w, df, method="fullorder-averagelinkage", bound_variable=d
 #' @param min_bound A minimum value that the sum value of bounding variable int each cluster should be greater than
 #' @param iterations (optional): The number of iterations of greedy algorithm. Defaults to 99.
 #' @param initial_regions (optional): The initial regions that the local search starts with. Default is empty. means the local search starts with a random process to "grow" clusters
-#' @param scale_method (optional) One of the scaling methods {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'} to apply on input data. Default is 'standardize' (Z-score normalization).
+#' @param scale_method (optional) One of the scaling methods ('raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust') to apply on input data. Default is 'standardize' (Z-score normalization).
 #' @param distance_method (optional) The distance method used to compute the distance betwen observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan"
 #' @param random_seed (optional) The seed for random number generator. Defaults to 123456789.
 #' @param cpu_threads (optional) The number of cpu threads used for parallel computation
@@ -313,7 +313,7 @@ maxp_greedy <- function(w, df, bound_variable, min_bound, iterations=99, initial
 #' @param cooling_rate The cooling rate of a simulated annealing algorithm. Defaults to 0.85
 #' @param iterations (optional): The number of iterations of SA algorithm. Defaults to 99.
 #' @param sa_maxit (optional): The number of iterations of simulated annealing. Defaults to 1
-#' @param scale_method (optional) One of the scaling methods {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'} to apply on input data. Default is 'standardize' (Z-score normalization).
+#' @param scale_method (optional) One of the scaling methods ('raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust') to apply on input data. Default is 'standardize' (Z-score normalization).
 #' @param distance_method (optional) The distance method used to compute the distance betwen observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan"
 #' @param random_seed (optional) The seed for random number generator. Defaults to 123456789.
 #' @param initial_regions (optional): The initial regions that the local search starts with. Default is empty. means the local search starts with a random process to "grow" clusters
@@ -389,7 +389,7 @@ maxp_sa <- function(w, df, bound_variable, min_bound, cooling_rate, sa_maxit=1,
 #' @param tabu_length (optional): The length of a tabu search heuristic of tabu algorithm. Defaults to 10.
 #' @param conv_tabu (optional): The number of non-improving moves. Defaults to 10.
 #' @param iterations (optional): The number of iterations of Tabu algorithm. Defaults to 99.
-#' @param scale_method (optional) One of the scaling methods {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'} to apply on input data. Default is 'standardize' (Z-score normalization).
+#' @param scale_method (optional) One of the scaling methods ('raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust') to apply on input data. Default is 'standardize' (Z-score normalization).
 #' @param distance_method (optional) The distance method used to compute the distance betwen observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan"
 #' @param random_seed (optional) The seed for random number generator. Defaults to 123456789.
 #' @param initial_regions (optional): The initial regions that the local search starts with. Default is empty. means the local search starts with a random process to "grow" clusters
@@ -462,7 +462,7 @@ maxp_tabu <- function(w, df, bound_variable, min_bound, tabu_length=10, conv_tab
 #' @param min_bound (optional) A minimum bound value that applies to all clusters
 #' @param inits (optional) The number of construction re-runs, which is for ARiSeL "automatic regionalization with initial seed location"
 #' @param initial_regions (optional) The initial regions that the local search starts with. Default is empty. means the local search starts with a random process to "grow" clusters
-#' @param scale_method (optional) One of the scaling methods {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'} to apply on input data. Default is 'standardize' (Z-score normalization).
+#' @param scale_method (optional) One of the scaling methods ('raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust') to apply on input data. Default is 'standardize' (Z-score normalization).
 #' @param distance_method (optional) The distance method used to compute the distance betwen observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan"
 #' @param random_seed (optional) The seed for random number generator. Defaults to 123456789.
 #' @param rdist (optional) The distance matrix (lower triangular matrix, column wise storage)
@@ -528,7 +528,7 @@ azp_greedy <- function(p, w, df, bound_variable=data.frame(), min_bound=0, inits
 #' @param min_bound (optional) A minimum bound value that applies to all clusters
 #' @param inits (optional) The number of construction re-runs, which is for ARiSeL "automatic regionalization with initial seed location"
 #' @param initial_regions (optional) The initial regions that the local search starts with. Default is empty. means the local search starts with a random process to "grow" clusters
-#' @param scale_method (optional) One of the scaling methods {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'} to apply on input data. Default is 'standardize' (Z-score normalization).
+#' @param scale_method (optional) One of the scaling methods ('raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust') to apply on input data. Default is 'standardize' (Z-score normalization).
 #' @param distance_method (optional) The distance method used to compute the distance betwen observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan"
 #' @param random_seed (optional) The seed for random number generator. Defaults to 123456789.
 #' @param rdist (optional) The distance matrix (lower triangular matrix, column wise storage)
@@ -594,7 +594,7 @@ azp_sa<- function(p, w, df, cooling_rate, sa_maxit=1, bound_variable=data.frame(
 #' @param min_bound (optional) A minimum bound value that applies to all clusters
 #' @param inits (optional) The number of construction re-runs, which is for ARiSeL "automatic regionalization with initial seed location"
 #' @param initial_regions (optional) The initial regions that the local search starts with. Default is empty. means the local search starts with a random process to "grow" clusters
-#' @param scale_method (optional) One of the scaling methods {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'} to apply on input data. Default is 'standardize' (Z-score normalization).
+#' @param scale_method (optional) One of the scaling methods ('raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust') to apply on input data. Default is 'standardize' (Z-score normalization).
 #' @param distance_method (optional) The distance method used to compute the distance betwen observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan"
 #' @param random_seed (optional) The seed for random number generator. Defaults to 123456789.
 #' @param rdist (optional) The distance matrix (lower triangular matrix, column wise storage)
diff --git a/R/lisa.R b/R/lisa.R
index b21db10b..32ff5f67 100644
--- a/R/lisa.R
+++ b/R/lisa.R
@@ -282,7 +282,7 @@ lisa_colors <- function(gda_lisa) {
 #' @param permutations (optional) The number of permutations for the LISA
 #' computation
 #' @param permutation_method (optional) The permutation method used for the
-#' LISA computation. Options are {'complete', 'lookup'}. Default is 'complete'.
+#' LISA computation. Options are ('complete', 'lookup'). Default is 'complete'.
 #' @param significance_cutoff  (optional) A cutoff value for significance
 #' p-values to filter not-significant clusters
 #' @param cpu_threads (optional) The number of cpu threads used for parallel
@@ -324,7 +324,7 @@ local_moran <- function(w, df, permutations=999, permutation_method="complete",
 #' @param permutations (optional) The number of permutations for the LISA
 #' computation
 #' @param permutation_method (optional) The permutation method used for the
-#' LISA computation. Options are {'complete', 'lookup'}. Default is 'complete'.
+#' LISA computation. Options are ('complete', 'lookup'). Default is 'complete'.
 #' @param significance_cutoff  (optional) A cutoff value for significance
 #' p-values to filter not-significant clusters
 #' @param cpu_threads (optional) The number of cpu threads used for parallel
@@ -381,7 +381,7 @@ local_bimoran <- function(w, df, permutations=999, permutation_method="complete"
 #' @param permutations (optional) The number of permutations for the LISA
 #' computation
 #' @param permutation_method (optional) The permutation method used for the LISA
-#'  computation. Options are {'complete', 'lookup'}. Default is 'complete'.
+#'  computation. Options are ('complete', 'lookup'). Default is 'complete'.
 #' @param significance_cutoff  (optional) A cutoff value for significance
 #' p-values to filter not-significant clusters
 #' @param cpu_threads (optional) The number of cpu threads used for parallel
@@ -430,7 +430,7 @@ local_moran_eb <- function(w, df, permutations=999,
 #' @param permutations (optional) The number of permutations for the LISA
 #' computation
 #' @param permutation_method (optional) The permutation method used for the
-#' LISA computation. Options are {'complete', 'lookup'}. Default is 'complete'.
+#' LISA computation. Options are ('complete', 'lookup'). Default is 'complete'.
 #' @param significance_cutoff  (optional) A cutoff value for significance
 #' p-values to filter not-significant clusters
 #' @param cpu_threads (optional) The number of cpu threads used for parallel
@@ -473,7 +473,7 @@ local_geary <- function(w, df, permutations=999, permutation_method="complete",
 #' @param permutations (optional) The number of permutations for the LISA
 #' computation
 #' @param permutation_method (optional) The permutation method used for the
-#' LISA computation. Options are {'complete', 'lookup'}. Default is 'complete'.
+#' LISA computation. Options are ('complete', 'lookup'). Default is 'complete'.
 #' @param significance_cutoff  (optional) A cutoff value for significance
 #' p-values to filter not-significant clusters
 #' @param cpu_threads (optional) The number of cpu threads used for parallel
@@ -523,7 +523,7 @@ local_multigeary <- function(w, df, permutations=999,
 #' @param permutations (optional) The number of permutations for the LISA
 #' computation
 #' @param permutation_method (optional) The permutation method used for the
-#' LISA computation. Options are {'complete', 'lookup'}. Default is 'complete'.
+#' LISA computation. Options are ('complete', 'lookup'). Default is 'complete'.
 #' @param significance_cutoff  (optional) A cutoff value for significance
 #' p-values to filter not-significant clusters
 #' @param cpu_threads (optional) The number of cpu threads used for parallel
@@ -563,7 +563,7 @@ local_g <- function(w, df, permutations=999, permutation_method="complete",
 #' @param permutations (optional) The number of permutations for the LISA
 #' computation
 #' @param permutation_method (optional) The permutation method used for the
-#' LISA computation. Options are {'complete', 'lookup'}. Default is 'complete'.
+#' LISA computation. Options are ('complete', 'lookup'). Default is 'complete'.
 #' @param significance_cutoff  (optional) A cutoff value for significance
 #' p-values to filter not-significant clusters
 #' @param cpu_threads (optional) The number of cpu threads used for parallel
@@ -605,7 +605,7 @@ local_gstar <- function(w, df, permutations=999, permutation_method="complete",
 #' @param permutations (optional) The number of permutations for the LISA
 #' computation
 #' @param permutation_method (optional) The permutation method used for the
-#' LISA computation. Options are {'complete', 'lookup'}. Default is 'complete'.
+#' LISA computation. Options are ('complete', 'lookup'). Default is 'complete'.
 #' @param significance_cutoff  (optional) A cutoff value for significance
 #' p-values to filter not-significant clusters
 #' @param cpu_threads (optional) The number of cpu threads used for parallel
@@ -659,7 +659,7 @@ local_joincount <- function(w, df, permutations=999,
 #' @param permutations (optional) The number of permutations for the LISA
 #' computation
 #' @param permutation_method (optional) The permutation method used for the
-#' LISA computation. Options are {'complete', 'lookup'}. Default is 'complete'.
+#' LISA computation. Options are ('complete', 'lookup'). Default is 'complete'.
 #' @param significance_cutoff  (optional) A cutoff value for significance
 #' p-values to filter not-significant clusters
 #' @param cpu_threads (optional) The number of cpu threads used for parallel
@@ -724,7 +724,7 @@ local_bijoincount <- function(w, df, permutations=999,
 #' @param permutations (optional) The number of permutations for the LISA
 #' computation
 #' @param permutation_method (optional) The permutation method used for the
-#' LISA computation. Options are {'complete', 'lookup'}. Default is 'complete'.
+#' LISA computation. Options are ('complete', 'lookup'). Default is 'complete'.
 #' @param significance_cutoff  (optional) A cutoff value for significance
 #' p-values to filter not-significant clusters
 #' @param cpu_threads (optional) The number of cpu threads used for parallel
@@ -798,7 +798,7 @@ local_multijoincount <- function(w, df, permutations=999,
 #' @param permutations (optional) The number of permutations for the LISA
 #' computation
 #' @param permutation_method (optional) The permutation method used for the LISA
-#'  computation. Options are {'complete', 'lookup'}. Default is 'complete'.
+#'  computation. Options are ('complete', 'lookup'). Default is 'complete'.
 #' @param significance_cutoff  (optional) A cutoff value for significance
 #' p-values to filter not-significant clusters
 #' @param cpu_threads (optional) The number of cpu threads used for parallel
@@ -851,7 +851,7 @@ local_quantilelisa <- function(w, df, k, q, permutations=999,
 #' @param permutations (optional) The number of permutations for the LISA
 #' computation
 #' @param permutation_method (optional) The permutation method used for the LISA
-#'  computation. Options are {'complete', 'lookup'}. Default is 'complete'.
+#'  computation. Options are ('complete', 'lookup'). Default is 'complete'.
 #' @param significance_cutoff  (optional) A cutoff value for significance
 #' p-values to filter not-significant clusters
 #' @param cpu_threads (optional) The number of cpu threads used for parallel
@@ -918,11 +918,11 @@ local_multiquantilelisa <- function(w, df, k, q, permutations=999,
 #' @param df A subset of sf object with selected variables.
 #' E.g. guerry[c("Crm_prs", "Crm_prp", "Litercy")]
 #' @param k a positive integer number for k-nearest neighbors searching.
-#' @param scale_method (optional) One of the scaling methods {'raw',
-#' 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'} to apply
+#' @param scale_method (optional) One of the scaling methods ('raw',
+#' 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust') to apply
 #'  on input data. Default is 'standardize' (Z-score normalization).
 #' @param distance_method (optional) The type of distance metrics used to
-#' measure the distance between input data. Options are {'euclidean', 'manhattan'}. Default is 'euclidean'.
+#' measure the distance between input data. Options are ('euclidean', 'manhattan'). Default is 'euclidean'.
 #' @param power (optional) The power (or exponent) of a number says how many
 #' times to use the number in a multiplication.
 #' @param is_inverse (optional) FALSE (default) or TRUE, apply inverse on
diff --git a/man/azp_greedy.Rd b/man/azp_greedy.Rd
index 35b4b94c..486f800d 100644
--- a/man/azp_greedy.Rd
+++ b/man/azp_greedy.Rd
@@ -33,7 +33,7 @@ azp_greedy(
 
 \item{initial_regions}{(optional) The initial regions that the local search starts with. Default is empty. means the local search starts with a random process to "grow" clusters}
 
-\item{scale_method}{(optional) One of the scaling methods {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'} to apply on input data. Default is 'standardize' (Z-score normalization).}
+\item{scale_method}{(optional) One of the scaling methods ('raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust') to apply on input data. Default is 'standardize' (Z-score normalization).}
 
 \item{distance_method}{(optional) The distance method used to compute the distance betwen observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan"}
 
diff --git a/man/azp_sa.Rd b/man/azp_sa.Rd
index e8c2db4a..43ddd691 100644
--- a/man/azp_sa.Rd
+++ b/man/azp_sa.Rd
@@ -39,7 +39,7 @@ azp_sa(
 
 \item{initial_regions}{(optional) The initial regions that the local search starts with. Default is empty. means the local search starts with a random process to "grow" clusters}
 
-\item{scale_method}{(optional) One of the scaling methods {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'} to apply on input data. Default is 'standardize' (Z-score normalization).}
+\item{scale_method}{(optional) One of the scaling methods ('raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust') to apply on input data. Default is 'standardize' (Z-score normalization).}
 
 \item{distance_method}{(optional) The distance method used to compute the distance betwen observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan"}
 
diff --git a/man/azp_tabu.Rd b/man/azp_tabu.Rd
index c6be9de1..61e470c9 100644
--- a/man/azp_tabu.Rd
+++ b/man/azp_tabu.Rd
@@ -39,7 +39,7 @@ azp_tabu(
 
 \item{initial_regions}{(optional) The initial regions that the local search starts with. Default is empty. means the local search starts with a random process to "grow" clusters}
 
-\item{scale_method}{(optional) One of the scaling methods {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'} to apply on input data. Default is 'standardize' (Z-score normalization).}
+\item{scale_method}{(optional) One of the scaling methods ('raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust') to apply on input data. Default is 'standardize' (Z-score normalization).}
 
 \item{distance_method}{(optional) The distance method used to compute the distance betwen observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan"}
 
diff --git a/man/local_bijoincount.Rd b/man/local_bijoincount.Rd
index 79bed63f..5579c40c 100644
--- a/man/local_bijoincount.Rd
+++ b/man/local_bijoincount.Rd
@@ -24,7 +24,7 @@ E.g. guerry[c("TopCrm", "InvCrm")]}
 computation}
 
 \item{permutation_method}{(optional) The permutation method used for the
-LISA computation. Options are {'complete', 'lookup'}. Default is 'complete'.}
+LISA computation. Options are ('complete', 'lookup'). Default is 'complete'.}
 
 \item{significance_cutoff}{(optional) A cutoff value for significance
 p-values to filter not-significant clusters}
diff --git a/man/local_bimoran.Rd b/man/local_bimoran.Rd
index 5bae0103..3c7c1824 100644
--- a/man/local_bimoran.Rd
+++ b/man/local_bimoran.Rd
@@ -23,7 +23,7 @@ local_bimoran(
 computation}
 
 \item{permutation_method}{(optional) The permutation method used for the
-LISA computation. Options are {'complete', 'lookup'}. Default is 'complete'.}
+LISA computation. Options are ('complete', 'lookup'). Default is 'complete'.}
 
 \item{significance_cutoff}{(optional) A cutoff value for significance
 p-values to filter not-significant clusters}
diff --git a/man/local_g.Rd b/man/local_g.Rd
index 92705666..3a4b5e64 100644
--- a/man/local_g.Rd
+++ b/man/local_g.Rd
@@ -23,7 +23,7 @@ local_g(
 computation}
 
 \item{permutation_method}{(optional) The permutation method used for the
-LISA computation. Options are {'complete', 'lookup'}. Default is 'complete'.}
+LISA computation. Options are ('complete', 'lookup'). Default is 'complete'.}
 
 \item{significance_cutoff}{(optional) A cutoff value for significance
 p-values to filter not-significant clusters}
diff --git a/man/local_geary.Rd b/man/local_geary.Rd
index 58fd82dc..a42b78e6 100644
--- a/man/local_geary.Rd
+++ b/man/local_geary.Rd
@@ -23,7 +23,7 @@ local_geary(
 computation}
 
 \item{permutation_method}{(optional) The permutation method used for the
-LISA computation. Options are {'complete', 'lookup'}. Default is 'complete'.}
+LISA computation. Options are ('complete', 'lookup'). Default is 'complete'.}
 
 \item{significance_cutoff}{(optional) A cutoff value for significance
 p-values to filter not-significant clusters}
diff --git a/man/local_gstar.Rd b/man/local_gstar.Rd
index f9e4587a..95f05b3c 100644
--- a/man/local_gstar.Rd
+++ b/man/local_gstar.Rd
@@ -23,7 +23,7 @@ local_gstar(
 computation}
 
 \item{permutation_method}{(optional) The permutation method used for the
-LISA computation. Options are {'complete', 'lookup'}. Default is 'complete'.}
+LISA computation. Options are ('complete', 'lookup'). Default is 'complete'.}
 
 \item{significance_cutoff}{(optional) A cutoff value for significance
 p-values to filter not-significant clusters}
diff --git a/man/local_joincount.Rd b/man/local_joincount.Rd
index f55bba26..83a024cc 100644
--- a/man/local_joincount.Rd
+++ b/man/local_joincount.Rd
@@ -23,7 +23,7 @@ local_joincount(
 computation}
 
 \item{permutation_method}{(optional) The permutation method used for the
-LISA computation. Options are {'complete', 'lookup'}. Default is 'complete'.}
+LISA computation. Options are ('complete', 'lookup'). Default is 'complete'.}
 
 \item{significance_cutoff}{(optional) A cutoff value for significance
 p-values to filter not-significant clusters}
diff --git a/man/local_moran.Rd b/man/local_moran.Rd
index cbb1e1ae..6914f8cb 100644
--- a/man/local_moran.Rd
+++ b/man/local_moran.Rd
@@ -23,7 +23,7 @@ local_moran(
 computation}
 
 \item{permutation_method}{(optional) The permutation method used for the
-LISA computation. Options are {'complete', 'lookup'}. Default is 'complete'.}
+LISA computation. Options are ('complete', 'lookup'). Default is 'complete'.}
 
 \item{significance_cutoff}{(optional) A cutoff value for significance
 p-values to filter not-significant clusters}
diff --git a/man/local_moran_eb.Rd b/man/local_moran_eb.Rd
index 95d4c91b..1a9d806b 100644
--- a/man/local_moran_eb.Rd
+++ b/man/local_moran_eb.Rd
@@ -24,7 +24,7 @@ is "base" variable. E.g. guerry[c("hr60", "po60")]}
 computation}
 
 \item{permutation_method}{(optional) The permutation method used for the LISA
-computation. Options are {'complete', 'lookup'}. Default is 'complete'.}
+computation. Options are ('complete', 'lookup'). Default is 'complete'.}
 
 \item{significance_cutoff}{(optional) A cutoff value for significance
 p-values to filter not-significant clusters}
diff --git a/man/local_multigeary.Rd b/man/local_multigeary.Rd
index 335d6124..c828b533 100644
--- a/man/local_multigeary.Rd
+++ b/man/local_multigeary.Rd
@@ -23,7 +23,7 @@ local_multigeary(
 computation}
 
 \item{permutation_method}{(optional) The permutation method used for the
-LISA computation. Options are {'complete', 'lookup'}. Default is 'complete'.}
+LISA computation. Options are ('complete', 'lookup'). Default is 'complete'.}
 
 \item{significance_cutoff}{(optional) A cutoff value for significance
 p-values to filter not-significant clusters}
diff --git a/man/local_multijoincount.Rd b/man/local_multijoincount.Rd
index 92f7140d..0b78535a 100644
--- a/man/local_multijoincount.Rd
+++ b/man/local_multijoincount.Rd
@@ -24,7 +24,7 @@ E.g. guerry[c("TopCrm", "TopWealth", "TopLit")]}
 computation}
 
 \item{permutation_method}{(optional) The permutation method used for the
-LISA computation. Options are {'complete', 'lookup'}. Default is 'complete'.}
+LISA computation. Options are ('complete', 'lookup'). Default is 'complete'.}
 
 \item{significance_cutoff}{(optional) A cutoff value for significance
 p-values to filter not-significant clusters}
diff --git a/man/local_multiquantilelisa.Rd b/man/local_multiquantilelisa.Rd
index 69f7712d..1579bfbb 100644
--- a/man/local_multiquantilelisa.Rd
+++ b/man/local_multiquantilelisa.Rd
@@ -32,7 +32,7 @@ variable used in local join count statistics. Value stars from 1.}
 computation}
 
 \item{permutation_method}{(optional) The permutation method used for the LISA
-computation. Options are {'complete', 'lookup'}. Default is 'complete'.}
+computation. Options are ('complete', 'lookup'). Default is 'complete'.}
 
 \item{significance_cutoff}{(optional) A cutoff value for significance
 p-values to filter not-significant clusters}
diff --git a/man/local_quantilelisa.Rd b/man/local_quantilelisa.Rd
index eba096a8..55c53e7a 100644
--- a/man/local_quantilelisa.Rd
+++ b/man/local_quantilelisa.Rd
@@ -30,7 +30,7 @@ count statistics. Value stars from 1.}
 computation}
 
 \item{permutation_method}{(optional) The permutation method used for the LISA
-computation. Options are {'complete', 'lookup'}. Default is 'complete'.}
+computation. Options are ('complete', 'lookup'). Default is 'complete'.}
 
 \item{significance_cutoff}{(optional) A cutoff value for significance
 p-values to filter not-significant clusters}
diff --git a/man/maxp_greedy.Rd b/man/maxp_greedy.Rd
index 98a83e9c..9ccdb829 100644
--- a/man/maxp_greedy.Rd
+++ b/man/maxp_greedy.Rd
@@ -31,7 +31,7 @@ maxp_greedy(
 
 \item{initial_regions}{(optional): The initial regions that the local search starts with. Default is empty. means the local search starts with a random process to "grow" clusters}
 
-\item{scale_method}{(optional) One of the scaling methods {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'} to apply on input data. Default is 'standardize' (Z-score normalization).}
+\item{scale_method}{(optional) One of the scaling methods ('raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust') to apply on input data. Default is 'standardize' (Z-score normalization).}
 
 \item{distance_method}{(optional) The distance method used to compute the distance betwen observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan"}
 
diff --git a/man/maxp_sa.Rd b/man/maxp_sa.Rd
index a37bbcc0..38c97f58 100644
--- a/man/maxp_sa.Rd
+++ b/man/maxp_sa.Rd
@@ -37,7 +37,7 @@ maxp_sa(
 
 \item{initial_regions}{(optional): The initial regions that the local search starts with. Default is empty. means the local search starts with a random process to "grow" clusters}
 
-\item{scale_method}{(optional) One of the scaling methods {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'} to apply on input data. Default is 'standardize' (Z-score normalization).}
+\item{scale_method}{(optional) One of the scaling methods ('raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust') to apply on input data. Default is 'standardize' (Z-score normalization).}
 
 \item{distance_method}{(optional) The distance method used to compute the distance betwen observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan"}
 
diff --git a/man/maxp_tabu.Rd b/man/maxp_tabu.Rd
index 5c4533c3..d1a94367 100644
--- a/man/maxp_tabu.Rd
+++ b/man/maxp_tabu.Rd
@@ -37,7 +37,7 @@ maxp_tabu(
 
 \item{initial_regions}{(optional): The initial regions that the local search starts with. Default is empty. means the local search starts with a random process to "grow" clusters}
 
-\item{scale_method}{(optional) One of the scaling methods {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'} to apply on input data. Default is 'standardize' (Z-score normalization).}
+\item{scale_method}{(optional) One of the scaling methods ('raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust') to apply on input data. Default is 'standardize' (Z-score normalization).}
 
 \item{distance_method}{(optional) The distance method used to compute the distance betwen observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan"}
 
diff --git a/man/neighbor_match_test.Rd b/man/neighbor_match_test.Rd
index a7081650..ed147acc 100644
--- a/man/neighbor_match_test.Rd
+++ b/man/neighbor_match_test.Rd
@@ -21,12 +21,12 @@ E.g. guerry[c("Crm_prs", "Crm_prp", "Litercy")]}
 
 \item{k}{a positive integer number for k-nearest neighbors searching.}
 
-\item{scale_method}{(optional) One of the scaling methods {'raw',
-'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'} to apply
+\item{scale_method}{(optional) One of the scaling methods ('raw',
+'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust') to apply
  on input data. Default is 'standardize' (Z-score normalization).}
 
 \item{distance_method}{(optional) The type of distance metrics used to
-measure the distance between input data. Options are {'euclidean', 'manhattan'}. Default is 'euclidean'.}
+measure the distance between input data. Options are ('euclidean', 'manhattan'). Default is 'euclidean'.}
 
 \item{power}{(optional) The power (or exponent) of a number says how many
 times to use the number in a multiplication.}
diff --git a/man/redcap.Rd b/man/redcap.Rd
index 487f331d..473aa4b6 100644
--- a/man/redcap.Rd
+++ b/man/redcap.Rd
@@ -31,7 +31,7 @@ redcap(
 
 \item{min_bound}{(optional) A minimum bound value that applies to all clusters}
 
-\item{scale_method}{(optional) One of the scaling methods {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'} to apply on input data. Default is 'standardize' (Z-score normalization).}
+\item{scale_method}{(optional) One of the scaling methods ('raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust') to apply on input data. Default is 'standardize' (Z-score normalization).}
 
 \item{distance_method}{(optional) The distance method used to compute the distance betwen observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan"}
 
diff --git a/man/schc.Rd b/man/schc.Rd
index f1aa4684..f728d88a 100644
--- a/man/schc.Rd
+++ b/man/schc.Rd
@@ -29,7 +29,7 @@ schc(
 
 \item{min_bound}{(optional) A minimum bound value that applies to all clusters}
 
-\item{scale_method}{One of the scaling methods {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'} to apply on input data. Default is 'standardize' (Z-score normalization).}
+\item{scale_method}{One of the scaling methods ('raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust') to apply on input data. Default is 'standardize' (Z-score normalization).}
 
 \item{distance_method}{(optional) The distance method used to compute the distance betwen observation i and j. Defaults to "euclidean". Options are "euclidean" and "manhattan"}
 
diff --git a/man/skater.Rd b/man/skater.Rd
index 4674e456..97a94d63 100644
--- a/man/skater.Rd
+++ b/man/skater.Rd
@@ -30,8 +30,8 @@ E.g. guerry[c("Crm_prs", "Crm_prp", "Litercy")]}
 \item{min_bound}{(optional) A minimum bound value that applies to all
 clusters}
 
-\item{scale_method}{One of the scaling methods {'raw', 'standardize',
-'demean', 'mad', 'range_standardize', 'range_adjust'} to apply on input data.
+\item{scale_method}{One of the scaling methods ('raw', 'standardize',
+'demean', 'mad', 'range_standardize', 'range_adjust') to apply on input data.
 Default is 'standardize' (Z-score normalization).}
 
 \item{distance_method}{(optional) The distance method used to compute the
diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp
index 8a2d257b..acaebdaf 100644
--- a/src/RcppExports.cpp
+++ b/src/RcppExports.cpp
@@ -5,6 +5,11 @@
 
 using namespace Rcpp;
 
+#ifdef RCPP_USE_GLOBAL_ROSTREAM
+Rcpp::Rostream<true>&  Rcpp::Rcout = Rcpp::Rcpp_cout_get();
+Rcpp::Rostream<false>& Rcpp::Rcerr = Rcpp::Rcpp_cerr_get();
+#endif
+
 // p_skater
 Rcpp::List p_skater(int k, SEXP xp_w, Rcpp::List& data, int n_vars, std::string scale_method, std::string distance_method, NumericVector& bound_vals, double min_bound, int seed, int cpu_threads, NumericVector& rdist);
 RcppExport SEXP _rgeoda_p_skater(SEXP kSEXP, SEXP xp_wSEXP, SEXP dataSEXP, SEXP n_varsSEXP, SEXP scale_methodSEXP, SEXP distance_methodSEXP, SEXP bound_valsSEXP, SEXP min_boundSEXP, SEXP seedSEXP, SEXP cpu_threadsSEXP, SEXP rdistSEXP) {