remove spatial hierarchical heterogeneity test

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: spEcula
 Title: Spatial Prediction Methods In R
-Version: 0.1.2
+Version: 0.1.2.9000
 Authors@R: c(
     person("Wenbo", "Lv", , "[email protected]", role = c("aut", "cre"),
            comment = c(ORCID = "0009-0002-6003-3800")),
@@ -15,7 +15,6 @@ Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.3.1
 Imports: 
     stats,
-    utils,
     parallel,
     tibble,
     dplyr (>= 1.1.0),
@@ -24,20 +23,15 @@ Imports:
     ggplot2,
     magrittr,
     sf,
-    ggrepel,
-    pander,
-    knitr
+    ggrepel
 Depends: 
     R (>= 4.1.0)
 LazyData: true
 Suggests: 
     terra,
-    ggspatial,
     tidyverse,
     tidyterra,
-    tmap,
     rmarkdown,
-    fs,
     skimr,
     car,
     ggpubr,
@@ -49,5 +43,6 @@ Suggests:
     writexl,
     DescTools,
     PerformanceAnalytics,
-    tictoc
+    tictoc,
+    knitr
 VignetteBuilder: knitr

NAMESPACE

@@ -1,33 +1,12 @@
 # Generated by roxygen2: do not edit by hand
 
-S3method(print,ecological_detector)
-S3method(print,factor_detector)
-S3method(print,interaction_detector)
-S3method(print,risk_detector)
 export("%>%")
-export(ecological_detector)
-export(factor_detector)
 export(gos)
 export(gos_bestkappa)
-export(interaction_detector)
 export(inverse_bcPower)
-export(risk_detector)
 export(sandwich)
-export(ssh.test)
-importFrom(dplyr,all_of)
-importFrom(dplyr,arrange)
-importFrom(dplyr,bind_cols)
-importFrom(dplyr,count)
-importFrom(dplyr,desc)
-importFrom(dplyr,everything)
-importFrom(dplyr,filter)
-importFrom(dplyr,group_by)
-importFrom(dplyr,mutate)
-importFrom(dplyr,n)
-importFrom(dplyr,pull)
 importFrom(dplyr,select)
 importFrom(dplyr,summarise)
-importFrom(dplyr,ungroup)
 importFrom(ggplot2,aes)
 importFrom(ggplot2,geom_line)
 importFrom(ggplot2,geom_point)
@@ -36,23 +15,13 @@ importFrom(ggplot2,scale_x_continuous)
 importFrom(ggplot2,scale_y_continuous)
 importFrom(ggplot2,theme_bw)
 importFrom(ggrepel,geom_label_repel)
-importFrom(knitr,kable)
 importFrom(magrittr,"%>%")
-importFrom(pander,pander)
 importFrom(parallel,clusterExport)
 importFrom(parallel,makeCluster)
 importFrom(parallel,parLapply)
 importFrom(parallel,stopCluster)
-importFrom(purrr,map2_dfr)
 importFrom(purrr,map_dfr)
 importFrom(stats,as.formula)
-importFrom(stats,pf)
 importFrom(stats,quantile)
 importFrom(stats,sd)
-importFrom(stats,t.test)
-importFrom(stats,var)
 importFrom(tibble,as_tibble)
-importFrom(tibble,tibble)
-importFrom(tidyr,crossing)
-importFrom(tidyr,pivot_wider)
-importFrom(utils,combn)

R/data.R

@@ -20,12 +20,3 @@
 #' @docType data
 #' @keywords dataset trace elements
 "grid"
-
-#' @title NTDs data
-#' @description
-#' See Geodetecor Model Vignette
-#'
-#' @name NTDs
-#' @format \code{NTDs}: A tibble with 185 rows and 5 variables.
-#' @docType data
-"NTDs"

R/globals.R

@@ -1,8 +1,6 @@
 # see https://forum.posit.co/t/how-to-solve-no-visible-binding-for-global-variable-note/28887
 # or https://forum.posit.co/t/when-programming-with-dplyr-what-is-the-correct-way-to-avoid-undefined-global-variables/55946
 
-utils::globalVariables(c(".", "Ecological", "Interaction", "Interactive variable",
-                         "Nh", "Nr", "Nrh", "Q-statistic", "Risk", "geometry",
-                         "rmse", "s_yr_bar", "sampling_id", "sh2", "v_yh_bar",
-                         "v_yr_bar", "variable", "variable1", "variable2", "yh_bar",
-                         "yr_bar", "zone1", "zone2"))
+utils::globalVariables(c(".", "Nh", "Nr", "Nrh", "geometry", "rmse", "s_yr_bar",
+                         "sampling_id", "sh2", "v_yh_bar", "v_yr_bar", "yh_bar",
+                         "yr_bar"))