modified some text

R/PermuTest.R

@@ -45,7 +45,7 @@ PermuTest <- R6Class(
         #' @description Plot histogram(s) of the permutation distribution. Note that it works only if the test's type is `"permu"`.
         #' 
         #' @template plot_params
-        #' @param ... extra parameters passed to `graphics::hist` or `ggplot2::stat_bin`.
+        #' @param ... extra parameters passed to `graphics::hist()` or `ggplot2::stat_bin()`.
         #' 
         #' @return The object itself (invisibly).
         plot = function(style = c("graphics", "ggplot2"), ...) {

R/pmt.R

@@ -1,13 +1,13 @@
 #' @title Syntactic Sugar for Object Construction
 #' 
-#' @description construct a test object in a unified way.
+#' @description construct test objects in a unified way.
 #' 
 #' @name pmt
 
 
 #' @rdname pmt
 #' 
-#' @param key a character string corresponding to the desired test. Check `pmts` for valid keys.
+#' @param key a character string corresponding to the desired test. Check `pmts()` for valid keys.
 #' @param ... extra parameters passed to the constructor.
 #' 
 #' @export
@@ -18,7 +18,7 @@ pmt <- function(key, ...) {
         stop(
             "The key '", key,
             "' is not valid. ",
-            "Check 'pmts' for valid keys."
+            "Check `pmts()` for valid keys."
         )
     }
 }

README.Rmd

@@ -29,6 +29,8 @@ library(LearnNonparam)
 
 This package implements some of the non-parametric tests in chapters 1-5 of [@Higgins2003](#references).
 
+It depends on [R6](https://CRAN.R-project.org/package=R6) for object oriented design and [Rcpp](https://CRAN.R-project.org/package=Rcpp) for integration of R and C++.
+
 Examples in the book can be found [here](https://qddyy.github.io/LearnNonparam/articles/examples). 
 
 ## Installation
@@ -40,12 +42,13 @@ pak::pkg_install("qddyy/LearnNonparam")
 
 ## Usage
 
-- Create a test object (for example, a `Wilcoxon` object)
+- Construct a test object
+    - from some R6 class directly
     ```{r, eval = FALSE}
     t <- Wilcoxon$new(alternative = "two_sided", type = "permu", n_permu = 1e6)
     ```
 
-    or you can use `pmt` (**p**er**m**utation **t**est) function (*Recommended*):
+    - using `pmt` (**p**er**m**utation **t**est) function (*recommended*)
     ```{r, create}
     t <- pmt("twosample.wilcoxon", alternative = "two_sided", type = "permu", n_permu = 1e6)
     ```
@@ -57,21 +60,22 @@ pak::pkg_install("qddyy/LearnNonparam")
 
 - Check the results
     ```{r, results}
+    t$statistic
     t$p_value
 
     t$print(digits = 2)
 
     t$plot(style = "ggplot2", binwidth = 1)
     ```
 
-- Modify some attributes and see how the results change
+- Modify some active bindings and see how the results change
     ```{r, modify}
     t$type <- "asymp"
 
     t$p_value
     ```
 
-implemented:
+Tests implemented in this package:
 
 ```{r}
 pmts()

README.md

@@ -16,6 +16,10 @@ check](https://github.com/qddyy/LearnNonparam/workflows/R-CMD-check/badge.svg)](
 This package implements some of the non-parametric tests in chapters 1-5
 of [Higgins (2003)](#references).
 
+It depends on [R6](https://CRAN.R-project.org/package=R6) for object
+oriented design and [Rcpp](https://CRAN.R-project.org/package=Rcpp) for
+integration of R and C++.
+
 Examples in the book can be found
 [here](https://qddyy.github.io/LearnNonparam/articles/examples).
 
@@ -28,14 +32,15 @@ pak::pkg_install("qddyy/LearnNonparam")
 
 ## Usage
 
-- Create a test object (for example, a `Wilcoxon` object)
+- Construct a test object
+
+  - from some R6 class directly
 
   ``` r
   t <- Wilcoxon$new(alternative = "two_sided", type = "permu", n_permu = 1e6)
   ```
 
-  or you can use `pmt` (**p**er**m**utation **t**est) function
-  (*Recommended*):
+  - using `pmt` (**p**er**m**utation **t**est) function (*recommended*)
 
   ``` r
   t <- pmt("twosample.wilcoxon", alternative = "two_sided", type = "permu", n_permu = 1e6)
@@ -50,35 +55,37 @@ pak::pkg_install("qddyy/LearnNonparam")
 - Check the results
 
   ``` r
+  t$statistic
+  #> [1] 526
   t$p_value
-  #> [1] 0.000103
+  #> [1] 0.000611
 
   t$print(digits = 2)
   #> 
   #>       Two Sample Wilcoxon Test 
   #> 
   #> scoring: rank    type: permu(1e+06)    method: default
-  #> statistic = 542, p-value = 1e-04
+  #> statistic = 526, p-value = 0.00061
   #> alternative hypothesis: true location shift is not equal to 0
-  #> estimate: 1.1
-  #> 95% confidence interval: (0.56, 1.7)
+  #> estimate: 1.2
+  #> 95% confidence interval: (0.43, 1.9)
 
   t$plot(style = "ggplot2", binwidth = 1)
   #> Loading required namespace: ggplot2
   ```
 
   <img src="man/figures/README-results-1.svg" width="100%" />
 
-- Modify some attributes and see how the results change
+- Modify some active bindings and see how the results change
 
   ``` r
   t$type <- "asymp"
 
   t$p_value
-  #> [1] 0.0003749904
+  #> [1] 0.001782376
   ```
 
-implemented:
+Tests implemented in this package:
 
 ``` r
 pmts()