build readme

Author: wvjgsuhp

README.md

@@ -22,15 +22,21 @@ version](https://img.shields.io/badge/R%3E%3D-3.5.0-6666ff.svg)](https://cran.r-
 <!-- badges: end -->
 
 The InPlotSampling package provides a way for researchers to easily
-implement Ranked Set Sampling in practice.
+implement these sampling methods in practice.
+
+- Judgment post-stratified (JPS) sampling
+- Ranked set sampling (RSS)
+- Porbability-proportional to size (PPS) sampling
+- Spatially balanced sampling (SBS)
+- Two-stage cluster sampling
 
 ## Table of Contents
 
 <!-- vim-markdown-toc GFM -->
 
 - [Sampling Methods](#sampling-methods)
   - [JPS Sampling](#jps-sampling)
-  - [RSS Sampling](#rss-sampling)
+  - [RSS](#rss-sampling)
 - [Installation](#installation)
 - [Examples](#examples)
   - [JPS Sample and Estimator](#jps-sample-and-estimator)
@@ -52,14 +58,13 @@ alt="JPS sampling diagram" />
 <figcaption aria-hidden="true">JPS sampling diagram</figcaption>
 </figure>
 
-### RSS Sampling
+### RSS
 
 Sampling is made following the diagram below.
 
 <figure>
-<img src="man/figures/rss-diagram.drawio.svg"
-alt="RSS sampling diagram" />
-<figcaption aria-hidden="true">RSS sampling diagram</figcaption>
+<img src="man/figures/rss-diagram.drawio.svg" alt="RSS diagram" />
+<figcaption aria-hidden="true">RSS diagram</figcaption>
 </figure>
 
 ## Installation
@@ -128,33 +133,33 @@ SBS PPS sample and estimator
 </summary>
 
 ``` r
-  set.seed(112)
-
-  # SBS sample size, PPS sample size
-  sample_sizes <- c(5, 5)
-
-  n_population <- 233
-  k <- 0:(n_population - 1)
-  x1 <- sample(1:13, n_population, replace = TRUE) / 13
-  x2 <- sample(1:8, n_population, replace = TRUE) / 8
-  y <- (x1 + x2) * runif(n = n_population, min = 1, max = 2) + 1
-  measured_sizes <- y * runif(n = n_population, min = 0, max = 4)
-
-  population <- matrix(cbind(k, x1, x2, measured_sizes), ncol = 4)
-  sample_result <- sbs_pps_sample(population, sample_sizes)
-
-  # estimate the population mean and construct a confidence interval
-  df_sample <- sample_result$sample
-  sample_id <- df_sample[, 1]
-  y_sample <- y[sample_id]
-
-  sbs_pps_estimates <- sbs_pps_estimate(
-    population, sample_sizes, y_sample, df_sample,
-    n_bootstrap = 100, alpha = 0.05
-  )
-  print(sbs_pps_estimates)
-  #>   n1 n2 Estimate  St.error 95% Confidence intervals
-  #> 1  5  5    2.849 0.1760682              2.451,3.247
+set.seed(112)
+
+# SBS sample size, PPS sample size
+sample_sizes <- c(5, 5)
+
+n_population <- 233
+k <- 0:(n_population - 1)
+x1 <- sample(1:13, n_population, replace = TRUE) / 13
+x2 <- sample(1:8, n_population, replace = TRUE) / 8
+y <- (x1 + x2) * runif(n = n_population, min = 1, max = 2) + 1
+measured_sizes <- y * runif(n = n_population, min = 0, max = 4)
+
+population <- matrix(cbind(k, x1, x2, measured_sizes), ncol = 4)
+sample_result <- sbs_pps_sample(population, sample_sizes)
+
+# estimate the population mean and construct a confidence interval
+df_sample <- sample_result$sample
+sample_id <- df_sample[, 1]
+y_sample <- y[sample_id]
+
+sbs_pps_estimates <- sbs_pps_estimate(
+  population, sample_sizes, y_sample, df_sample,
+  n_bootstrap = 100, alpha = 0.05
+)
+print(sbs_pps_estimates)
+#>   n1 n2 Estimate  St.error 95% Confidence intervals
+#> 1  5  5    2.849 0.1760682              2.451,3.247
 ```
 
 </details>