Merge pull request #16 from AAGI-AUS/feature/update-readme

Feature/update readme

Author: wvjgsuhp

DESCRIPTION

@@ -18,8 +18,8 @@ Description: The InPlotSampling package provides a way for researchers
     and Kravchuk (2021) <https://doi.org/10.1007/s13253-021-00439-1> and
     enables easy use of the methods.
 License: MIT + file LICENSE
-URL: https://biometryhub.github.io/InPlotSampling/
-BugReports: https://github.com/biometryhub/InPlotSampling/issues
+URL: https://aagi-aus.github.io/InPlotSampling/
+BugReports: https://github.com/AAGI-AUS/InPlotSampling/issues
 Depends:
     R (>= 3.5.0)
 Imports: 

README.Rmd

@@ -28,8 +28,8 @@ min.r <- substr(description[, "Depends"], 7, 11)
 
 [![Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.](http://www.repostatus.org/badges/latest/wip.svg)](http://www.repostatus.org/#wip)
 [![Codecov test coverage](https://codecov.io/gh/biometryhub/RankedSetSampling/branch/main/graph/badge.svg)](https://codecov.io/gh/biometryhub/RankedSetSampling?branch=main)
-[![R build status](https://github.com/biometryhub/InPlotSampling/workflows/R-CMD-check/badge.svg)](https://github.com/biometryhub/InPlotSampling/actions)
-![pkgdown](https://github.com/biometryhub/InPlotSampling/workflows/pkgdown/badge.svg)
+[![R build status](https://github.com/AAGI-AUS/InPlotSampling/workflows/R-CMD-check/badge.svg)](https://github.com/AAGI-AUS/InPlotSampling/actions)
+![pkgdown](https://github.com/AAGI-AUS/InPlotSampling/workflows/pkgdown/badge.svg)
 <br>
 [![minimal R version](https://img.shields.io/badge/R%3E%3D-`r min.r`-6666ff.svg)](https://cran.r-project.org/)
 [![packageversion](https://img.shields.io/badge/Package%20version-`r gsub('-', '--', version)`-orange.svg?style=flat-square)](/commits/main)
@@ -38,15 +38,22 @@ min.r <- substr(description[, "Depends"], 7, 11)
 
 <!-- badges: end -->
 
-The InPlotSampling package provides a way for researchers to easily implement Ranked Set Sampling in practice.
+The InPlotSampling package provides a way for researchers to easily 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)
@@ -64,101 +71,101 @@ Sampling is made following the diagram below.
 
 ![JPS sampling diagram](man/figures/jps-diagram.drawio.svg)
 
-### RSS Sampling
+### RSS
 
 Sampling is made following the diagram below.
 
-![RSS sampling diagram](man/figures/rss-diagram.drawio.svg)
+![RSS diagram](man/figures/rss-diagram.drawio.svg)
 
 ## Installation
 
 Use the following code to install this package:
 
-```{r eval=F}
+```{r, eval=F}
 if (!require("remotes")) install.packages("remotes")
-remotes::install_github("biometryhub/InPlotSampling", upgrade = FALSE)
+remotes::install_github("AAGI-AUS/InPlotSampling", upgrade = FALSE)
 ```
 
 ## Examples
 
 ### JPS Sample and Estimator
 
 <details>
-  <summary>JPS sample and estimator</summary>
-
-  ``` r
-  set.seed(112)
-  population_size <- 600
-  # the number of samples to be ranked in each set
-  H <- 3
-
-  with_replacement <- FALSE
-  sigma <- 4
-  mu <- 10
-  n_rankers <- 3
-  # sample size
-  n <- 30
-
-  rhos <- rep(0.75, n_rankers)
-  taus <- sigma * sqrt(1 / rhos^2 - 1)
-  population <- qnorm((1:population_size) / (population_size + 1), mu, sigma)
-
-  data <- InPlotSampling::jps_sample(population, n, H, taus, n_rankers, with_replacement)
-  data <- data[order(data[, 2]), ]
-
-  InPlotSampling::rss_jps_estimate(
-    data,
-    set_size = H,
-    method = "JPS",
-    confidence = 0.80,
-    replace = with_replacement,
-    model_based = FALSE,
-    pop_size = population_size
-  )
-  #>          Estimator Estimate Standard Error 80% Confidence intervals
-  #> 1       UnWeighted    9.570          0.526               8.88,10.26
-  #> 2      Sd.Weighted    9.595          0.569             8.849,10.341
-  #> 3 Aggregate Weight    9.542          0.500             8.887,10.198
-  #> 4     JPS Estimate    9.502          0.650             8.651,10.354
-  #> 5     SRS estimate    9.793          0.783             8.766,10.821
-  #> 6          Minimum    9.542          0.500             8.887,10.198
-  ```
+<summary>JPS sample and estimator</summary>
+
+``` r
+set.seed(112)
+population_size <- 600
+# the number of samples to be ranked in each set
+H <- 3
+
+with_replacement <- FALSE
+sigma <- 4
+mu <- 10
+n_rankers <- 3
+# sample size
+n <- 30
+
+rhos <- rep(0.75, n_rankers)
+taus <- sigma * sqrt(1 / rhos^2 - 1)
+population <- qnorm((1:population_size) / (population_size + 1), mu, sigma)
+
+data <- InPlotSampling::jps_sample(population, n, H, taus, n_rankers, with_replacement)
+data <- data[order(data[, 2]), ]
+
+InPlotSampling::rss_jps_estimate(
+  data,
+  set_size = H,
+  method = "JPS",
+  confidence = 0.80,
+  replace = with_replacement,
+  model_based = FALSE,
+  pop_size = population_size
+)
+#>          Estimator Estimate Standard Error 80% Confidence intervals
+#> 1       UnWeighted    9.570          0.526               8.88,10.26
+#> 2      Sd.Weighted    9.595          0.569             8.849,10.341
+#> 3 Aggregate Weight    9.542          0.500             8.887,10.198
+#> 4     JPS Estimate    9.502          0.650             8.651,10.354
+#> 5     SRS estimate    9.793          0.783             8.766,10.821
+#> 6          Minimum    9.542          0.500             8.887,10.198
+```
 </details>
 
 ### SBS PPS Sample and Estimator
 
 <details>
-  <summary>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$sample
-  sample_id <- df_sample[, 1]
-  y_sample <- y[sample_id]
-
-  sbs_pps_estimates <- sbs_pps_estimate(
-    population, sample_sizes, y_sample, df_sample,
-    n_bootstrap = 100, alpha = 0.05
-  )
-  print(sbs_pps_estimates)
-  #>   n1 n2 Estimate  St.error 95% Confidence intervals
-  #> 1  5  5    2.849 0.1760682              2.451,3.247
-  ```
+<summary>SBS PPS sample and estimator</summary>
+
+``` r
+set.seed(112)
+
+# SBS sample size, PPS sample size
+sample_sizes <- c(5, 5)
+
+n_population <- 233
+k <- 0:(n_population - 1)
+x1 <- sample(1:13, n_population, replace = TRUE) / 13
+x2 <- sample(1:8, n_population, replace = TRUE) / 8
+y <- (x1 + x2) * runif(n = n_population, min = 1, max = 2) + 1
+measured_sizes <- y * runif(n = n_population, min = 0, max = 4)
+
+population <- matrix(cbind(k, x1, x2, measured_sizes), ncol = 4)
+sample_result <- sbs_pps_sample(population, sample_sizes)
+
+# estimate the population mean and construct a confidence interval
+df_sample <- sample_result$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
+```
 </details>
 
 # Citing this package