Update README

Author: paulgovan

.Rproj.user/CB2BA49E/pcs/files-pane.pper

@@ -5,5 +5,5 @@
             "ascending": true
         }
     ],
-    "path": "~/Documents/ReliaGrowR"
+    "path": "~/Documents/ReliaGrowR/R"
 }

R/rga.R

@@ -156,6 +156,9 @@
 #' @importFrom stats lm predict AIC BIC logLik cor residuals
 #' @importFrom segmented segmented slope intercept seg.control
 #' @export
+
+
+
 rga <- function(times, failures, model_type = "Crow-AMSAA", breaks = NULL, conf_level = 0.95) {
   if (is.data.frame(times)) {
     if (!all(c("times", "failures") %in% names(times))) {
@@ -224,7 +227,7 @@ rga <- function(times, failures, model_type = "Crow-AMSAA", breaks = NULL, conf_
   log_cum_failures <- log(cum_failures)
 
   # Check for perfect collinearity
-  cor_val <- suppressWarnings(cor(log_times, log_cum_failures))
+  cor_val <- suppressWarnings(stats::cor(log_times, log_cum_failures))
   if (is.na(cor_val) || abs(cor_val - 1) < .Machine$double.eps^0.5 ||
     abs(cor_val + 1) < .Machine$double.eps^0.5) {
     stop("Perfect collinearity detected between predictor ('log_times') and response ('log_cum_failures'). Regression cannot be performed.")

R/srr-stats-standards.R

@@ -14,8 +14,10 @@
 #' @srrstatsNA {G2.9} No information is lost in type conversions.
 #' @srrstatsNA {G2.12} list columns in `data.frame` objects are not used.
 #' @srrstatsNA {G3.0} There are no comparisons made between floating point numbers.
-#' @srrstatsNA {G3.1} There are no covariance calculations.
-#' @srrstatsNA {G3.1a} There are no covariance calculations.
+#' @srrstatsNA {G3.1} Currently, there are no covariance calculations since all models
+#' are univariate. However, this may change in future versions.
+#' @srrstatsNA {G3.1a} Currently, there are no covariance calculations since all models
+#' are univariate. However, this may change in future versions.
 #' @srrstatsNA {G4.0} There are no local file outputs at this time.
 #' @srrstatsNA {G5.3} Missing values return an error.
 #' @srrstatsNA {G5.4a} There are new implementations of existing methods but no new methods.

README.Rmd

@@ -34,6 +34,9 @@ Welcome to **ReliaGrowR**! This package provides modeling and plotting functions
 * Piecewise NHPP
 * Piecewise NHPP with Change Point Detection
 
+RGA is an important part of reliability engineering, focused on improving reliability during development and testing of products.
+By analyzing failure data, RGA helps engineers and researchers identify trends, estimate reliability parameters, and track improvements over time. 
+
 To learn more about RGA and this package, please view the [vignette](https://paulgovan.github.io/ReliaGrowR/articles/RGA.html).
 
 ## Installation
@@ -52,25 +55,25 @@ devtools::install_github('paulgovan/ReliaGrowR')
 
 ## Example
 
-Here is a basic example of Reliability Growth Analysis:
-
-First, load the package:
+Here is a basic example of Reliability Growth Analysis. First, load the package.
 ```{r}
 library(ReliaGrowR)
 ```
 
-Next, set up some cumulative time and failure data:
+Next, suppose a machine has failed at the following times with the corresponding number of failures. 
 ```{r}
 times <- c(100, 200, 300, 400, 500)
 failures <- c(1, 2, 1, 3, 2)
 ```
 
-Then run the rga and plot the results:
+To perform RGA, use the `rga` function and plot the results. The plot displays the cumulative failures over time along with the fitted reliability growth model. 
 ```{r}
 result <- rga(times, failures)
 plot(result, main = "Reliability Growth Analysis", xlab = "Cumulative Time", ylab = "Cumulative Failures")
 ```
 
+
+
 ## Code of Conduct
 
 Please note that the ReliaGrowR project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/1/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.

README.md

@@ -28,6 +28,12 @@ functions for Reliability Growth Analysis (RGA), including:
 - Piecewise NHPP
 - Piecewise NHPP with Change Point Detection
 
+RGA is an important part of reliability engineering, focused on
+improving reliability during development and testing of products. By
+analyzing failure data, RGA helps engineers and researchers identify
+trends, estimate reliability parameters, and track improvements over
+time.
+
 To learn more about RGA and this package, please view the
 [vignette](https://paulgovan.github.io/ReliaGrowR/articles/RGA.html).
 
@@ -47,22 +53,24 @@ devtools::install_github('paulgovan/ReliaGrowR')
 
 ## Example
 
-Here is a basic example of Reliability Growth Analysis:
-
-First, load the package:
+Here is a basic example of Reliability Growth Analysis. First, load the
+package.
 
 ``` r
 library(ReliaGrowR)
 ```
 
-Next, set up some cumulative time and failure data:
+Next, suppose a machine has failed at the following times with the
+corresponding number of failures.
 
 ``` r
 times <- c(100, 200, 300, 400, 500)
 failures <- c(1, 2, 1, 3, 2)
 ```
 
-Then run the rga and plot the results:
+To perform RGA, use the `rga` function and plot the results. The plot
+displays the cumulative failures over time along with the fitted
+reliability growth model.
 
 ``` r
 result <- rga(times, failures)