I'm using weibulltools package in R to estimate the life of a product. I would like to know if it is possible to add more functions like creating probability density plots, hazard plots, reliability plot (which is 1- unreliability). As far as I know, the weibulltolls only calculates the unreliability plots. How to do that?

Here it is an example on how to plot unreliability graph

library(weibulltools)
library(readxl)
data3 <- data3 <- structure(list(id = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 
                  13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 
                  29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 
                  45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 
                  61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72), cycles = c(300, 
                                                                              300, 300, 300, 300, 291, 274, 271, 269, 257, 256, 227, 226, 224, 
                                                                              213, 211, 205, 203, 197, 196, 190, 189, 188, 187, 184, 180, 180, 
                                                                              177, 176, 173, 172, 171, 170, 170, 169, 168, 168, 162, 159, 159, 
                                                                              159, 159, 152, 152, 149, 149, 144, 143, 141, 141, 140, 139, 139, 
                                                                              136, 135, 133, 131, 129, 123, 121, 121, 118, 117, 117, 114, 112, 
                                                                              108, 104, 99, 99, 96, 94), status = c(0, 0, 0, 0, 0, 1, 1, 1, 
                                                                                                                    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
                                                                                                                    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
                                                                                                                    1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
                                                                                                                    1)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, 
                                                                                                                                                                                 -72L))
View(data3)


rel_tbl <- reliability_data(
  data = data3,
  x = "cycles",
  status = "status",
  id = NULL
)

cdf_tbl <- estimate_cdf(
  x = rel_tbl,
  methods = "johnson", # choose beteen jhonson, kaplan, nelson, mr method
  options = list()
)

p_prob <- plot_prob(
  x = cdf_tbl,
  distribution = "weibull", # type any distribution
  title_main = "Probability Plot",
  title_x = "Characteristic",
  title_y = "Unreliability",
  title_trace = "Sample",
  plot_method = "plotly"
)


rr <- rank_regression(
  x = cdf_tbl,
  distribution = "weibull", # type any distribution
  conf_level = 0.95,
  direction = "x_on_y",
  control = list(),
  options = list(conf_method = "HC")
)

rr

conf_bb <- confint_betabinom(rr,
                             b_lives = c(
                               0.01,
                               0.1,
                               0.5
                             ),
                             bounds = "two_sided",
                             conf_level = 0.95,
                             direction = "y"
)

p_conf <- plot_conf(
  p_obj = p_prob,
  x = conf_bb,
  title_trace_mod = "Fit",
  title_trace_conf = "Confidence Limit"
)


p_conf
View(conf_bb)

here it is the data structure

id cycles status
1   300 0
2   300 0
3   300 0
4   300 0
5   300 0
6   291 1
7   274 1
8   271 1
9   269 1
10  257 1
11  256 1
12  227 1
13  226 1
14  224 1
15  213 1
16  211 1
17  205 1
18  203 1
19  197 1
20  196 1
21  190 1
22  189 1
23  188 1
24  187 1
25  184 1
26  180 1
27  180 1
28  177 1
29  176 1
30  173 1
31  172 1
32  171 1
33  170 1
34  170 1
35  169 1
36  168 1
37  168 1
38  162 1
39  159 1
40  159 1
41  159 1
42  159 1
43  152 1
44  152 1
45  149 1
46  149 1
47  144 1
48  143 1
49  141 1
50  141 1
51  140 1
52  139 1
53  139 1
54  136 1
55  135 1
56  133 1
57  131 1
58  129 1
59  123 1
60  121 1
61  121 1
62  118 1
63  117 1
64  117 1
65  114 1
66  112 1
67  108 1
68  104 1
69  99  1
70  99  1
71  96  1
72  94  1