library(dplyr)
library(knitr)

Criação da função

test.normal <- function(x)
{
  if(!require(nortest))
    install.packages("nortest")
  library(nortest)
  # Estimativas dos parâmetros
  xb <- mean(x) # mu
  sx <- sd(x) # sigma
  cat("\n Média amostral =", xb, "\n Desvio padrão amostral =", sx, "\n")
  # Testes
  t1 <- ks.test(x, "pnorm", xb, sx) # KS
  t2 <- lillie.test(x) # Lilliefors
  t3 <- cvm.test(x) # Cramér-von Mises
  t4 <- shapiro.test(x) # Shapiro-Wilk
  t5 <- sf.test(x) # Shapiro-Francia
  t6 <- ad.test(x) # Anderson-Darling
  # Tabela de resultados
  testes <- c(t1$method,
              t2$method,
              t3$method,
              t4$method,
              t5$method,
              t6$method)
  estt <- as.numeric(c(t1$statistic,
                       t2$statistic,
                       t3$statistic,
                       t4$statistic,
                       t5$statistic,
                       t6$statistic))
  valorp <- c(t1$p.value,
              t2$p.value,
              t3$p.value,
              t4$p.value,
              t5$p.value,
              t6$p.value)
  
  resultados <- cbind(estt, valorp) %>%  as.data.frame() %>%
    mutate(valorp = ifelse(valorp < 0.001,
                           paste("< 0.001", "***"), 
                           ifelse(valorp < 0.01,
                                  paste(round(valorp, 4), "**"),
                                  ifelse(valorp < 0.05,
                                         paste(round(valorp, 4), "*"),
                                         ifelse(valorp < 0.1, 
                                                paste(round(valorp, 4), "."), 
                                                paste(round(valorp, 4), "ns"))))))
  rownames(resultados) <- testes
  colnames(resultados) <- c("Estatística", "p")
  resultados
  
}

Gerando os dados

set.seed(1234)
dados <- rnorm(100,5)

dados
##   [1] 3.792934 5.277429 6.084441 2.654302 5.429125 5.506056 4.425260 4.453368
##   [9] 4.435548 4.109962 4.522807 4.001614 4.223746 5.064459 5.959494 4.889715
##  [17] 4.488990 4.088805 4.162828 7.415835 5.134088 4.509314 4.559452 5.459589
##  [25] 4.306280 3.551795 5.574756 3.976344 4.984862 4.064051 6.102298 4.524407
##  [33] 4.290560 4.498742 3.370907 3.832381 2.819960 3.659007 4.705706 4.534102
##  [41] 6.449496 3.931357 4.144635 4.719377 4.005660 4.031486 3.892682 3.748014
##  [49] 4.476172 4.503150 3.193969 4.417924 3.891110 3.985038 4.837690 5.563056
##  [57] 6.647817 4.226647 6.605910 3.842191 5.656588 7.548991 4.965240 4.330366
##  [65] 4.992395 6.777084 3.861392 6.367827 6.329565 5.336473 5.006893 4.544531
##  [73] 4.633476 5.648287 7.070271 4.846602 3.609299 4.276418 5.258262 4.682941
##  [81] 4.822210 4.830006 3.627698 4.826213 5.850232 5.697609 5.549997 4.597268
##  [89] 4.808406 3.805472 4.946841 5.255196 6.705964 6.001513 4.504417 5.355550
##  [97] 3.865392 5.878204 5.972917 7.121117

Utilizando a função

test.normal(dados) %>% kable(digits = 4)
## 
##  Média amostral = 4.843238 
##  Desvio padrão amostral = 1.004405
Estatística p
One-sample Kolmogorov-Smirnov test 0.1012 0.257 ns
Lilliefors (Kolmogorov-Smirnov) normality test 0.1012 0.0133 *
Cramer-von Mises normality test 0.2190 0.003 **
Shapiro-Wilk normality test 0.9659 0.0108 *
Shapiro-Francia normality test 0.9669 0.0147 *
Anderson-Darling normality test 1.2438 0.0029 **