crdPlan: Completely randomized design (CRD)
In Prof-ThiagoOliveira/planExp: Experimental Planning

Description Usage Arguments Author(s) Examples

levels of treatment are randomly assigned to the experimental units. Through randomization, every experimental unit has same probability of receiving any treatment. This provides a basis for making a valid estimate of random error which is so essential in testing of significance of genuine differences

1	crdPlan(treat, rep, seed)

`treat`	`numeric` or complex vector containing treatments levels.
`rep`	`numeric` or complex vector containing thenumber of replications by treatment.
`seed`	A single `numeric` value, interpreted as an integer, that specifies the starting value of the random number generator.

Thiago de Paula Oliveira, thiago.paula.oliveira@usp.br

## 3 treatments with same number of replicates
crdPlan(treat = 3, rep = 4)

## 4 treatments with different number of replicates
crdPlan(treat = 4, rep = c(rep(4, 3), 6))

## Running the shiny app
treat <- LETTERS[seq( from = 1, to = 10 )]
design <- crdPlan(treat = treat, rep = 6)
## Not run: 
buildShiny(design)

## End(Not run)

## Treatment with different replicates
design2 <- crdPlan(treat = paste("T", seq(1, 7, 1)),
                   rep = c(rep(6, 4),rep(7, 3)))
## Not run: 
buildShiny(design2)

## End(Not run)