Hello. I have a use case in R and the solution is eluding me.
The code below produces 2 vectors and a tibble 
library(tidyverse)
lamda_values < c(0.2, 0.3, 0.4)
alpha_values < c(0.3, 0.5, 0.9)
set.seed(123)
data < tibble(
sales = runif(5, min = 1000, max = 1500),
var_1 = runif(5, min = 12, max = 25),
var_2 = runif(5, min = 75, max = 90),
)
I want to

Apply a transformation to the data using the following formula as an example;
(var * lambda_value) ^ alpha_value
for bothvar_1
and ```var_2````. 
Run a regression a linear regression on the transformed data using
sales
as the target. 
Return a tibble with the lambda and alpha values (for each var) that produced the highest rsquared (similar to the output for
show_best()
in thetune
package. Basically I need to run a separate regression for on transformed data for all possible combinations of lambda and alpha values for each var.