Question about scaling in (R) Principal Components Regression
I agree that if scaling is used on the training data, it should also be used on the test data.
However, from what I see in the pcr documentation there is not scale option in the function. This seems to be confirmed by running the code from the example in the documentation:
pcr: Prinicpal component regression transfer function models
&gt; library(analogue)
&gt; data(ImbrieKipp)
&gt; data(SumSST)
&gt; pcr(ImbrieKipp, SumSST, tranFun = Hellinger, scale = TRUE)

    Principal Component Regression Model

Call:
pcr(x = ImbrieKipp, y = SumSST, tranFun = Hellinger, scale = TRUE)

No. of Components: 27

RMSE (Apparent):
     PC1      PC2      PC3      PC4      PC5      PC6      PC7      PC8      PC9     PC10     PC11 
2.381215 1.707588 1.680896 1.679774 1.608903 1.535145 1.507995 1.496939 1.496908 1.432142 1.426444 
    PC12     PC13     PC14     PC15     PC16     PC17     PC18     PC19     PC20     PC21     PC22 
1.405155 1.391348 1.349172 1.349172 1.315284 1.313187 1.311801 1.291201 1.206484 1.188438 1.187503 
    PC23     PC24     PC25     PC26     PC27 
1.171215 1.170947 1.170380 1.162497 1.162355 
&gt; pcr(ImbrieKipp, SumSST, tranFun = Hellinger, scale = FALSE)

    Principal Component Regression Model

Call:
pcr(x = ImbrieKipp, y = SumSST, tranFun = Hellinger, scale = FALSE)

No. of Components: 27

RMSE (Apparent):
     PC1      PC2      PC3      PC4      PC5      PC6      PC7      PC8      PC9     PC10     PC11 
2.381215 1.707588 1.680896 1.679774 1.608903 1.535145 1.507995 1.496939 1.496908 1.432142 1.426444 
    PC12     PC13     PC14     PC15     PC16     PC17     PC18     PC19     PC20     PC21     PC22 
1.405155 1.391348 1.349172 1.349172 1.315284 1.313187 1.311801 1.291201 1.206484 1.188438 1.187503 
    PC23     PC24     PC25     PC26     PC27 
1.171215 1.170947 1.170380 1.162497 1.162355 

I did check the the data were not standardised to begin with !
So in your case I would simply use scale() on the data before running pcr

Edit to address the point raised in comments. In the pcr function there is no warning or error if you use a completely fictitious variable:
&gt; pcr(ImbrieKipp, SumSST, tranFun = Hellinger, SomethingIjustMadeUp = TRUE)

    Principal Component Regression Model

Call:
pcr(x = ImbrieKipp, y = SumSST, tranFun = Hellinger, SomethingIjustMadeUp = TRUE)

No. of Components: 27

RMSE (Apparent):
     PC1      PC2      PC3      PC4      PC5      PC6      PC7      PC8      PC9     PC10     PC11 
2.381215 1.707588 1.680896 1.679774 1.608903 1.535145 1.507995 1.496939 1.496908 1.432142 1.426444 
    PC12     PC13     PC14     PC15     PC16     PC17     PC18     PC19     PC20     PC21     PC22 
1.405155 1.391348 1.349172 1.349172 1.315284 1.313187 1.311801 1.291201 1.206484 1.188438 1.187503 
    PC23     PC24     PC25     PC26     PC27 
1.171215 1.170947 1.170380 1.162497 1.162355 

No warning or error