The value of center determines how column centering is performed. If center is a numeric-alike vector with length equal to the number of columns of x, then each column of x has the corresponding value from center subtracted from it. If center is TRUE then centering is done by subtracting the column means (omitting censoring values) of x$X from their corresponding columns, and if center is FALSE, no centering is done. The same is done for x$lo and x$up.
The value of scale determines how column scaling is performed (after centering). If scale is a numeric-alike vector with length equal to the number of columns of x, then each column of x$X is divided by the corresponding value from scale. If scale is TRUE then scaling is done by dividing the (centered) columns of x$X by their standard deviations if center is TRUE, and the root mean square otherwise. If scale is FALSE, no scaling is done. The same is done for x$lo and x$up.
The root-mean-square for a (possibly centered) column is defined as \(\sqrt{\sum(x^2)/(n-1)}\), where \(x\) is a vector of observed values and \(n\) is the number of observed values. In the case center = TRUE, this is the same as the standard deviation, but in general it is not. (To scale by the standard deviations without centering, use scale(x, center = FALSE, scale = apply(x, 2, sd, na.rm = TRUE)).)