Jackknife is a bit different from bootstrap, as I'm sure you know, but there is some jackknife distance stuff for outlier analysis in the multivariate platform. That's the only place I know of with any built-in jackknife analysis.
https://www.jmp.com/support/help/14/distance-measures.shtml#240680
For your use case, you would probably need to script it up by hand. It wouldn't be hard, although, why not bootstrap if you have access to JMP Pro. I thought of a way to vectorize the operation for a standard arithmetic mean. Here's an example.
//Create Example Table
n = 100; //number of observations
dt = New Table("Jackknife Example",<< New Column("X",Formula(Random Normal(100,10))));
dt << Add Rows(n);
//Jackknife Calculations
b = Identity(n)*-1 + 1; //matrix of 1s with 0s on the diagonal
Xvals = dt:X << Get Values; //get column X as vector
repX = shape(Xvals, n, n); //repeat vector X n times in new matrix (repeated as row vectors)
means_jack = repX:*b*j(n,1,1)/(n-1); //compute jackknife means
//Results
xbar = mean(means_jack);
var_jack = (n-1)^2/n*stddev(means_jack)^2;
se_jack = sqrt(var_jack);
-- Cameron Willden
