log(f(y, theta, ...)) with respect to theta[[i]]/theta[[i]] and theta[[j]].numDerivLogf(f, logZero = .Machine$double.xmin,
logInf = .Machine$double.xmax/2, method = "Richardson",
method.args = list(eps = 1e-04, d = 0.1, zero.tol =
sqrt(.Machine$double.eps/7e-07), r = 4, v = 2, show.details = F))numDeriv2Logf(f, logZero = .Machine$double.xmin,
logInf = .Machine$double.xmax/2, method = "Richardson",
method.args = list(eps = 1e-04, d = 0.1, zero.tol =
sqrt(.Machine$double.eps/7e-07), r = 4, v = 2, show.details = F))
function(y, theta, ...), where theta is a list of parameters.
A joint probability density function.log(f) should return if f evaluates to 0.log(f) should return if f evaluates to Inf.numDerivLogf returns function(y, theta, i, ...) which evaluates to the first derivative of log(f(y, theta, ...)) with respect to theta[[i]].numDeriv2Logf returns function(y, theta, i, j, ...) which evaluates to the second derivative of log(f(y, theta, ...)) with respect to theta[[i]] and theta[[j]].
NaNs if the log evaluates to (negative) Inf so you may want to specify logZero and logInf.buildf, DerivLogf, fisherI