log(f(y, theta, ...)) with respect to theta[[i]]/theta[[i]] and theta[[j]].numDerivLogf(f, isLogf = FALSE, logZero = .Machine$double.xmin,
logInf = .Machine$double.xmax/2, method = "Richardson",
method.args = list())numDeriv2Logf(f, isLogf = FALSE, logZero = .Machine$double.xmin,
logInf = .Machine$double.xmax/2, method = "Richardson",
method.args = list())
function(y, theta, ...), where theta is a list of parameters.
A joint probability density function.TRUE if f is already log(f).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