Package 'condGEE'

matrix of data with one row for each gap time; the first column should be a subject ID, the second column the gap time, the third column a completeness indicator equal to 1 if the gap time is complete and 0 if the gap time is censored, and the remaining columns the covariates for use in the mean and variance functions

vector containing initial guesses for the unknown parameter vector

the specification for the mean of the gap time; the default is a linear combination of the covariates; the function should take two arguments (theta, and a matrix of covariates with each row corresponding to one gap time) and it should return a vector of means

the derivative of mu.fn with respect to the parameter vector; the default corresponds to a linear mean function

the specification for $V^2$, where the variance of the gap time is $\sigma^2 V^2$; the default is a vector of ones; the function should take two arguments ($theta$, and a matrix of covariates with each row corresponding to one gap time) and it should return a vector of variances

the function to solve for the conditional mean length of the censored gap times; its sole argument should be the vector of standardized (i.e.\ $(Y-\mu)/(\sigma V)$) censored gap times; the default assumes the standardized censored gap times follow a standard normal distribution, but K1.t3 and K1.exp are also provided in the package - they assume a standardized t with 3 degrees of freedom and an exponential with mean 0 and variance 1 respectively

the function to solve for the conditional mean length of the square of the censored gap times; its sole argument should be the vector of standardized (i.e.\ $(Y-\mu)/(\sigma V)$) censored gap times; the default assumes the standardized censored gap times follow a standard normal distribution, but K2.t3 and K2.exp are also provided in the package - they assume a standardized t with 3 degrees of freedom and an exponential with mean 0 and variance 1 respectively

logical, if FALSE, the mean and variance parameters are solved for simultaneously, increasing efficiency, but decreasing the leeway to misguess start and still find the root of the GEE

logical, if FALSE, the function returns NULL for the asymptotic variance matrix

see multiroot; maximal number of iterations allowed

see multiroot; relative error tolerance

see multiroot; absolute error tolerance

see multiroot; if between two iterations, the maximal change in the variable values is less than this amount, then it is assumed that the root is found

see multiroot; logical, if FALSE, then an R implementation of Newton-Raphson is used

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