Optimization algorithm for the estimation of beta regression coefficient functions and intercepts
optimization(x, y, beta, loglik, gradient, Hessian)a design matrix which is a product of inner product of basis functions and basis coefficients of functional covariate X
a response variable of class factor
initial values for beta regression coefficients and intercepts
log-likelihood function
function for the estimation of first derivative of log-likelihood function - gradient
function for the estimation of second derivative of log-likelihood function - Hessian
a vector with estimated beta regression coefficients and intercepts
a value of the log-likelihood function at the estimated optimum
a vector of gradient values at the estimated optimum
Hessian matrix at the estimated optimum