#' @title Accept the current proposed move
#' @description "accept_move" updates the output list after accepting the current proposed move.
#'
#' @param output: List of output values with entries as explained below.
#' @param num_estimate: int.
#' Number of parameters to estimate.
#' @param estimate_idx: list of int.
#' Indices of parameters to estimate in the model's full parameter list.
#' @param initial_values: list of float.
#' Starting values for parameters to estimate, taken from the parameters'
#' nominal values in the model or explicitly specified in 'options'.
#' @param initial_position: list of float.
#' Starting position of the MCMC walk in parameter space (log10 of 'initial_values').
#' @param position: list of float.
#' Current position of MCMC walk in parameter space, i.e. the most
#' recently accepted move.
#' @param test_position: list of float.
#' Proposed MCMC mmove.
#' @param acceptance: int.
#' Number of accepted moves.
#' @param T: float.
#' Current value of the simulated annealing temperature.
#' @param T_decay: float.
#' Constant for exponential decay of 'T', automatically calculated such
#' that T will decay from 'options$T_init' down to 1 over the first
#' 'options$anneal_length' setps.
#' @param sig_value: float.
#' Current value of sigma, the scaling factor for the proposal distribution.
#' The MCMC algorithm dynamically tunes this to maintain the aaceptance
#' rate specified in 'options$accept_rate_target'.
#' @param iter: int.
#' Current MCMC step number.
#' @param start_iter: int.
#' Starting MCMC step number.
#' @param ode_options: list.
#' Options for the ODE integrator, currently just 'rtol' for relative
#' tolerance and 'atol' for absolute tolerance.
#' @param initial_prior: float.
#' Starting prior value, i.e. the value at 'initial_position'.
#' @param initial_likelihood: float.
#' Starting likelihood value, i.e. the value at 'initial_position'.
#' @param initial_posterior: float.
#' Starting posterior value, i.e. the value at 'initial_position'.
#' @param accept_prior: float.
#' Current prior value i.e. the value at 'position'.
#' @param accept_likelihood: float.
#' Current likelihood value i.e. the value at 'position'.
#' @param accept_posterior: float.
#' Current posterior value i.e. the value at 'position'.
#' @param test_prior: float.
#' Prior value at 'test_position'.
#' @param test_likelihood: float.
#' Likelihood value at 'test_position'.
#' @param test_posterior: float.
#' Posterior value at 'test_position'.
#' @param hessian: array of float.
#' Current hessian of the posterior landscape. Size is
#' 'num_estimate' x 'num_estimate'.
#' @param positions: array of float.
#' Trace of all proposed moves. Size is 'num_estimate' x 'nsteps'.
#' @param priors: array of float.
#' Trace of all priors corresponding to 'positions'. Length is 'nsteps'.
#' @param likelihoods: array of float.
#' Trace of all likelihoods corresponding to 'positions'. Length is 'nsteps'.
#' @param posteriors: array of float.
#' Trace of all posteriors corresponding to 'positions'. Length is 'nsteps'.
#' @param alphas: array of float. Trace of 'alpha' parameter and calculated values. Length is 'nsteps'.
#' @param sigmas: array of float. Trace of 'sigma' parameter and calculated values. Length is 'nsteps'.
#' @param delta_posteriors: array of float. Trace of 'delta_posterior' parameter and calculated values. Length is 'nsteps'.
#' @param ts: array of float. Trace of 'T' parameter and calculated values. Length is 'nsteps'.
#' @param accepts: logical array.
#' Trace of wheter each proposed move was accepted or not.
#' Length is 'nsteps'.
#' @param rejects: logical array. Trace of wheter each proposed move was rejected or not. Length is 'nsteps'.
#' @param hessians: array of float.
#' Trace of all hessians. Size is 'num_estimate' x 'num_estimate' x 'num_hessians'
#' where 'num_hessians' is the actual number of hessians to be calculated.
#'
#'
#' @return The updated output after the move is accepted -- list with entries as explained in 'Arguments'.
#'
#'
accept_move = function(output){
output$accept_prior = output$test_prior
output$accept_likelihood = output$test_likelihood
output$accept_posterior = output$test_posterior
output$position = output$test_position
output$acceptance = output$acceptance + 1
output$accepts[output$iter] = TRUE
return(output)
}
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