model_polytomous_3dindex creates indices extracting 3D stats
model_polytomous_3dresponse converts responses from 2D to 3D
hermite_gauss stores pre-computed hermite gaussian
quadratures points and weights
nr_iteration updates the parameters using the Newton-Raphson method
model_polytomous_3dindex(u)model_polytomous_3dresponse(u)
hermite_gauss(degree = c("20", "11", "7"))
nr_iteration(param, free, dv, h_max, lr, bound)
estimate_3pl_debug(tracking, k)
estimate_3pl_eval(true_params, t, a, b, c, t_free, a_free, b_free, c_free)
estimate_gpcm_debug(tracking, k)
estimate_gpcm_eval(true_params, n_c, t, a, b, d, t_free, a_free, b_free,
d_free)
estimate_grm_debug(tracking, k)
estimate_grm_eval(true_params, n_c, t, a, b, t_free, a_free, b_free)
the observed response, 2d matrix, values start from 0
the degree of hermite-gauss quadrature: '20', '11', '7'
the parameter being estimated
TRUE to free parameters, otherwise fix parameters
the first and second derivatives
the maximum value of h
the learning rate
the lower and upper bounds of the parameter
estimation tracking information
the number of iterations in estimation
a list of true parameters
estimated ability parameters
estimated discrimination parameters
estimated difficulty parameters
estimated guessing parameters
TRUE to estimate ability parameters, otherwise fix
TRUE to estimate discrimination parameters, otherwise fix
TRUE to estimate difficulty parameters, otherwise fix
TRUE to estimate guessing parameters, otherwise fix