edge_weight_inverse: Alternative edge weight functions for network-based item...

In meow: Unified Framework for Computer Adaptive Testing Simulations

Alternative edge weight functions for network-based item selection

Description

These functions provide different approaches to calculating edge weights from the adjacency matrix.

Usage

edge_weight_inverse(adj_mat, alpha = 1)

edge_weight_negative_log(adj_mat, alpha = 1)

edge_weight_linear(adj_mat, max_co_responses = NULL)

edge_weight_power(adj_mat, beta = 0.5, alpha = 1)

edge_weight_exponential(adj_mat, lambda = 0.1, alpha = 1)

Arguments

adj_mat	The adjacency matrix where entry i,j is the number of co-responses between items i and j
alpha	Smoothing parameter for avoiding division by zero
max_co_responses	Scaling factor for linear weighting
beta	Exponent for power transformation
lambda	Decay constant for exponential decay weighting

Value

A matrix of edge weights for use in distance calculations

Examples

adj_mat <- matrix(c(3, 1, 1, 2), nrow = 2)
edge_weight_inverse(adj_mat)

meow documentation built on July 6, 2026, 5:11 p.m.