Package index • libqe

All functions

accumulate_stanza(): Traditional stanza-window accumulation (rENA model)
accumulate_unit(): Ground/response accumulation for one unit (tma model)
accumulate_unit_with_rows(): Ground/response accumulation for one unit — returns unit vector and per-row matrix
apply_tensor(): Tensor-based multi-modal accumulation for one unit (tma model)
center_points(): Center points (subtract column means)
choose_two(): Number of upper-triangle pairs for n codes
code_connections(): Pairwise products → upper-triangle vector
complete_rotation(): Complete a rotation — keep named axes verbatim, fill remainder from SVD
connection_indices(): Upper-triangle index pairs
connection_matrix(): Core connection matrix for one ground+response pair
connection_names(): Code-name pairs for upper-triangle positions ("A & B")
deflate(): Project a matrix onto the hyperplane orthogonal to a unit-norm axis
directed_node_positions(): Least-squares node positions for directed ENA
directed_node_positions_combine_pairs(): Directed node positions with paired ground+response rows combined
ena_correlation(): Pearson correlation with CI between ENA points and centroids
ena_svd(): SVD rotation (matches prcomp(retx=F, scale=F, center=F, tol=0))
flat_index(): Compute a column-major linear index into a multi-dimensional array
fold_directed_network(): Fold a directed (n*n) vector into an undirected upper-triangle vector
generalized_means_rotation(): Generalized Means Rotation
libqe-package libqe: libqe: Shared C++ Core for Quantitative Ethnography Packages
mean_ci(): Confidence interval for the mean of a group of ENA unit points
means_rotation(): Means rotation
network_to_vector(): Flatten an adjacency matrix to a connection vector
node_positions(): Least-squares node positions for undirected ENA
normalize_networks(): Row-wise L2 (sphere) normalization
orthogonal_svd(): Orthogonal SVD — orthonormalize named axes via QR, fill the rest from SVD
outlier_ci(): Outlier interval based on IQR (Tukey fence) for a group of ENA unit points
rolling_window_sum(): Rolling backward window sum of a code matrix
row_connections(): Per-row upper-triangle co-occurrence matrix
scale_networks(): Max-norm scaling (divide all rows by the largest row L2 norm)