R package that ships the libqe C++ headers so downstream packages can consume them via LinkingTo, and exports Rcpp-wrapped versions of every function for direct use and testing in R.
Installation
install.packages("libqe", repos = c("https://cran.qe-libs.org", "https://cran.rstudio.com"))From source (within the repo root):
Using libqe headers in your R package
Add to your DESCRIPTION:
LinkingTo: Rcpp, RcppArmadillo, libqe
Imports: RcppThen in your .cpp files:
R function reference
All exported R functions are prefixed ``.
Adjacency utilities
| Function | Description |
|---|---|
connection_indices(len, row) |
Upper-triangle (i, j) index pairs for a matrix of side len. row = -1 returns both rows, 0 row indices only, 1 col indices only. |
code_connections(v) |
Pairwise products of a code vector → upper-triangle connection vector. |
fold_directed_network(v) |
Fold a directed (n²) flat vector into an undirected upper-triangle vector by summing symmetric pairs. |
network_to_vector(x, full) |
Flatten an adjacency matrix. full = TRUE returns the full n² vector (directed); full = FALSE returns the upper triangle. |
connection_names(v) |
Generate code-pair labels ("A & B") for every upper-triangle position from a character vector of code names. |
Normalization
| Function | Description |
|---|---|
normalize_networks(m) |
Row-wise L2 normalization. Each row is divided by its Euclidean norm (zero rows are left as-is). |
scale_networks(m) |
Max-norm scaling: divide all rows by the largest row L2 norm in the matrix. |
Modeling
| Function | Description |
|---|---|
mean_ci(points, conf_level) |
t-based confidence interval for the mean of a group of ENA unit points. Returns an n_dims × 3 matrix with columns [mean, ci_lower, ci_upper]. Uses n-1 degrees of freedom; default conf_level = 0.95. Bounds match rENA’s t.test(points[,d])$conf.int exactly — see shape note below. When n == 1 the CI bounds are ±Inf. |
outlier_ci(points, iqr_factor) |
Outlier interval using IQR × iqr_factor (Tukey fence; default iqr_factor = 1.5). Returns an n_dims × 2 matrix with columns [lower, upper] where lower = -iqr_factor × IQR and upper = +iqr_factor × IQR. Symmetric around 0. Matches rENA’s IQR-based formula exactly. |
center_points(values) |
Subtract column means (center each dimension). |
ena_correlation(points, centroids, conf_level) |
Pearson correlation with confidence interval between ENA unit points and group centroids. Returns a matrix with columns r, lower, upper. Default conf_level = 0.95. |
node_positions(adj_mats, t, num_dims) |
Least-squares node positions for undirected ENA. Returns a list with nodes, centroids, weights, points. |
directed_node_positions(line_weights, points, num_dims) |
Least-squares node positions for directed ENA. Same return structure. |
directed_node_positions_combine_pairs(line_weights, points, num_dims) |
Directed node positions where paired ground+response rows are combined before solving. Same return structure. |
Accumulation
| Function | Description |
|---|---|
connection_matrix(ground, response, response_weight, ordered) |
Core adjacency math for one ground+response event pair. ordered = TRUE → directed; FALSE → undirected. |
accumulate_stanza(codes, window_back, window_forward, binary, ordered=FALSE) |
Stanza-window accumulation. ordered=FALSE (default): undirected upper-tri, returns n_rows × choose(n_codes, 2). ordered=TRUE: directed — focal row as response, prior window rows as ground, returns n_rows × n_codes². Use .Machine$integer.max for an infinite back window. |
row_connections(codes, binary) |
Per-row upper-triangle co-occurrence matrix without windowing. |
rolling_window_sum(codes, window_size) |
Rolling backward sum of raw code values (no upper-tri transform). |
accumulate_unit(codes, unit_rows, decay_fn, ordered) |
Ground/response accumulation for one unit (tma model). decay_fn is an R function mapping a distance vector to weights. unit_rows are 0-based. |
accumulate_unit_with_rows(codes, unit_rows, decay_fn, ordered) |
Like accumulate_unit but also returns per-response-row connection data. Returns a list with networks and row_networks. |
flat_index(indices, dims) |
Column-major linear index into a multi-dimensional array. indices and dims are 0-based integer vectors. |
apply_tensor(tensor, dims, dims_sender, dims_receiver, dims_mode, context_lookup, unit_rows, codes, times, ordered) |
Tensor-based multi-modal accumulation (tma model). Returns a list with connection_counts and row_connection_counts. |
Shape note — mean_ci vs rENA
rENA returns a 2 × n_dims matrix (rows = lower/upper, columns = dimensions):
# rENA
matrix(c(t.test(pts[,1])$conf.int, t.test(pts[,2])$conf.int), ncol = 2)
# [,1] [,2]
# [1,] lower lower ← row 1 = lower bounds
# [2,] upper upper ← row 2 = upper boundsmean_ci returns n_dims × 3 (rows = dimensions, columns = mean/lower/upper):
mean_ci(pts)
# [,1] [,2] [,3]
# [1,] mean lower upper ← dim 1
# [2,] mean lower upper ← dim 2The lower/upper values are identical — the shapes are transposed. Convert with:
Quick examples
library(libqe)
# Code-pair labels for 4 codes
connection_names(c("A", "B", "C", "D"))
# [1] "A & B" "A & C" "A & D" "B & C" "B & D" "C & D"
# Stanza-window accumulation (infinite back window)
codes <- matrix(c(1,1,0, 1,0,1, 0,1,1), nrow = 3, byrow = TRUE)
accumulate_stanza(codes, window_back = .Machine$integer.max, binary = TRUE)
# Sphere normalization
normalize_networks(codes)
# Group confidence interval (matches R's t.test per dimension)
group_pts <- matrix(rnorm(20), nrow = 10, ncol = 2)
mean_ci(group_pts, conf_level = 0.95)
# n_dims × 3: cols = [mean, ci_lower, ci_upper]
# Outlier interval (matches rENA's IQR * 1.5 formula)
outlier_ci(group_pts, iqr_factor = 1.5)
# n_dims × 2: cols = [lower, upper] (symmetric: lower = -1.5*IQR, upper = +1.5*IQR)
# tma-style accumulation with a simple decay function
decay <- function(distances) rep(1, length(distances))
accumulate_unit(codes, unit_rows = c(0L, 1L, 2L), decay_fn = decay, ordered = FALSE)