Shared C++ Core for Quantitative Ethnography Packages • libqe

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; inst/include/libqe/ is a committed copy of the repo-level include/libqe/ headers, kept in sync by scripts/sync-headers.sh):

R CMD INSTALL R/

Using libqe headers in your R package

Add to your DESCRIPTION:

LinkingTo: Rcpp, RcppArmadillo, libqe
Imports: Rcpp

Then in your .cpp files:

// [[Rcpp::depends(RcppArmadillo, libqe)]]
#include <RcppArmadillo.h>
#include <libqe/libqe.hpp>

// All qe:: functions are now available

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).

The ENA model code (rotations, node positions, ena_correlation, CCD) moved to rENA and all accumulation (accumulate_stanza, apply_tensor, …) to tma in libqe 0.2.0.

Door, trajectory, and stability

Function	Description
`door_lookback_block(block, lookback_size, aggregate_mean, weighting_linear, segment_ids)`	Lookback pooling over one unit block, matching ETM missing-value behavior.
`door_ema_block(block, alpha, segment_ids)`	EMA smoothing over one unit block, carrying prior smoothed values for missing observations.
`fit_trajectory_poly(points, t, max_degree, fixed_degree, criterion, basis)`	Fit a 2D polynomial trajectory; defaults to R-compatible orthogonal polynomials.
`eval_trajectory_curve(coeffs_x, coeffs_y, t_eval)`	Evaluate fitted polynomial trajectory coordinates.
`eval_trajectory_derivatives(coeffs_x, coeffs_y, t_eval)`	Evaluate velocity, acceleration, speed, heading rate, and curvature.
`integrated_trajectory_distance(...)`	Integrated Euclidean distance between two fitted trajectory curves.
`lagged_trajectory_distance(...)`	Normalized lagged distance for follower/leader curve comparisons.
`signed_turn_lag(...)` / `sweep_signed_turn_lags(...)`	Discrete turn-lag follower/leader distance metrics.
`dist_dist_correlation(X, Y)`	Pearson correlation between pairwise distance structures.

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 bounds

mean_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 2

The lower/upper values are identical — the shapes are transposed. Convert with:

t(mean_ci(pts)[, 2:3])   # → 2 × n_dims matching rENA's format

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"

# Sphere normalization
lw <- matrix(c(1,1,0, 1,0,1, 0,1,1), nrow = 3, byrow = TRUE)
normalize_networks(lw)

# 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)

Running tests

testthat::test_local()
# or
R CMD check .

libqe R package