Package {rd2dnet}

rd2dnet: Boundary Discontinuity Designs on Networks

Description

A network-native extension of the location-based boundary discontinuity framework and software of rd2d (Cattaneo, Titiunik and Yu; https://rdpackages.github.io/rd2d/), from Euclidean coordinates to network coordinates and user-defined network metrics. Please cite the rd2d papers when using this package (see citation("rd2dnet")).

Details

Typical workflow:

net    <- network_graph(edges, nodes, metric = "length")  # or travel_time(), ...
bnd    <- boundary(line, treated_region = zone)
coords <- network_coordinates(net, bnd, points)
res    <- rdnet(y, coords)

Main functions: network_graph() and the metrics, boundary(), network_coordinates(), rdnet() (with wbate() and lbate()), rdbwnet() and the density test density.rd2dnet_coords().

Author(s)

Maintainer: Zach Winship zachtwinship@gmail.com [copyright holder]

Other contributors:

• Matias D. Cattaneo (author of rd2d and rddensity, from which the bandwidth selector and density test are ported) [contributor, copyright holder]

• Roc<U+00ED>o Titiunik (author of rd2d, from which the bandwidth selector is ported) [contributor, copyright holder]

• Ruiqi Rae Yu (author of rd2d, from which the bandwidth selector is ported) [contributor, copyright holder]

• Michael Jansson (author of rddensity, from which the density test is ported) [contributor, copyright holder]

• Xinwei Ma (author of rddensity, from which the density test is ported) [contributor, copyright holder]

See Also

Useful links:

• https://github.com/zwinship/rd2dnet

• Report bugs at https://github.com/zwinship/rd2dnet/issues

Boundary separating treated from control locations

Description

A boundary line, the side that is treated, and optional masked stretches.

Usage

boundary(
  line,
  treated_region = NULL,
  treated_side = NULL,
  mask = NULL,
  tol = 1e-06
)

Arguments

Value

An object of class rd2dnet_boundary.

See Also

boundary_project(), boundary_is_treated(), boundary_is_masked().

Examples

b <- boundary(rbind(c(0, 0), c(100, 0)), treated_side = "left",
              mask = list(rect_polygon(20, 0, 40, 10), c(80, 90)))
b
boundary_is_treated(b, rbind(c(50, 1), c(50, -1)))
boundary_is_masked(b, c(10, 30, 50, 85))

Boundary geometry operations

Description

Distances to the boundary line, arc-length positions along it, and points at given positions.

Usage

boundary_distance(b, xy)

boundary_project(b, xy)

boundary_interpolate(b, arc)

Arguments

Value

boundary_distance(): Euclidean distance from each point to the line. boundary_project(): arc-length position of each point's projection onto the line. boundary_interpolate(): an ⁠n x 2⁠ matrix of coordinates.

Examples

b <- boundary(rbind(c(0, 0), c(100, 0)), treated_side = "left")
boundary_distance(b, rbind(c(10, 5), c(120, 0)))
boundary_project(b, rbind(c(10, 5), c(120, 0)))
boundary_interpolate(b, c(0, 50))

Equally spaced positions along the boundary

Description

Equally spaced positions along the boundary

Usage

boundary_grid(b, n = NULL, spacing = NULL, start = 0, end = NULL)

Arguments

Value

Numeric vector of arc-length positions.

Examples

b <- boundary(rbind(c(0, 0), c(100, 0)), treated_side = "left")
boundary_grid(b, n = 5)
boundary_grid(b, spacing = 30)

Masked stretches of the boundary

Description

Masked stretches of the boundary

Usage

boundary_is_masked(b, arc)

Arguments

Value

Logical: whether each arc-length position falls in a masked stretch.

Examples

b <- boundary(rbind(c(0, 0), c(100, 0)), treated_side = "left", mask = list(c(80, 90)))
boundary_is_masked(b, c(10, 85))

Treatment side of points

Description

Treatment side of points

Usage

boundary_is_treated(b, xy)

Arguments

Value

Logical: whether each point lies on the treated side (inside treated_region, within tol of it, or on treated_side of the line).

Examples

b <- boundary(rbind(c(0, 0), c(10, 0)), treated_side = "right")
boundary_is_treated(b, rbind(c(5, 1), c(5, -1)))

Kernel distance and boundary offset at an evaluation point

Description

Kernel distance and boundary offset at an evaluation point

Usage

coords_at(coords, j)

Arguments

Value

A list with d, the network distance from evaluation point j to every observation (for kernel weights), and o, each observation's signed offset along the boundary from j (NA when the boundary cannot be reached).

Examples

city <- load_mock_midtown()
coords <- network_coordinates(city$network(), city$boundary, city$obs, n_eval = 5)
str(coords_at(coords, 3))

Density (sorting) test at a network boundary

Description

Tests for a discontinuity in the density of observations at the boundary with the local polynomial density estimator of Cattaneo, Jansson and Ma (as in rddensity), with jackknife standard errors and bias-corrected inference, applied to the signed network distance s.

Usage

## S3 method for class 'rd2dnet_coords'
density(
  x,
  eval_index = NULL,
  window = NULL,
  h = NULL,
  p = 2,
  q = NULL,
  normalize = "network",
  kernel = "triangular",
  weights = NULL,
  bwselect = "common",
  scaleregul = 1,
  ...
)

Arguments

Details

With the default normalize = "network", each observation's distance is re-expressed as cumulative street length: G(a) is the total length of street on that side lying within network distance a of the boundary, and the test runs on ⁠sign * G(|s|)⁠. The density in that coordinate is observations per metre of street, so jumps in street supply at the boundary (a highway on one side, a dense grid on the other, a park frontage) do not masquerade as sorting. Bandwidths are then in street-length units. Masked stretches of the boundary are always excluded.

Value

An object of class rdnet_density: the density jump diff (⁠f+ - f-⁠, order p) and diff_bc (bias-corrected), its jackknife standard error se, z, pvalue, the descriptive log ratio theta (⁠log(f+/f-)⁠), side-wise densities f_left/f_right, bandwidths h_left/h_right, sample sizes n_left/n_right and within the bandwidth n_left_h/n_right_h, p, q and normalize.

References

Cattaneo, M. D., Jansson, M. and Ma, X. (2020). Simple Local Polynomial Density Estimators. Journal of the American Statistical Association 115(531): 1449-1455.

Cattaneo, M. D., Jansson, M. and Ma, X. (2018). Manipulation Testing Based on Density Discontinuity. Stata Journal 18(1): 234-261.

Examples

city <- load_mock_midtown()
coords <- network_coordinates(city$network(), city$boundary, city$obs, n_eval = 5)
density(coords)
density(coords, normalize = "none")

Street segments of a network

Description

One row per undirected street segment (the first edge of each node pair): endpoint indices u and v, geometric length (the length column, or the geometry's length), the metric cost of traversing it (NA for a precomputed metric), and its geometry oriented from u to v.

Usage

edge_frame(net)

Arguments

Value

A data frame with a list column geometry.

Examples

city <- load_mock_midtown()
head(edge_frame(city$network())[, c("u", "v", "length", "cost")])

Evaluation points of network coordinates

Description

Evaluation points of network coordinates

Usage

eval_table(coords)

Arguments

Value

A data frame with one row per evaluation point: requested arc position arc, nearest network node id node, arc position after snapping onto the network node_arc, coordinates x, y, and masked.

Examples

city <- load_mock_midtown()
coords <- network_coordinates(city$network(), city$boundary, city$obs, n_eval = 5)
eval_table(coords)

Distance-decay kernels

Description

Kernel functions evaluated at u = d / h, with d a network distance.

Usage

get_kernel(kernel)

Arguments

Value

A vectorised function of u.

Examples

k <- get_kernel("epanechnikov")
k(c(0, 0.5, 1.5))

Mock Midtown city

Description

load_mock_midtown() loads the bundled synthetic Midtown-Manhattan-style street grid and 3,000 simulated parcels (no internet needed); it is the same data as the Python package's bundled city. make_mock_midtown() simulates a new city of the same design with R's random number generator.

Usage

load_mock_midtown()

make_mock_midtown(n_obs = 3000, seed = 0, park = TRUE)

Arguments

Details

Layout (metres): 11 north-south avenues 250 m apart ("primary"), 41 east-west streets 80 m apart numbered 40th-80th ("residential"), a west-side highway ("motorway") joined to the grid by ramps ("motorway_link"), and a park north of the boundary between avenues 3 and 7 with no interior streets or parcels. The boundary is 60th Street (y = 1600); the treated zone lies south of it. The outcome is ⁠0.0008 (y - 1600) + 0.6 cos(x / 700) + 0.2 log(far) + tau(arc) T + N(0, 1)⁠ with tau given by mock_true_tau().

Value

A rd2dnet_mock_city list with nodes, edges, obs, the boundary() boundary, the park polygon park, boundary_y, true_tau (a function of arc length) and network(metric, ...), a function returning the network_graph() with the given metric.

See Also

mock_midtown_obs, mock_midtown_nodes, mock_midtown_edges.

Examples

city <- load_mock_midtown()
city
head(city$obs)
city$network("length")
small <- make_mock_midtown(n_obs = 500, seed = 1)

Network metrics

Description

A metric defines what distance means on a network_graph(). Distance is dissimilarity: it does not have to be metres. The coordinates s and o, the kernel distances and the bandwidths are all in the metric's units.

Usage

edge_cost(cost = "length", snap_scale = NULL, name = NULL)

transition_cost(
  edge_cost = "length",
  class_attr = "highway",
  penalty = NULL,
  default_penalty = 0,
  connectors = character(),
  default_class = "unclassified",
  snap_scale = NULL
)

precomputed(matrix, nodes = NULL)

travel_time(
  speed_attr = "speed_kph",
  length_attr = "length",
  default_kph = NULL
)

node_contrast(attr, weight, base = "length", snap_scale = NULL)

Arguments

Details

• edge_cost(): path cost is the sum of edge costs. cost is an edge column name ("length", "travel_time") or a function. A function is called once, vectorised over all edges, as cost(edges) or, if it takes two arguments, cost(edges, nodes); edges has columns from and to (node ids) plus every edge attribute, nodes has name, x, y and the node attributes. It returns one non-negative cost per edge (Inf blocks an edge).

• transition_cost(): edge costs plus a penalty for changing edge class (e.g. highway to local street). Edges whose class is in connectors (ramps, links) are transparent: the penalty is charged between the last non-connector class and the next one, so highway -> ramp -> highway stays cheap while highway -> ramp -> residential is expensive.

• precomputed(): a user-supplied node-by-node dissimilarity matrix. Observations then snap to nodes, since per-edge costs are unknown.

• travel_time(): seconds to traverse each edge from a length (m) and a speed (km/h) column.

• node_contrast(): ⁠base + weight * |attr(u) - attr(v)|⁠, for land-use or other node-level dissimilarity.

Value

An object of class rd2dnet_metric.

Examples

edge_cost("length")
edge_cost(function(edges) 2 * edges$length, name = "double length")
travel_time()
node_contrast("far", 50)
transition_cost("length", class_attr = "highway",
                penalty = data.frame(from = "motorway", to = "residential", penalty = 2000),
                connectors = c("motorway_link", "trunk_link"))

Mock Midtown data

Description

The bundled synthetic city used in examples, tests and the vignette (see load_mock_midtown(), which assembles them with the boundary).

Format

mock_midtown_obs: 3,000 simulated parcels with columns

x, y

projected coordinates (metres)

treated

south of the boundary (60th Street, y < 1600)

far

floor-area ratio, used as intensity weight

block

city block id, for clustering

outcome

simulated outcome

mock_midtown_nodes: 450 street intersections with columns name, x, y and a node-level floor-area ratio far.

mock_midtown_edges: street segments with columns from, to, length (metres), highway (road class), speed_kph and travel_time (seconds).

Source

Simulated; identical to the data bundled with the Python package rd2dnet.

Mock city treatment effect

Description

The treatment effect used to simulate the mock city's outcome, by arc length along its boundary: ⁠1.5 + 0.75 sin(2 pi x / 2500)⁠ with x the easting.

Usage

mock_true_tau(arc)

Arguments

Value

Numeric vector of true effects.

Examples

mock_true_tau(c(0, 500, 1000))

Network coordinates around a boundary

Description

Translates point locations into network coordinates around a boundary: the signed network distance s to the boundary (positive on the treated side) and, at every evaluation point b along the boundary, the network distance d(b) used for kernel weights and the signed offset o(b) along the boundary. These replace rd2d's Euclidean ⁠(X1 - b1, X2 - b2)⁠.

Usage

network_coordinates(
  network,
  boundary,
  points,
  treated = NULL,
  eval_points = NULL,
  n_eval = 25,
  snap = "edge",
  lonlat = FALSE,
  split_edges = TRUE
)

## S3 method for class 'rd2dnet_coords'
as.data.frame(x, row.names = NULL, optional = FALSE, j = NULL, ...)

Arguments

Details

Each observation snaps to the street network on its own side of the boundary; its off-network depth is charged at the network's snap_scale. Repeated locations (panels, sensors) are processed once.

Value

An object of class rd2dnet_coords, a list with (among others) s, treated, xy, crossing_arc (where each observation reaches the boundary), eval_arc, eval_node_arc, eval_xy, eval_masked, loc (location id of each observation), n_obs, n_eval, n_locations, and the (boundary-split) network and boundary. Use coords_at() for d and o at an evaluation point, eval_table() for the evaluation points, and as.data.frame() for a per-observation table.

Examples

city <- load_mock_midtown()
coords <- network_coordinates(city$network(), city$boundary, city$obs, n_eval = 11)
coords
head(as.data.frame(coords, j = 5))

Network distances

Description

Metric distances between network nodes.

Usage

distances_from(net, sources, limit = Inf)

nearest_source(net, sources)

node_distance(net, a, b)

Arguments

Value

distances_from(): a ⁠length(sources) x n_nodes⁠ matrix. nearest_source(): a list with, for every node, the distance to the nearest source (dist) and that source's node index (src, NA if unreachable). node_distance(): a single distance.

Examples

nodes <- data.frame(name = 1:3, x = c(0, 1, 2), y = 0)
net <- network_graph(data.frame(from = 1:2, to = 2:3, length = c(5, 7)), nodes)
distances_from(net, 1)
nearest_source(net, c(1, 3))
node_distance(net, 1, 3)

Spatial network with a distance metric

Description

A street (or any spatial) network together with the metric that defines distance on it.

Usage

network_graph(
  edges,
  nodes,
  metric = "length",
  directed = FALSE,
  direction = "both",
  x = "x",
  y = "y",
  crs = NULL,
  snap_scale = NULL
)

Arguments

Value

An object of class rd2dnet_network.

See Also

network_graph_from_adjacency(), network_graph_from_igraph(), network_graph_from_place(), network_coordinates().

Examples

nodes <- data.frame(name = c("a", "b", "c"), x = c(0, 100, 200), y = 0)
edges <- data.frame(from = c("a", "b"), to = c("b", "c"), length = c(100, 100))
net <- network_graph(edges, nodes)
net
node_distance(net, "a", "c")

Build a network from an adjacency matrix

Description

Each nonzero entry A[i, j] becomes an edge with weight (the entry) and length (the straight-line distance between the two nodes).

Usage

network_graph_from_adjacency(
  adjacency,
  coords,
  nodes = NULL,
  directed = FALSE,
  metric = "weight",
  ...
)

Arguments

Value

A network_graph().

Examples

A <- rbind(c(0, 2, 0), c(2, 0, 3), c(0, 3, 0))
net <- network_graph_from_adjacency(A, rbind(c(0, 0), c(1, 0), c(2, 0)))
node_distance(net, 1, 3)

Build a network from an igraph graph

Description

Vertices must carry projected coordinates; vertex names (if any) become node ids and edge attributes become edge columns.

Usage

network_graph_from_igraph(g, metric = "weight", x = "x", y = "y", ...)

Arguments

Value

A network_graph().

Examples

g <- igraph::make_ring(4)
igraph::V(g)$x <- c(0, 1, 1, 0)
igraph::V(g)$y <- c(0, 0, 1, 1)
igraph::E(g)$weight <- 1
network_graph_from_igraph(g)

Download an OpenStreetMap street network

Description

Downloads streets with dodgr (which uses osmdata) and projects them to the local UTM zone with sf. If a package is missing or the download fails and offline_fallback is TRUE, the bundled mock Midtown network is returned with a warning, so scripts keep working offline.

Usage

network_graph_from_place(
  query = NULL,
  bbox = NULL,
  network_type = "walk",
  metric = "length",
  offline_fallback = TRUE,
  dist = 1000,
  min_area = 250000,
  ...
)

Arguments

Value

A network_graph() with length (m), highway, and travel_time (s) edge columns.

Examples

net <- suppressWarnings(network_graph_from_place("Midtown Manhattan, New York"))
net

Network distances between observations

Description

Network distances between observations

Usage

pairwise_distances(coords, idx, cols = NULL, cutoff = Inf)

Arguments

Value

A ⁠length(idx) x length(cols)⁠ matrix of network distances (including both snapping depths); an observation is at distance 0 from itself.

Examples

city <- load_mock_midtown()
coords <- network_coordinates(city$network(), city$boundary, city$obs, n_eval = 5)
round(pairwise_distances(coords, 1:4), 1)

Project longitude/latitude to a planar coordinate system

Description

Project longitude/latitude to a planar coordinate system

Usage

project_lonlat(lon, lat, to_crs)

Arguments

Value

An ⁠n x 2⁠ matrix of projected coordinates. Requires sf.

Examples

project_lonlat(-73.98, 40.76, "EPSG:32618")

Bandwidth selection for network boundary discontinuity designs

Description

Selects MSE- or coverage-error-optimal bandwidths (in metric units) at each evaluation point. A port of rd2d's direct plug-in selector (rdbw2d) to network coordinates: a rule-of-thumb pilot, a separate pilot bandwidth for the bias, regularisation (scaleregul) and a higher-order bias term (scalebiascrct).

Usage

rdbwnet(
  y,
  coords,
  p = 1,
  deriv = c(0, 0),
  tangvec = NULL,
  covs = NULL,
  kernel = "triangular",
  bwselect = "mserd",
  vce = "hc1",
  cluster = NULL,
  fuzzy = NULL,
  weights = NULL,
  bwcheck = NULL,
  scaleregul = 1,
  scalebiascrct = 1
)

Arguments

Details

deriv/tangvec target a derivative jump and covs are partialled out (with coefficients common to both sides, estimated at the pilot bandwidth), as in rd2d. The ⁠cer*⁠ variants shrink the MSE-optimal bandwidth by rd2d's coverage-error rate factor ⁠n^(1/(2p+4) - 1/(p+4))⁠. Fuzzy designs use the linearised outcome y / fs - itt / fs^2 * D, as rd2d does.

Value

A data frame with one row per evaluation point: the control/treated bandwidths h0/h1 (and h when they are equal), the pilots dn, bn0, bn1, the MSE constants (V, B, Reg1, Reg2 per side, suffixed 0 for control and 1 for treated), the bounds hmin0/hmin1/hmax0/hmax1, local sample sizes n0/n1, and a status.

References

Cattaneo, M. D., Titiunik, R. and Yu, R. R. (2026). rd2d: Causal Inference in Boundary Discontinuity Designs. arXiv:2505.07989.

Examples

city <- load_mock_midtown()
coords <- network_coordinates(city$network(), city$boundary, city$obs, n_eval = 7)
bw <- rdbwnet(city$obs$outcome, coords)
bw[, c("arc", "h0", "h1", "n0", "n1", "status")]

Boundary discontinuity estimation on a network

Description

Estimates the boundary average treatment effect curve (BATEC) at each evaluation point along the boundary, with robust bias-corrected inference and a uniform confidence band. This carries rd2d's location-based estimator over to network coordinates: ⁠(s, o(b))⁠ replace ⁠(X1 - b1, X2 - b2)⁠ and the network distance d(b) replaces the Euclidean distance in the kernel.

Usage

rdnet(
  y,
  coords,
  h = NULL,
  p = 1,
  q = NULL,
  deriv = c(0, 0),
  tangvec = NULL,
  covs = NULL,
  kernel = "triangular",
  bwselect = "mserd",
  vce = "hc1",
  cluster = NULL,
  conley_cutoff = NULL,
  conley_distance = "network",
  conley_resid_scale = 3,
  conley_resid_order = NULL,
  fuzzy = NULL,
  weights = NULL,
  pooled = FALSE,
  level = 95,
  cbands = TRUE,
  repp = 2000,
  min_obs = NULL,
  scaleregul = 3,
  scalebiascrct = 1,
  seed = NULL
)

Arguments

Value

An object of class rdnet, a list with

table

one row per evaluation point: eval, arc, node_arc, bandwidths h0, h1 (and h when equal), local sample sizes n0, n1, the order-p estimate tau and its standard error se, the bias-corrected tau_bc with robust se_rb, the robust interval ci_lo/ci_hi, pvalue, a status ("ok", "masked", "insufficient", "singular", "no bandwidth", "weak first stage"), and optionally the uniform band cb_lo/cb_hi. Fuzzy designs add itt, fs and fs_se.

cov

the ⁠J x J⁠ covariance of tau_bc across evaluation points (rd2d's params_cov), NA for points not estimated; its diagonal is se_rb^2.

crit

the uniform critical value implied by cov.

bandwidths

the rdbwnet() output when h was selected.

plus p, q, kernel, vce, level, metric, fuzzy and coords. Use wbate() and lbate() for aggregate effects, summary(), plot(), coef(), vcov() and confint() for the usual summaries.

References

Cattaneo, M. D., Titiunik, R. and Yu, R. R. (2026). rd2d: Causal Inference in Boundary Discontinuity Designs. arXiv:2505.07989.

Calonico, S., Cattaneo, M. D. and Titiunik, R. (2014). Robust Nonparametric Confidence Intervals for Regression-Discontinuity Designs. Econometrica 82(6): 2295-2326.

Conley, T. G. (1999). GMM Estimation with Cross Sectional Dependence. Journal of Econometrics 92(1): 1-45.

Examples

city <- load_mock_midtown()
coords <- network_coordinates(city$network(), city$boundary, city$obs, n_eval = 9)
res <- rdnet(city$obs$outcome, coords, weights = city$obs$far, seed = 1)
res
ok <- res$table$status == "ok"
wbate(res, as.numeric(ok))
plot(res)

Summaries of rdnet results

Description

Summaries of rdnet results

Usage

## S3 method for class 'rdnet'
summary(object, wbate = NULL, lbate = FALSE, ...)

## S3 method for class 'summary.rdnet'
print(x, digits = 3, ...)

## S3 method for class 'rdnet'
print(x, ...)

## S3 method for class 'rdnet'
coef(object, type = c("tau_bc", "tau"), ...)

## S3 method for class 'rdnet'
vcov(object, ...)

## S3 method for class 'rdnet'
confint(object, parm, level = object$level/100, ...)

## S3 method for class 'rdnet'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

## S3 method for class 'rdnet'
plot(x, band = TRUE, true_tau = NULL, ...)

Arguments

Value

summary() returns an object of class summary.rdnet; coef() the estimates by evaluation point; vcov() the joint covariance x$cov; confint() robust intervals ⁠tau_bc -/+ z se_rb⁠; as.data.frame() the result table; plot() draws the BATEC curve with pointwise robust intervals and the uniform band and returns x invisibly.

Rectangle polygon

Description

A convenience constructor for an axis-aligned rectangle, usable wherever a polygon is accepted (treated regions, masks).

Usage

rect_polygon(xmin, ymin, xmax, ymax)

Arguments

Value

A 5 x 2 matrix of ring vertices (closed).

Examples

rect_polygon(0, 0, 10, 5)

Attach points to a network

Description

Each point reaches node u at cost cost_u and node v at cost cost_v along its street segment, after an off-network depth (Euclidean units). Points snapped to a node have u == v and zero edge costs.

Usage

snap_points(net, xy, allowed = NULL, mode = "edge")

Arguments

Value

A list with integer vectors u, v (node indices) and numeric vectors cost_u, cost_v, depth.

Examples

nodes <- data.frame(name = 1:2, x = c(0, 100), y = 0)
net <- network_graph(data.frame(from = 1, to = 2, length = 100), nodes)
snap_points(net, rbind(c(30, 10)))

Aggregate boundary treatment effects

Description

wbate() is the weighted boundary average treatment effect ⁠w' tau⁠ (rd2d's WBATE): weights has one entry per evaluation point and is normalised to sum to one; points that were not estimated must get weight 0. Its standard error ⁠sqrt(w' Sigma w)⁠ uses the joint covariance x$cov, so it accounts for the correlation between nearby points.

Usage

wbate(x, weights)

lbate(x)

Arguments

Details

lbate() is the largest boundary average treatment effect ⁠max_j tau(b_j)⁠ (rd2d's LBATE), with interval ⁠[max_j (tau_bc - crit se_rb), max_j (tau_bc + crit se_rb)]⁠ over the estimated points, crit the uniform critical value, so it inherits the uniform band's joint coverage.

Value

A named numeric vector: tau, tau_bc, se_rb, ci_lo, ci_hi, pvalue (the bias-corrected estimate drives the interval and p-value; se_rb and pvalue are NA for LBATE).

Examples

city <- load_mock_midtown()
coords <- network_coordinates(city$network(), city$boundary, city$obs, n_eval = 9)
res <- rdnet(city$obs$outcome, coords, h = 600, seed = 1)
ok <- res$table$status == "ok"
wbate(res, as.numeric(ok))
lbate(res)

Split edges where they cross the boundary

Description

Inserts a node wherever an edge crosses the boundary line, so that every path between the two sides passes through a node on the boundary. Columns listed in extensive are divided in proportion to the split; all others are copied. network_coordinates() does this automatically.

Usage

with_boundary_crossings(net, b, extensive = c("length", "travel_time"))

Arguments

Value

A network (the same one when nothing needs splitting), with attribute "n_crossings_inserted".

Examples

nodes <- data.frame(name = 1:2, x = 0, y = c(0, 100))
net <- network_graph(data.frame(from = 1, to = 2, length = 100), nodes)
b <- boundary(rbind(c(-10, 40), c(10, 40)), treated_region = rect_polygon(-50, -50, 50, 40))
split <- with_boundary_crossings(net, b)
split$edges