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spconform

R-CMD-check License: GPL v3 DOI

Conformal Prediction for Spatially and Spatio-Temporally Dependent Data in R

spconform provides distribution-free prediction intervals with finite-sample coverage properties for spatial and spatio-temporal data. It relaxes the exchangeability assumption of standard conformal prediction by using spatial-distance and graph-neighbourhood kernel weights, offering a unified framework for both geostatistical (point-referenced) and areal (lattice) data structures.

Why spconform?

Standard conformal prediction assumes exchangeable data — an assumption routinely violated in spatial settings where nearby observations are more similar than distant ones. Existing R packages address either purely temporal dependence (conformalForecast, AdaptiveConformal) or i.i.d./exchangeable data (conformalInference, conformalClassification, cfcausal), but none provides a documented, unit-tested, unified solution for spatial data on CRAN.

Feature conformalInference conformalForecast scp (GitHub) geoconformal (Python) spconform
Language R R R Python R
Geostatistical (point-ref.) — — ✓ ✓ ✓
Areal (lattice) — — — — ✓
Spatio-temporal — — (temp. only) — — ✓ (opt.)
Model-agnostic ✓ ✓ ✓ ✓ ✓
Unit-tested / CRAN-ready ✓ ✓ — — ✓

spconform is, to our knowledge, the first R package to offer conformal prediction spanning both major spatial data structures with optional spatio-temporal extension.

> Status: spconform has passed R CMD check --as-cran with 0 errors, > 0 warnings, and 0 notes on Windows 11 (R 4.6.1), win-builder (R-devel), > and R-hub v2 (Linux, Windows, macOS, donttest). The package is CRAN-ready > and will be submitted to CRAN as soon as the submission form re-opens. > A permanent, citable snapshot of version 0.1.0 is archived on Zenodo > (DOI above). The accompanying manuscript is currently in preparation > for submission to the Journal of Statistical Software.

Installation 

# Install the development version from GitHub
remotes::install_github("amjed-droid/spconform")

# Once accepted on CRAN:
# install.packages("spconform")

**Dependencies: The package imports only stats (base R). Suggested packages (‘sp’, ‘knitr’, ‘rmarkdown’) are used to build and run the vignette; ‘mgcv’, ‘ranger’, and ‘bmstdr’ are only needed to reproduce the extended examples shown in the accompanying paper and are not required for core package functionality.

Quick start

1. Geostatistical data (point-referenced) 

library(spconform)
library(sp)

data(meuse)
s <- as.matrix(meuse[, c("x", "y")])
y <- log(meuse$zinc)

# Any user-supplied point predictor
pred_fun <- function(s_train, y_train, s_new) {
  fit <- lm(y_train ~ s_train[, 1] + s_train[, 2] +
            I(s_train[, 1]^2) + I(s_train[, 2]^2))
  cbind(1, s_new[, 1], s_new[, 2],
        s_new[, 1]^2, s_new[, 2]^2) %*% coef(fit)
}

# 90% locally weighted conformal intervals
set.seed(123)
idx <- sample(nrow(s), floor(0.7 * nrow(s)))
out <- scp_geostatistical(s[idx, ], y[idx], s[-idx, ], pred_fun, alpha = 0.1)

print(out)
#> <spconform> geostatistical conformal prediction
#> Target coverage: 90.0%
#> Number of prediction points: 47

coverage_report(out, y[-idx])
#> $coverage
#> [1] 0.957
#> $mean_width
#> [1] 2.21

2. Comprehensive Spatial Diagnostics

‘spconform’ includes a multi-panel diagnostic suite (diagnose()) to evaluate marginal coverage, conditional coverage across spatial strata, boundary effects, and the distribution of nonconformity scores: # Run diagnostics and produce publication-quality multi-panel plot diag <- diagnose(out, y_true = y[-idx], s_test = s[-idx], plot = TRUE)

View textual diagnostic summary

print(diag) === spconform Diagnostic Report ===

Marginal coverage: Empirical: 0.9574 (nominal: 0.9) Mean width: 2.2105 n = 47 , covered = 45

Conditional coverage by spatial bin: Q1-1: 1.0000 (n=7, width=3.529) Q1-2: 1.0000 (n=6, width=2.975) Q4-4: 0.8889 (n=9, width=1.930)

Boundary effect: Near boundary: 0.9583 (n=24) Far from boundary: 0.9565 (n=23) ### 3. Areal / lattice data

# Aggregate Meuse to a 6x6 grid (21 occupied cells)
xbreaks <- seq(min(meuse$x), max(meuse$x), length.out = 7)
ybreaks <- seq(min(meuse$y), max(meuse$y), length.out = 7)
meuse$cell_x  <- cut(meuse$x, xbreaks, include.lowest = TRUE, labels = FALSE)
meuse$cell_y  <- cut(meuse$y, ybreaks, include.lowest = TRUE, labels = FALSE)
meuse$cell_id <- (meuse$cell_y - 1) * 6 + meuse$cell_x

agg <- aggregate(log(zinc) ~ cell_id, data = meuse, FUN = mean)
names(agg) <- c("cell_id", "y")
cell_coords <- unique(meuse[, c("cell_id", "cell_x", "cell_y")])
agg <- merge(agg, cell_coords, by = "cell_id")
agg <- agg[order(agg$cell_id), ]

# Build adjacency matrix (Queen contiguity)
n_cells <- nrow(agg)
adj <- matrix(0, n_cells, n_cells)
for (i in 1:n_cells) {
  for (j in 1:n_cells) {
    if (i != j) {
      dx <- abs(agg$cell_x[i] - agg$cell_x[j])
      dy <- abs(agg$cell_y[i] - agg$cell_y[j])
      if (dx <= 1 && dy <= 1) adj[i, j] <- 1
    }
  }
}

# 80% neighbourhood-weighted conformal intervals
out_areal <- scp_areal(agg$y, adjacency = adj, alpha = 0.2, decay = 0.5)
coverage_report(out_areal, agg$y)
#> $coverage
#> [1] 0.81
#> $mean_width
#> [1] 1.79

4. Spatio-temporal data 

library(mgcv)
library(bmstdr)

data("nysptime")
df <- nysptime[complete.cases(nysptime[, c("utmx", "utmy", "y8hrmax", "Day", "Month")]), ]
df$day_idx <- ifelse(df$Month == 7, df$Day, 31 + df$Day)

s <- as.matrix(df[, c("utmx", "utmy")])
t <- df$day_idx
s_3d <- cbind(s, t)
y <- df$y8hrmax

# Spatio-temporal GAM predictor
pred_fun_st <- function(s_train, y_train, s_new) {
  train_df <- data.frame(x = s_train[, 1], y = s_train[, 2],
                         day = s_train[, 3], z = y_train)
  fit <- gam(z ~ te(x, y, day, k = c(8, 8, 4)), data = train_df)
  new_df <- data.frame(x = s_new[, 1], y = s_new[, 2], day = s_new[, 3])
  as.numeric(predict(fit, newdata = new_df))
}

set.seed(123)
n <- nrow(s_3d)
train_idx <- sample(n, floor(0.7 * n))
out_st <- scp_geostatistical(
  s_train = s_3d[train_idx, ],
  y_train = y[train_idx],
  s0 = s_3d[-train_idx, ],
  pred_fun = pred_fun_st,
  t_train = t[train_idx],
  t0 = t[-train_idx],
  temporal_bandwidth = 5,
  alpha = 0.1,
  split = 0.5
)

coverage_report(out_st, y[-train_idx])
#> $coverage
#> [1] 0.909
#> $mean_width
#> [1] 41.7

Quality Assurance

spconform has been rigorously tested across all major platforms to ensure CRAN-readiness:

Platform R Version Status
Linux (Ubuntu) R-devel ✅ Pass
macOS (Sequoia) R-devel ✅ Pass
Windows R-devel ✅ Pass

The package passes R CMD check --as-cran with 0 errors, 0 warnings, and 0 notes on all tested platforms. Continuous integration is monitored via GitHub Actions.

Key features

  • Model-agnostic: Works with any user-supplied point predictor (kriging, GAM, random forest, linear model, …).
  • Finite-sample coverage: Maintains coverage close to nominal level regardless of predictor misspecification (under local exchangeability).
  • Lightweight: Imports only stats; no heavy spatial-modelling dependencies.
  • Fully documented: S3 methods (print, summary, plot, coverage_report) included, plus a full introductory vignette.

Citation

If you use spconform in your research, please cite:

Jabbar, A. S. (2026). spconform: Conformal Prediction for Spatially and Spatio-Temporally Dependent Data in R (Version 0.1.0) [Computer software]. Zenodo. https://doi.org/10.5281/zenodo.21862025

A companion manuscript describing the package methodology is currently submitted to the Journal of Statistical Software and will be cited here upon acceptance.

citation("spconform")

Getting help

License

This package is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 3 of the License, or (at your option) any later version.