Quick Start

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Quick Start

Gilles Colling

2026-11-02

Installation

install.packages("tulpaMesh")            # CRAN

install.packages("pak")                  # development version
pak::pak("gcol33/tulpaMesh")

Your first mesh

tulpaMesh takes point coordinates and returns a triangulated mesh with FEM matrices ready for SPDE models.

set.seed(42)
coords <- cbind(x = runif(100), y = runif(100))
mesh <- tulpa_mesh(coords)
mesh
#> tulpa_mesh:
#>   Vertices:   138 
#>   Triangles:  236 
#>   Edges:      373 
#>   ( 100  input + 38  generated)

The mesh extends slightly beyond the convex hull of your points (controlled by extend). Plot it:

plot(mesh, vertex_col = "steelblue", main = "Basic mesh")

Controlling mesh density

Use max_edge to add refinement points. The mesh generator places a hexagonal lattice of points at this spacing, producing near-equilateral triangles.

mesh_fine <- tulpa_mesh(coords, max_edge = 0.08)
mesh_fine
#> tulpa_mesh:
#>   Vertices:   336 
#>   Triangles:  595 
#>   Edges:      930 
#>   ( 100  input + 236  generated)
plot(mesh_fine, main = "Refined mesh (max_edge = 0.08)")

Getting FEM matrices

fem_matrices() returns the three sparse matrices needed for SPDE models:

C: mass matrix (consistent, symmetric positive definite)
G: stiffness matrix (symmetric, zero row sums)
A: projection matrix mapping mesh vertices to observation locations

fem <- fem_matrices(mesh_fine, obs_coords = coords)
dim(fem$C)
#> [1] 336 336
dim(fem$A)
#> [1] 100 336

# Verify key properties
all(Matrix::diag(fem$C) > 0)        # positive diagonal
#> [1] TRUE
max(abs(Matrix::rowSums(fem$G)))     # row sums ~ 0
#> [1] 7.882583e-15
range(Matrix::rowSums(fem$A))        # row sums = 1
#> [1] 1 1

For the SPDE Q-builder, you typically need the lumped (diagonal) mass matrix:

fem_l <- fem_matrices(mesh_fine, obs_coords = coords, lumped = TRUE)
Matrix::isDiagonal(fem_l$C0)
#> [1] TRUE

Using a formula interface

If your coordinates live in a data.frame, use a formula:

df <- data.frame(lon = runif(50), lat = runif(50), y = rnorm(50))
mesh_f <- tulpa_mesh(~ lon + lat, data = df)
mesh_f
#> tulpa_mesh:
#>   Vertices:   82 
#>   Triangles:  130 
#>   Edges:      211 
#>   ( 50  input + 32  generated)

Mesh quality

Check triangle quality with mesh_quality() and mesh_summary():

mesh_summary(mesh_fine)
#> Mesh quality summary:
#>   Triangles:      595
#>   Min angle:      1.4 / 40.2 / 60.0 (min / median / max)
#>   Max angle:      60.0 / 80.1 / 149.5
#>   Aspect ratio:   1.00 / 1.52 / 40.75
#>   Area:           0.0000 / 0.0023 / 0.0090
#>   WARNING: 16 triangles with min angle < 10 degrees
#>   WARNING: 8 triangles with min angle < 5 degrees (slivers)

Color triangles by minimum angle:

plot(mesh_fine, color = "quality", main = "Colored by minimum angle")

Ruppert refinement

For guaranteed minimum angles, use min_angle:

mesh_r <- tulpa_mesh(coords, min_angle = 25, max_edge = 0.15)
mesh_summary(mesh_r)
#> Mesh quality summary:
#>   Triangles:      697
#>   Min angle:      25.2 / 39.3 / 59.5 (min / median / max)
#>   Max angle:      60.6 / 76.9 / 128.4
#>   Aspect ratio:   1.02 / 1.55 / 2.32
#>   Area:           0.0000 / 0.0015 / 0.0159

Next steps

Spatial Workflows – boundary constraints, barrier models, sf integration
Spherical and Temporal Meshes – global meshes, space-time, metric graphs

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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