Codex/a1 r bindings merge main

.gitignore

@@ -10,4 +10,16 @@ target
 .hypothesis/
 .pytest_cache/
 .ruff_cache/
-*.proptest-regressions
+__pycache__/
+*.py[cod]
+*.proptest-regressions
+
+# R build/check artifacts
+*.Rcheck/
+*.tar.gz
+.Rhistory
+.RData
+.Rproj.user/
+
+# Serena MCP
+.serena/

CHANGELOG.md

@@ -11,10 +11,13 @@ and this project follows [Semantic Versioning](https://semver.org/).
 
 - **Locality sort:** `Design` construction reorders observations by the highest-cardinality factor when unsorted, copying them once into an internal sorted store (the caller's store is never mutated; the `Store` trait stays read-only). Transparent — results return in caller row order.
 - Coalesced scatter for large sorted factors: one atomic add per equal-level run per chunk instead of one per row.
+- `withinr` preconditioner handles now expose `variant` and `build_time_seconds` metadata for cache compatibility checks and diagnostics.
+- `withinr` manual tests now cover diagonal/additive/off correctness parity, prebuilt preconditioner reuse, and sorted-vs-unsorted caller-order equivalence.
 
 ### Changed
 
 - Category views are borrowed only when the dominant factor is already sorted; otherwise the columns are copied once for the locality sort. The reorder changes summation order, so unsorted-input results match 0.2.0 within solver tolerance, not bitwise.
+- `withinr` development builds patch `within` to the workspace crate, while offline builds patch it to the vendored local crate sources.
 
 ## [0.2.0] - 2026-06-04
 

Cargo.toml

@@ -3,6 +3,7 @@ members = [
     "crates/schwarz-precond",
     "crates/within",
     "crates/within-py",
+    "crates/within-r",
 ]
 resolver = "2"
 

README.md

@@ -43,69 +43,89 @@ result = solve(fe, y, weights=np.ones(n))
 result = solve(fe, y, preconditioner=PreconditionerConfig.Diagonal)
 ```
 
-### FWL regression example
+## R quickstart
 
-```python
-beta_true = np.array([1.0, -2.0, 0.5])
-X = np.random.randn(n, 3)
-y = X @ beta_true + np.random.randn(n)
-
-result = solve_batch(fe, np.column_stack([y, X]))
-y_tilde, X_tilde = result.demeaned[:, 0], result.demeaned[:, 1:]
-beta_hat = np.linalg.lstsq(X_tilde, y_tilde, rcond=None)[0]
-print(np.round(beta_hat, 4))  # [ 0.9982 -2.006   0.5005]
+Requires R and a Rust toolchain (`cargo` on `PATH`).
+
+From the repository root, use `devtools` to install R dependencies and build the
+package:
+
+```r
+install.packages("devtools")
+Sys.setenv(NOT_CRAN = "true")
+devtools::install_deps("withinr/", dependencies = TRUE)
+devtools::load_all("withinr/")
 ```
 
-## Python API
+Example (FWL with two-way fixed effects):
 
-### High-level functions
+```r
+set.seed(42)
+n <- 1000
+n_firms <- 50L
+n_years <- 20L
 
-| Function | Description |
-|---|---|
-| `solve(categories, y, options?, weights?, preconditioner?)` | Solve a single right-hand side. Returns `SolveResult`. |
-| `solve_batch(categories, Y, options?, weights?, preconditioner?)` | Solve multiple RHS vectors in parallel. `Y` has shape `(n_obs, k)`. Returns `BatchSolveResult`. |
+# 1-based fixed-effect ids in R
+firm <- rep(seq_len(n_firms), each = n_years)
+year <- rep(seq_len(n_years), times = n_firms)
+categories <- cbind(firm, year)
 
-`categories` is a 2-D `uint32` array of shape `(n_obs, n_factors)`. A `UserWarning` is emitted when a C-contiguous array is passed — use `np.asfortranarray(categories)` for best performance.
+beta <- 1.5
+firm_fe <- rnorm(n_firms, sd = 3)[firm]
+year_fe <- rnorm(n_years, sd = 1)[year]
+x <- rnorm(n) + 0.3 * firm_fe
+y <- beta * x + firm_fe + year_fe + rnorm(n, sd = 0.5)
 
-### Persistent solver
+res <- withinr::solve_batch(categories, cbind(y, x))
+y_tilde <- res$demeaned[, 1]
+x_tilde <- res$demeaned[, 2]
+beta_hat <- sum(x_tilde * y_tilde) / sum(x_tilde^2)
 
-For repeated solves with the same design matrix, `Solver` builds the preconditioner once and reuses it.
+print(beta_hat)
+print(res$converged)
+```
 
-```python
-from within import Solver
+| Function | Description |
+|---|---|
+| `solve(categories, y, options?, weights?, preconditioner?)` | Solve a single right-hand side. Returns a list shaped like `SolveResult`. |
+| `solve_batch(categories, Y, options?, weights?, preconditioner?)` | Solve multiple RHS vectors in parallel. `Y` has shape `(n_obs, k)`. |
+
+For repeated solves with the same design matrix, `Solver` builds the preconditioner once and reuses it. In R, the solver is an environment with methods.
 
-solver = Solver(fe)
-r = solver.solve(y)                            # reuses preconditioner
-r = solver.solve_batch(np.column_stack([y, X]))
+```r
+solver <- withinr::Solver(categories)
+r <- solver$solve(y)
+r <- solver$solve_batch(cbind(y, x))
 
-precond = solver.preconditioner                # picklable property
-solver2 = Solver(fe, preconditioner=precond)   # skip re-factorization
+precond <- solver$preconditioner()
+payload <- precond$serialize()
+solver2 <- withinr::Solver(categories, preconditioner = withinr::Preconditioner(payload))
 ```
 
 | Property / Method | Description |
 |---|---|
 | `Solver(categories, weights?, preconditioner?)` | Build solver. Factorizes the preconditioner at construction. |
-| `.solve(y, options?)` | Solve a single RHS with the given LSMR tuning. Returns `SolveResult`. |
-| `.solve_batch(Y, options?)` | Solve multiple RHS columns in parallel. Returns `BatchSolveResult`. |
-| `.preconditioner` | Return the built `Preconditioner` (picklable), or `None`. Reuse via `Solver(fe, preconditioner=p)`. |
+| `$solve(y, options?)` | Solve a single RHS with the given LSMR tuning. |
+| `$solve_batch(Y, options?)` | Solve multiple RHS columns in parallel. |
+| `$preconditioner()` | Return the built `Preconditioner`, or `NULL`. Reuse via `Solver(categories, preconditioner=p)`. |
 
 
 ### Solver configuration
 
 | Class | Description |
 |---|---|
-| `LsmrOptions(tol=1e-8, maxiter=1000, local_size=None)` | Modified LSMR. `local_size` enables windowed reorthogonalization. |
+| `LsmrOptions(tol=1e-8, maxiter=1000, local_size=None)` / `LsmrOptions(tol = 1e-8, maxiter = 1000L, local_size = NULL)` | Modified LSMR. `local_size` enables windowed reorthogonalization. |
 
 ### Preconditioner (5-form Union)
 
 The `preconditioner` argument accepts any of:
 
 | Form | Meaning |
 |---|---|
-| `None` (default) | Library default — Additive Schwarz with sensible defaults. |
-| `PreconditionerConfig.Off` | Explicit identity — solve unpreconditioned. |
-| `PreconditionerConfig.Additive` | Additive Schwarz shortcut, equivalent to `None`. |
-| `PreconditionerConfig.Diagonal` | Diagonal/Jacobi preconditioner using `diag(D^T W D)^{-1}`. |
+| `None` / `NULL` (default) | Library default — Additive Schwarz with sensible defaults. |
+| `PreconditionerConfig.Off` / `PreconditionerConfig$Off` | Explicit identity — solve unpreconditioned. |
+| `PreconditionerConfig.Additive` / `PreconditionerConfig$Additive` | Additive Schwarz shortcut, equivalent to the default. |
+| `PreconditionerConfig.Diagonal` / `PreconditionerConfig$Diagonal` | Diagonal/Jacobi preconditioner using `diag(D^T W D)^{-1}`. |
 | `AdditiveSchwarz(local_solver?, reduction?)` | Tuned Schwarz config — import from `within.config`. |
 | `Preconditioner` instance | Reuse a previously-built preconditioner across solvers. |
 
@@ -114,7 +134,7 @@ The `preconditioner` argument accepts any of:
 | Class | Description |
 |---|---|
 | `LocalSolverConfig(approx_chol?, approx_schur?, dense_threshold=24)` | Schur reduction + approximate Cholesky. Omit `approx_schur` for the library-default approximate variant; pass `approx_schur=None` to request an exact Schur (slower, used for validation). |
-| `ApproxCholConfig(seed=0, split=1)` | Approximate Cholesky parameters. |
+| `ApproxCholConfig(seed=0, split_merge=None)` | Approximate Cholesky parameters. |
 | `ApproxSchurConfig(seed=0, split=1)` | Approximate Schur complement sampling parameters. |
 | `ReductionStrategy` enum | `Auto` (default), `AtomicScatter`, `ParallelReduction`. |
 
@@ -194,7 +214,9 @@ crates/
   schwarz-precond/   Generic domain decomposition library (traits, solvers, Schwarz preconditioners)
   within/            Core fixed-effects solver (observation stores, domains, operators, orchestration)
   within-py/         PyO3 bridge (cdylib → within._within)
+  within-r/          Workspace mirror for the extendr bridge
 python/within/       Python package re-exporting the Rust extension
+withinr/             R package using the published within 0.2.0 crate
 benchmarks/          Python benchmark framework
 ```
 

crates/within-r/Cargo.toml

@@ -0,0 +1,13 @@
+[package]
+name = "within-r"
+version = "0.1.0"
+edition = "2021"
+
+[lib]
+crate-type = ["staticlib"]
+
+[dependencies]
+within = { path = "../within" }
+extendr-api = "0.8"
+ndarray = "0.16"
+postcard = { version = "1.1", features = ["use-std"] }

crates/within-r/src/lib.rs

@@ -0,0 +1 @@
+include!("../../../withinr/src/rust/src/lib.rs");

withinr/.Rbuildignore

@@ -0,0 +1,11 @@
+^.*\.Rproj$
+^\.Rproj\.user$
+^src/rust/target
+^src/rust/vendor$
+^src/vendor$
+^src/\.cargo
+^src/.*\.o$
+^src/.*\.so$
+^src/.*\.dll$
+^test_bindings\.R$
+^benchmarks$

withinr/.gitignore

@@ -0,0 +1,7 @@
+src/.cargo/
+src/rust/target/
+src/vendor/
+src/*.o
+src/*.so
+src/*.dll
+src/withinr.lib

withinr/DESCRIPTION

@@ -0,0 +1,19 @@
+Package: withinr
+Title: High-Performance Fixed Effects Solver
+Version: 0.1.0
+Authors@R: c(
+    person("Alexander", "Fischer", role = c("aut", "cre"),
+           email = "alexander-fischer1801@t-online.de"),
+    person("Kristof", "Schroeder", role = "aut"))
+Author: Alexander Fischer [aut, cre], Kristof Schroeder [aut]
+Maintainer: Alexander Fischer <alexander-fischer1801@t-online.de>
+Description: Fast modified LSMR solvers with Schwarz and diagonal
+    preconditioners for absorbing high-dimensional fixed effects in panel
+    data regressions. The computational core is written in Rust via version
+    0.2.0 of the 'within' crate and accessed through 'extendr'.
+License: MIT + file LICENSE
+Encoding: UTF-8
+Roxygen: list(markdown = TRUE)
+RoxygenNote: 7.3.3
+SystemRequirements: Rust tool chain w/ cargo, rustc
+Config/rextendr/version: 0.5.0

withinr/LICENSE

@@ -0,0 +1,2 @@
+YEAR: 2025
+COPYRIGHT HOLDER: Alexander Fischer, Kristof Schröder

withinr/NAMESPACE

@@ -0,0 +1,17 @@
+# Generated by roxygen2: do not edit by hand
+
+S3method(print,within_preconditioner)
+S3method(print,within_solver)
+export(AdditiveSchwarz)
+export(ApproxCholConfig)
+export(ApproxSchurConfig)
+export(LocalSolverConfig)
+export(LsmrOptions)
+export(Preconditioner)
+export(PreconditionerConfig)
+export(ReductionStrategy)
+export(Solver)
+export(lsmr_options)
+export(solve)
+export(solve_batch)
+useDynLib(withinr, .registration = TRUE)

withinr/R/extendr-wrappers.R

@@ -0,0 +1,83 @@
+# Generated by extendr: Do not edit by hand
+#
+# This file was created with the following call:
+#   .Call("wrap__make_withinr_wrappers", use_symbols = TRUE, package_name = "withinr")
+
+#' @usage NULL
+#' @useDynLib withinr, .registration = TRUE
+NULL
+
+# Internal .Call wrapper.
+solve_impl <- function(categories, y, weights, tol, maxiter, local_size, preconditioner) {
+  .Call("wrap__solve_impl", categories, y, weights, tol, maxiter, local_size, preconditioner)
+}
+
+# Internal .Call wrapper.
+solve_batch_impl <- function(categories, y_matrix, weights, tol, maxiter, local_size, preconditioner) {
+  .Call("wrap__solve_batch_impl", categories, y_matrix, weights, tol, maxiter, local_size, preconditioner)
+}
+
+# Internal .Call wrapper.
+solver_new_impl <- function(categories, weights, preconditioner) {
+  .Call("wrap__solver_new_impl", categories, weights, preconditioner)
+}
+
+# Internal .Call wrapper.
+solver_solve_impl <- function(solver, y, tol, maxiter, local_size) {
+  .Call("wrap__solver_solve_impl", solver, y, tol, maxiter, local_size)
+}
+
+# Internal .Call wrapper.
+solver_solve_batch_impl <- function(solver, y_matrix, tol, maxiter, local_size) {
+  .Call("wrap__solver_solve_batch_impl", solver, y_matrix, tol, maxiter, local_size)
+}
+
+# Internal .Call wrapper.
+solver_preconditioner_impl <- function(solver) {
+  .Call("wrap__solver_preconditioner_impl", solver)
+}
+
+# Internal .Call wrapper.
+solver_n_dofs_impl <- function(solver) {
+  .Call("wrap__solver_n_dofs_impl", solver)
+}
+
+# Internal .Call wrapper.
+solver_n_obs_impl <- function(solver) {
+  .Call("wrap__solver_n_obs_impl", solver)
+}
+
+# Internal .Call wrapper.
+preconditioner_apply_impl <- function(preconditioner, x) {
+  .Call("wrap__preconditioner_apply_impl", preconditioner, x)
+}
+
+# Internal .Call wrapper.
+preconditioner_nrows_impl <- function(preconditioner) {
+  .Call("wrap__preconditioner_nrows_impl", preconditioner)
+}
+
+# Internal .Call wrapper.
+preconditioner_ncols_impl <- function(preconditioner) {
+  .Call("wrap__preconditioner_ncols_impl", preconditioner)
+}
+
+# Internal .Call wrapper.
+preconditioner_variant_impl <- function(preconditioner) {
+  .Call("wrap__preconditioner_variant_impl", preconditioner)
+}
+
+# Internal .Call wrapper.
+preconditioner_build_time_seconds_impl <- function(preconditioner) {
+  .Call("wrap__preconditioner_build_time_seconds_impl", preconditioner)
+}
+
+# Internal .Call wrapper.
+preconditioner_serialize_impl <- function(preconditioner) {
+  .Call("wrap__preconditioner_serialize_impl", preconditioner)
+}
+
+# Internal .Call wrapper.
+preconditioner_deserialize_impl <- function(data) {
+  .Call("wrap__preconditioner_deserialize_impl", data)
+}