Introduction to pls4all

pls4all is the slim, PLS-focused distribution of the nirs4all-methods chemometrics engine — every method built on the shared PLS core (NIPALS, SIMPLS, kernel, …). The C++17 implementation is vendored and compiled from source at install time; no external system library is required.

Quick start (matrix API)

library(pls4all)

set.seed(42)
n <- 50
p <- 8
X <- matrix(rnorm(n * p), nrow = n, ncol = p)
beta <- c(2, -1, 0.5, rep(0, p - 3))
y <- matrix(as.numeric(X %*% beta + rnorm(n, sd = 0.1)), ncol = 1)

fit <- n4m_fit(X, y, algo = "pls_nipals", n_components = 3L)
preds <- n4m_predict(fit, X)

cat("RMSE:", sqrt(mean((preds - y)^2)), "\n")
#> RMSE: 0.08915837

Formula interface

The base-R formula API mirrors lm() and other standard regression modelling conventions.

df <- as.data.frame(X)
df$y <- as.numeric(y)
fit_form <- pls(y ~ ., data = df, ncomp = 3L)
class(fit_form)
#> [1] "n4m_fit" "pls_fit"

Version and ABI

The runtime version string embeds both the project semver and the stable C ABI version. Downstream consumers can assert ABI compatibility before calling lower-level entry points.

n4m_version()
#> [1] "0.99.0+abi.1.22.0"
n4m_abi_version()
#> [1]  1 22  0

Supported algorithms

n4m_fit() selects the solver via the algo argument. Recognised values include "pls_nipals" (default), "pls_simpls", "pls_svd", "pls_kernel_algorithm", "pls_wide_kernel", "pls_orthogonal_scores", "pls_power", "pls_randomized_svd", and "pcr_svd". The full algorithm taxonomy is documented in the package website.

fit_simpls <- n4m_fit(X, y, algo = "pls_simpls", n_components = 3L)
fit_svd    <- n4m_fit(X, y, algo = "pls_svd",    n_components = 3L)

# Same numerical answers up to algorithmic tolerance.
all.equal(n4m_predict(fit_simpls, X[1:5, ]),
          n4m_predict(fit_svd,    X[1:5, ]),
          tolerance = 1e-10)
#> [1] TRUE

Further reading

  • The n4m_method() entry point exposes the full method catalogue (sparse SIMPLS, CPPLS, weighted, robust, ridge, continuum, multi-block, GLM, MIR, PDS, DS, and others) with a unified parameter-list interface.
  • Variable-selection wrappers (spa_select, cars_select, variable_select_rank) return a ranked feature index suitable for downstream model retraining.
  • Diagnostics (pls_diagnostics_compute, approximate_press_compute, pls_monitoring_run) implement Hotelling T-squared, Q residuals, DModX, and process-monitoring alarms.

See the project repository and the cross-binding parity reports at https://github.com/GBeurier/nirs4all-methods/tree/main/docs/parity for the full feature matrix.