muscal
muscal is an R package for multiblock and matrix-based multivariate analysis.
It focuses on methods for integrating several tables measured on the same
observations, linked designs with partial overlap, and repeated
covariance/connectivity matrices.
The package includes classical "French school" methods such as Multiple Factor
Analysis (MFA) and STATIS-style workflows, along with newer extensions for
adaptive weighting, regularization, and repeated-matrix modeling. Most fitted
objects work naturally with the multivarious ecosystem for scoring,
projection, and preprocessing.
What to use when
mfa() for several data blocks measured on the same observations.ipca() when you want adaptive block weighting instead of fixed MFA
normalization.penalized_mfa() when you want MFA with loading-similarity penalties.anchored_mfa() for linked blocks that map back to a reference table but do
not share identical rows.response_aligned_mfa() when blocks have paired multivariate responses but
do not share exact response rows.aligned_rrr() as the pure shared reduced-rank regression baseline for the
same response-prediction setting.aligned_mfa() and aligned_mcca() for aligned multiblock integration and
correlation analysis.covstatis() for lists of covariance or connectivity matrices.distatis() for distance matrices on the same objects, including sorting
tasks converted with sorting_distances().bada() and bamfa() for barycentric and discriminant variants.bilinear_mixed() for repeated connectivity matrices with subject- and
design-level structure.design_mfa() for trialwise multivariate fields decomposed by named design
effects, with subject-aware weighting, coordinate-derived sparse alignment,
and held-out or nested tuning via tune_design_mfa() and
nested_tune_design_mfa(). Despite the API name, this is an
ASCA+/partial-RDA-style design decomposition; it does not apply MFA block
normalization.
Installation
Install the package from r-universe:
install.packages(
"muscal",
repos = c("https://bbuchsbaum.r-universe.dev", "https://cloud.r-project.org")
)
Or install the current GitHub version with pak:
install.packages("pak")
pak::pak("bbuchsbaum/muscal")
Quick start
library(muscal)
sim <- synthetic_multiblock(
S = 4,
n = 60,
p = c(20, 30, 15, 25),
r = 3,
sigma = 0.3,
seed = 42
)
fit <- mfa(sim$data_list, ncomp = 3)
dim(multivarious::scores(fit))
#> [1] 60 3
plot_variance(fit, type = "bar")
ggplot2::autoplot(fit)
That example covers the most common case: several feature blocks measured on
the same subjects. If your data are linked across uneven row sets, covariance
matrices, or repeated connectivity observations, jump to the method-specific
articles below.
Where to start
Package scope
Beyond the core factor methods, muscal also includes helpers for simulation
(synthetic_multiblock()), component estimation
(estimate_components(), significant_components()), projection of new data,
and a fairly broad set of plotting helpers for scores, loadings, block weights,
partial scores, convergence traces, and coverage diagnostics.
Development
For local site builds, use Rscript scripts/build-pkgdown.R. On this setup,
the default pkgdown::build_site() call can fail in its callr subprocess
even when the package itself installs and the GitHub Pages build path works.
