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msPCA 0.5.0

• mspca() and tpm() now take two possible inputs: the covariance/correlation matrix or the data matrix directly. In practice, the functions take single generic argument M together with a type = c("Sigma", "X") selector. type = "Sigma" (the default) treats M as a covariance/correlation matrix (p x p); type = "X" treats M as a raw data matrix (n observations x p variables). The "Sigma" default preserves the behaviour of existing matrix-based calls.
• The raw-data path applies the algorithm to the data directly: each product Sigma %*% beta is evaluated as t(X) %*% (X %*% beta) / (n - 1) at cost O(np), and the p x p matrix is never materialized. This substantially improves scalability when n << p. The covariance back-end was refactored behind a covariance-operator abstraction (DenseOp / GramOp) shared by both input modes.
• Added preprocessing controls for type = "X": center, scale (covariance vs correlation), and divisor (“n-1” or “n”).
• Added validation for both input modes: a Sigma input is checked for squareness, symmetry and positive semidefiniteness (checkPSD, symTolerance, psdTolerance); an X input is checked for finiteness, dimensions and (when scaling) zero-variance columns.
• mspca() results now include variance_explained (per-PC) and total_variance; X-mode results also record inputType, center, scale, divisor, nObs and p.
• mspca() and tpm() now return S3 objects of class "mspca" and "tpm" respectively, enabling use of standard R generics.
• Added print.mspca(): S3 print method displaying the sparse loading matrix restricted to the union of active variables, the percentage of variance explained per PC, and the number of non-zero loadings. Replaces the removed print_mspca().
• Added summary.mspca(): produces a per-PC table of sparsity, variance explained, FVE, and cumulative FVE, followed by the full pairwise feasibility violation matrix.
• Updated citation

msPCA 0.4.1

• Standardized function man page titles to consistent title style.
• Removed unnecessary library(datasets) calls from examples while keeping explicit datasets::mtcars usage, and added datasets to Suggests to align example dependencies with CRAN guidance.
• Improved efficiency and clarity of R code
• Added a vignette

msPCA 0.4.0

• Renamed hyperparameters controlling truncated power method restart budgets for clearer and more consistent API naming.
• Documentation polish across function docs and package materials.
• Removed pairwise_correlation() and orthogonality_violation() and replaced them with a unified feasibility_violation_off() helper for feasibility diagnostics across constraint types.

msPCA 0.3.0

• Improved scalability of mspca() and tpw() through algorithmic and implementation optimizations.
• Function mspca() now accepts a new hyper-parameter minRestartTPM that limits the number of random restarts for the truncated power method after the first outer iteration
• Improved scaling of the penalty parameters for the case of zero-correlation constraints
• Fixed incorrect acronym for truncated power method (TPW <- TPM)

msPCA 0.2.0

• Added support for no-correlation constraints between PCs as well as orthogonality constraints. User chooses between orthogonality and uncorrelatedness constraints via the feasibilityConstraintType parameter to msPCA().
• Renamed return field from orthogonality_violation to feasibility_violation to support both constraint types.
• Renamed function feasibility_violation() as orthogonality_violation() to be more explicit
• Created function pairwise_correlation()
• Added warning message when no feasible solution is found

msPCA 0.1.0

• Initial CRAN release

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.