rsvd 2.0.0
Requirements and
compatibility
- Require R 4.4 or later and Matrix 1.7-0 or later. CI checks the
supported minimum and current R on Linux, Windows, and macOS, and
verifies the loaded Matrix version in every job.
- Preserve public function signatures, defaults, result classes, and
component names. Singular-vector counts retain their clamping rules, and
the existing sampling distributions are unchanged.
- Validate ranks, flags, and sampling parameters before numerical
work. Reject invalid randomized settings, including negative
oversampling that could produce missing singular values. Reject empty,
nonnumeric, and nonfinite matrix inputs with direct errors. Routines
that support missing data still omit incomplete rows with a warning.
Numeric data frames work consistently across the decompositions.
- Require at least two complete observations for PCA. Prediction
checks that new data have the training number of variables and still
uses their supplied column order. For compatibility, ordinary missing
values propagate and
NaN values encountered during scaling
become zero.
Numerical fixes
- Deterministic
rqb() honors k and returns
only the requested basis columns. Randomized QB retains its oversampling
columns, with width bounded by both matrix dimensions.
- Complex PCA uses squared magnitudes to compute real scale factors.
With
center = FALSE, total processed energy agrees with the
uncentered SVD; variance proportions use all processed directions,
including omitted ones.
- The internal pseudoinverse preserves dimensions when one singular
direction remains and returns the correct shape for rectangular zero
matrices. Its rank cutoff accounts for matrix dimensions, preventing CUR
from inverting roundoff singular values when the requested rank exceeds
the true rank. Small, resolved singular directions remain
supported.
- Robust PCA handles zero input and thresholding steps that retain no
singular values. Dual initialization uses the entrywise maximum norm,
and saturated singular-value thresholding increases the sampled rank
within the matrix dimensions.
- Robust PCA checks both reconstruction feasibility and stationarity.
The penalty increases only after the stationarity test is satisfied.
Stalled randomized updates receive bounded power-iteration refinements
and then an exact-SVD fallback when needed. These checks can require
more time than the former reconstruction-only stop.
err
remains the reconstruction history; trace = TRUE also
displays stationarity. A loose tolerance still completes an update and
returns its measured residual.
- PCA plots handle one retained component and fits without stored
scores.
plot.rpca() routes named options to the helpers
that accept them and invisibly returns the last plot. Factor maps
require valid integer component indices, matching group and label
lengths, and real coordinates. top.n must be a positive
integer. Plotting uses supported ggplot2 line-width and legend
arguments.
Memory and runtime
- Randomized QB keeps sparse Matrix inputs sparse during
multiplication, converting only sketches and projected factors to dense
matrices. Randomized ID and CUR use the same sparse products.
Missing-row omission in ID and CUR also preserves sparse storage.
- Deterministic QB factors only the leading
k columns
when a real, independent prefix gives the same basis as full base QR.
Complex inputs, deficient prefixes, and other cases use the full dense
QR fallback.
- Randomized PCA with an explicit rank centers and scales real sparse
inputs implicitly. Prediction applies the same transformation without
creating a full dense centered matrix. Sparse column moments account for
implicit zeros, nearly constant columns, and overflow in mean
accumulation.
rpca(retx = FALSE) avoids unused left singular vectors.
Total-variance calculations reuse column norms or a Frobenius norm
instead of applying sample-variance calculations to copied columns.- Full-column ID avoids a discarded pseudoinverse. CUR reuses its
selected row and column matrices. Bounding QB sketch width by the
smaller dimension can also reduce the number of random draws for very
wide inputs.
Documentation and tests
- Add the testthat entry point so package checks run the test suite.
Cover prescribed spectra, invariant subspaces, complex arithmetic,
vector counts, rank-deficient decompositions, input validation, and
sparse storage formats. Pseudoinverse tests use analytic examples and
the Moore-Penrose identities.
- Compare PCA with
prcomp() and explicitly processed
dense references. Check implicit sparse products, structural zeros,
custom matrix dispatch, and allocation limits. Compare partial QR with
the full base-QR factors.
- Test robust-PCA rank selection, rescaling, and recovery, including
known low-rank-only and sparse-only optima. These distinguish an
appropriate split from an arbitrary split with a small reconstruction
residual.
- Render the PCA plotting helpers and check component selection,
variance denominators, return values, and argument routing. Treat
lifecycle deprecations as errors in the relevant tests.
- Edit the README, package description, and all help pages for direct
language and concrete explanations. Add an early reconstruction example
and method guide, explain how rank differs from oversampling and power
iterations, and document sparse storage, preprocessing, and robust-PCA
stopping diagnostics.
- Clarify PCA fitted scores versus new-data projections, total versus
retained variance, and the loading/correlation interpretation. Explain
that robust PCA fits a batch of observations and does not return a
prediction method.
- Correct the digit dataset description and image example: the first
column contains labels, followed by 784 pixel columns. Complete the
tiger image attribution, and update the citation contact to match the
maintainer.
- Preserve the recorded README benchmarks and their raw data,
including reconstruction errors and cumulative R allocations. Identify
their source revision and distinguish them from checks of later release
candidates. Provide reproducible tiger and iris illustrations alongside
the performance examples.