CRAN’s incoming pretest still reported “Overall checktime 11 min >
10 min” for 0.9.5, almost all of it
checking tests ... [442s]. Uwe Ligges suggested running the
less important tests conditionally on an environment variable set only
on the maintainer’s machine, and that is what this release does.
tests/testthat/test-cran-smoke.R is
new and is the only test file that runs by default. It exercises every
exported fitter and its S3 methods on toy data (6 x 20 matrices) in
under a second: 35 assertions, no bootstrap, no cross-validation, no
restarts.
Every other block – 145 of them – now begins with
skip_unless_full(), defined in
tests/testthat/helper-nmfkc.R. The full suite runs when
NMFKC_FULL_TESTS is set:
Sys.setenv(NMFKC_FULL_TESTS = "true"); devtools::test()3173 assertions, 144 seconds locally, and it is what the maintainer and CI run before every release. Nothing was deleted: the regression tests for the always-zero refit p-values, the RNG-stream pollution, the convergence tolerances and the identification conditions are all still there.
The earlier skip_on_cran() guards are gone, subsumed
by the new one.
Measured: 1.7 s in CRAN mode against 144 s in full mode, both with zero failures.
CRAN’s incoming pretest rejected 0.9.4 for its overall check time (48 minutes on the pretest Windows machine against a 10-minute budget; 34 of those were the tests and 11 the vignettes).
skip_on_cran() and
keep running locally at full size. Each has a CRAN-sized copy (smaller
fixture, fewer replicates), so the defects they guard, in particular the
always-zero refit p-values fixed in 0.9.4, remain covered on CRAN
itself.wild.B = 50 in its inference example.nmf.rrr family drops the rank /
rank.encoder aliasesrank and rank.encoder are removed from
nmf.rrr(), nmf.rrr.cv(),
nmf.rrr.ecv(), nmf.rrr.kernel.beta.cv(),
nmf.rrr.rank(), nmf.rrr.signed(),
nmf.rrr.signed.ecv() and
nmf.rrr.signed.rank(). Use
rank1 and
rank2. Q and R
still work, via ......., which
put deprecated names in every signature and made the help page read as
though they were worth using. They cannot simply move into
...: rank is a prefix of both
rank1 and rank2, so R’s partial matching turns
nmf.rrr(Y, rank = 3) into “argument matches multiple formal
arguments” before the body runs. Declaring them after ...
was the only way to suppress that – so the choice was to keep the odd
signature or drop the aliases, and they are dropped.rank.encoder is not a prefix of any remaining formal,
so it would otherwise have been swallowed by ... and
silently ignored. Passing either name now raises a
clear error naming its replacement.nmf.rrr() renames B.prob /
B.cluster to B1.prob /
B1.clusternmf.rrr() now returns two score
matrices instead of one. B1 = C X2 Y2 (Q x N) is the
decoder-side score, with ; the new B2 = X2 Y2 (R x N) is
the encoder-side score, i.e. B1 before the C
map. Each gets column-normalized memberships and hard labels:
B1.prob, B1.cluster,
B2.prob, B2.cluster.B.prob and B.cluster are
gone; they shipped in 0.8.8, so code reading them must be
updated to B1.prob / B1.cluster. The name
B alone became ambiguous once both scores were exposed, and
keeping it as an alias would reintroduce exactly the
one-object-two-names problem this release removes elsewhere. The
undocumented B component added during development is also
removed.H is retained but deprecated. It is
identical to B1. Package-internal code now reads
B1 (through an accessor that still falls back to
H, so objects saved by earlier releases keep working). Use
B1 in new code.nmfkc() stops computing criteria nothing
consumeddetail now defaults to
"fast". The only thing "full" adds is
the sample-clustering criteria silhouette,
CPCC and dist.cor, which cost (two distance
matrices plus a cophenetic correlation) and had no consumer: they left
rank selection, summary.nmfkc() never printed them, and
nmf.cluster.criteria() recomputes them from the fits it is
given. At they were 26x the cost of the rest of the call; over a
500-replicate bootstrap, 83s against 9s. They are
absent from fit$criterion unless
detail = "full" is asked for – absent rather than
NA, because CPCC is legitimately
NA at and the two meanings must not collide.criterion$B.prob.max.mean is removed.
Nothing in the package read it except the summary line that printed it,
and that line is now the effective-rank index (below).nmfkc.inference(method = "refit") and
nmfkc.ar.latent.inference() pass
detail = "fast" explicitly, so this holds even if the
default moves back.summary()
reports a factor diagnosticcriterion$effective.rank.index is new on a single fit:
the broken-stick correction , , that nmfkc.rank() already
plotted. It is verified to agree bit-for-bit with the
nmfkc.rank() column at matched settings, and both now call
one helper.summary() prints it as
Factor variance share in place of the old
Clustering Crispness. Unlike the crispness (range ,
monotone in ) this is a genuine index. Read it as how evenly the
across-sample coefficient variance is shared, which is not the
same as how useful the factors are: two duplicated factors split the
variance evenly and score near 1.nmfre()
correctness fixes (identification + signed warm start)U
inside the U-step. The (U, Theta) indeterminacy is
U -> U + Delta A,
Theta -> Theta - Delta, so the identification condition
is U A' = 0, not U 1 = 0; the alternating
fixed point satisfies U A' = 0 automatically, and imposing
row-centering on top broke it whenever 1' is not in the row
space of A. (Verified: after the fix ||U A'||
is ~1e-6 at convergence.)C.init to
+eps when C.signed = TRUE: a warm-start
C.init may legitimately carry negative entries (e.g. a
full-refit bootstrap restarting from the previous estimate), and the
unconditional pmax() destroyed their sign at every refit.
Clipping now applies only in the non-negative mode, mirroring the
in-loop update rule.B.L1 penalty (and its gamma
alias)B.L1 placed an L1 penalty on the fitted coefficient
field . Because is dense and tracks the overall reconstruction
magnitude, B.L1 acted as a crude global shrinkage that
pulls the fit toward zero and degrades prediction, without producing
useful structural sparsity. It has been removed from
nmfkc() and from the test-set B refit inside
nmfkc.cv(). Use C.L1 for sparsity / variable
selection on the parameter matrix (individual entries are driven to
exactly zero). Passing B.L1/gamma now has no
effect (silently ignored via ...).nmfre()nmfre() gains three optional penalties (default 0, via
...), acting as Gaussian priors on the basis/coefficients
and orthogonal to the random-effect machinery (U,
lambda, sigma2, tau2 are
unchanged):
X.L2.smooth: path-graph row smoothness of the basis
X — well suited to longitudinal / ordered-row models.X.L2.ortho: column orthogonality of
X.C.L2: ridge on Theta = C. For
C.signed = TRUE the C-step stays a closed-form solve (a
Sylvester ridge-least-squares via the eigenbases of X'X and
AA'); for C.signed = FALSE it is added to the
MU denominator. Penalties enter the fixed-lambda inner objective; the EM
variance updates are untouched. (L1 sparsity on Theta is
intentionally not offered: it would break the signed closed-form step
and conflicts with the random-effect shrinkage that already regularizes
the model.)nmfae*
deprecated in favour of nmf.rrr*nmf.rrr* / nmf.rrr.signed*
names (nmf.rrr, .inference, .ecv,
.cv, .rank, .DOT,
.heatmap, .kernel.beta.cv,
.rename, and the six signed variants). The former
nmfae* / nmfae.signed* names are now thin
deprecated wrappers that emit .Deprecated() and forward to
their nmf.rrr* counterpart. Fitted objects keep the legacy
S3 classes (e.g. class = c("nmf.rrr", "nmfae", "nmf")), so
all S3 methods and saved objects continue to work unchanged.nmf.sem*
deprecated in favour of nmf.ffb*nmf.ffb* names
(nmf.ffb, nmf.ffb.inference,
nmf.ffb.cv, nmf.ffb.split,
nmf.ffb.DOT). The former nmf.sem* names are
now thin deprecated wrappers that emit .Deprecated() and
forward to their nmf.ffb* counterpart. Fitted objects keep
class = c("nmf.ffb", "nmf.sem", "nmf"), so all S3 methods
and existing saved objects continue to work unchanged.C.L2
ridge for the signed familiesnmfkc.signed() and nmf.rrr.signed() gain a
C.L2 ridge (default 0, via ...) on the signed
coefficient matrix , penalizing C.L2 * ||Cp - Cn||^2.
Because only the difference (= ) enters the model, the penalty has zero
gradient on the unidentified common mode ; it is injected symmetrically
into the Cp/Cn multiplicative updates
(num_Cp += C.L2*Cn, den_Cp += C.L2*Cp) across
both the unweighted and weighted paths, and added to the tracked
objective.nmfkc.signed() now accepts X.L2.ortho
(column orthogonality) and X.L2.smooth (path-graph row
smoothness), matching nmfkc(). Both default to 0 (off), are
passed via ..., and are skipped when
X.restriction = "fixed". The penalties are folded into both
the fast unweighted and the weighted MU paths and into the tracked
objective.nmfae.signed() now accepts X1.L2.ortho /
X2.L2.ortho (orthogonality of the response-basis columns
and covariate-basis rows), matching nmfae(). Default 0, via
..., wired into both MU paths and the objective.by
option: grouping order of coefficient tablesprint() methods for the inference summaries
(nmfkc.inference,
nmfae/nmfae.inference,
nmfae.signed.inference, nmfkc.net.inference)
and summary.nmfre() gain a by argument
controlling how the significance table is grouped:
by = "covariate" (default, unchanged behaviour) lists all
bases within each covariate (1-1, 1-2, …), while
by = "basis" lists all covariates within each basis (1-1,
2-1, …). The default reproduces the previous ordering exactly.nmfkc.net, tri-type)
follows the same rule: since it sets , the parameter
matrix ’s column factor (Basis.col) is the covariate slot
and its row factor (Basis.row) is the basis slot, so
by groups by Basis.col /
Basis.row respectively. (The bi-type has no free , only ,
so no coefficient table.)print / plotnmfkc.ecv(), nmfkc.cv() and
nmfkc.bicv() now return classed objects
("nmfkc.ecv" / "nmfkc.cv" /
"nmfkc.bicv") with print() and
plot() methods — the rank sweeps (ecv,
bicv) plot a score-vs-rank curve with a marker, matching
nmfae.ecv() / nmfre.ecv(). Field access
(cv$sigma, etc.) is unchanged; nmfkc.ecv()
also now returns the swept rank vector.runtime as numeric
seconds everywhere (was a preformatted string in
nmfkc()); print() formats it for display.nmfkc.net() fit objects now return sigma
(RMSE), for parity with nmfkc() /
nmfkc.signed().nmfre.ecv() returns the held-out RMSE as
$sigma (was $sigma.ecv) to match
nmfkc.ecv().predict.nmfre(): fixed-effect prediction for new
covariates, or the in-sample BLUP fit when newA is
omitted.nmf.sem/nmf.ffb fit objects now carry the
shared "nmf" class, so the common
coef/fitted/residuals fallbacks
apply.nfolds
(nmfre.ecv was nfold; legacy names accepted
via ...); summary.nmfre() CI toggle is
ci.show (object-first);
nmfae/nmfae.signed fit objects gained an
iter alias of niter.X.L2.smooth:
row-smoothness penalty on the basisX.L2.smooth (nonnegative, default 0) adds
with the path-graph Laplacian over the rows, i.e. it penalizes squared
differences between adjacent rows and yields gently-varying (smooth)
bases — useful when the rows of have a natural order (e.g. time points).
Like X.L2.ortho, it slots into the multiplicative -step (),
preserving non-negativity and monotone descent. Default 0 reproduces
prior results exactly.nmf.rrr and nmf.ffb are the documented
namesnmfae* and nmf.sem* families
are now marked internal (@keywords internal): they remain
exported and fully functional for backward compatibility, but no longer
appear in the reference index / pkgdown site. Their documented,
user-facing names are the NMF-RRR aliases (nmf.rrr*) and
the NMF-FFB aliases (nmf.ffb*), which now each have their
own self-contained help page (previously nmf.ffb* shared
the nmf.sem* pages).X.init = "kmeans++"
basis initializationX.init = "kmeans++"
(alias "kmeanspp") seeds the -means centres by weighting
(Arthur & Vassilvitskii, 2007, SODA) before Lloyd refinement, giving
a more careful, -competitive initialization than uniform-random seeding.
Available in every optimizer: nmfkc, nmfre,
nmf.sem, nmfkc.net, nmfkc.signed
(shared initializer), and nmfae /
nmfae.signed, which now forward X.init to
their internal nmfkc() basis-init steps. The default
remains "kmeans" (unchanged results); nstart
is not used for "kmeans++" (one careful seeding replaces
random restarts).nmf.rrr
/ nmfae family: rank1 / rank2
argumentsrank1 (response basis ) and
rank2 (covariate basis , default rank1),
replacing the asymmetric rank / rank.encoder.
Applies across the whole family:
nmfae/nmfae.signed and their
.ecv/.cv/.rank/.kernel.beta.cv
helpers (and the nmf.rrr* aliases). The legacy
rank / rank.encoder (and Q /
R) remain accepted for backward compatibility, so existing
calls keep working.nmfre.ecv:
rank selection for NMF-REnmfre.ecv() selects the basis rank by Wold-style
element-wise (entry-holdout) cross-validation with iterative imputation,
scoring the held-out prediction RMSE (sigma.ecv). The
held-out entries of a column are predicted from that column’s retained
entries via the BLUP , so — unlike nmfkc.ecv() (zero-weight
mask, fixed-effect prediction) — it evaluates the full NMF-RE model
including the random effects. Returns a "nmfre.ecv" object
with print/plot methods (the plot marks the
minimizing rank). Sign convention follows C.signed; CV
tolerances are loosened by default and overridable via
....nmfkc.DOT:
signed-coefficient graphsC.signed lets nmfkc.DOT()
draw graphs when () is signed (real-valued), e.g. from
nmfre(C.signed = TRUE) or the *.signed fits.
In signed mode threshold is an
absolute-value cut ( threshold), edge
widths scale by , and negative edges are drawn as black dashed
lines (positive edges solid) with their signed numeric labels.
Default C.signed = NULL auto-detects from
result$C.signed or negative entries in / ;
FALSE restores the historical non-negative behaviour. The
basis is always non-negative, so edges are unaffected.nmfre:
marginal-NLL convergence tracenmfre() now records nll.trace, the
marginal negative log-likelihood (random effects integrated out), which
the ECM algorithm decreases monotonically. plot.nmfre()
displays this instead of the fixed- penalized objective
(objfunc.iter), which is not monotone across outer
iterations because it jumps when is updated.nmfre:
optimization and inference fully separatednmfre() now performs optimization
only, mirroring the nmfkc() /
nmfkc.inference() split. The wild.bootstrap
argument and all inference outputs (coefficients,
C.se, C.se.boot, C.ci.*,
C.p.side, sigma2.used, …) are removed from
nmfre(); the inline inference block is gone, making the
function lighter and easier to maintain. Obtain standard errors,
z-values, p-values, and confidence intervals for by passing the fit to
nmfre.inference(fit, Y, A). summary() prints
the coefficient table only after inference has been run.nmfre:
EM/ECM algorithm and sign-free fixed effects (paper port)nmfre() is re-implemented to follow the Psychometrika
manuscript’s NMF-RE mixed model . The optimizer is now an
outer-inner ECM: the inner loop is a fixed-
block-coordinate descent (random-effect ridge BLUP for , complete-EM
semi-NMF step for the basis including the posterior variance , and a
fixed-effect update for ); the outer loop runs the EM M-steps for and
until stabilizes.C.signed (logical, default
TRUE, recommended, matches the paper). TRUE
makes the fixed-effect coefficients () real-valued,
updated by exact least squares, with a two-sided test (interior null)
and no projection of the bootstrap replicates. FALSE
restores the historical non-negative variant (multiplicative update,
one-sided/boundary test). A character value ("signed" /
"nonneg") is also accepted for backward compatibility.C.signed is the single switch for the whole estimation
scheme: it also selects the basis () update rule (TRUE →
complete-EM semi-NMF, FALSE → positive-part multiplicative
update), reproducing the paper’s pairing. is non-negative in both cases.
(x.postvar remains an advanced toggle for the
posterior-variance term of the semi-NMF step.)dfU.control is now deprecated and inert.
Output gains the logical C.signed;
summary.nmfre() reports the sign convention and p-value
side.nmfre.dfU.scan() (and its print method): it
scanned df_U cap rates, which no longer exist now that the
variance components are estimated. The df.rate argument is
retained but inert.nmf.rrr:
NMF-RRR names for the nmfae familynmf.rrr / nmf.rrr.signed (and
.inference, .ecv, .cv,
.rank, .DOT, .heatmap,
.kernel.beta.cv, .rename) are thin aliases of
the corresponding nmfae* / nmfae.signed*
functions, matching the (tri-factorized non-negative reduced-rank
regression) name used in Satoh & Tokuda. The legacy
nmfae* names remain fully functional (no deprecation).
nmf.rrr() / nmf.rrr.signed() prepend the
NMF-RRR class to the fit; all existing S3 methods are reused by
inheritance.Resp
(response basis ) and Cov (covariate basis
) instead of Dec/Enc, in both
nmfae()/nmfae.signed() (and so
nmf.rrr*), matching the response/covariate co-clustering
reading.nmfae():
Kullback-Leibler divergence objectivenmfae() gains method = c("EU", "KL")
(mirroring nmfkc()). (default, unchanged) minimises the
Frobenius distance; minimises the generalised Kullback-Leibler
divergence via Lee-Seung multiplicative updates for all three factors of
(numerator carries the ratio , denominator the column/weight sums).
Weights, L1/L2 penalties and the encoder structure are supported in both
modes. For the residual SE is (not on the data scale); records the
objective used.nmfae() and nmfre() now honour
nstart (previously silently ignored): it is forwarded to
the nmfkc() initialisation step(s) (k-means multi-start).
Default keeps the historical single-start behaviour; a larger value
gives a more stable initialisation and is recommended before inference.
(nmfkc(), nmfae.signed(),
nmfkc.net() and nmfkc.signed() already
supported nstart; all expose it via ....)nmfre(): a character X.init
(e.g. "runif", "nndsvd",
"kmeans") previously fell through unresolved and crashed in
.nmfre.normalize.X() (“‘x’ must be an array of at least two
dimensions”). X.init now accepts (default), a named init
method forwarded to nmfkc() (so random-init multi-start
works), or a numeric basis matrix (used as-is, with estimated given that
fixed ).nmfkc.inference():
re-fit wild bootstrap for singular informationmethod = "refit" (alongside the default
backward-compatible "onestep") performs a residual wild
(multiplier) bootstrap that re-estimates () to convergence with the
basis held FIXED, using no information matrix. It stays
valid when the Fisher information is singular (over-parameterised /
kernel covariates) or lies on the boundary, where the one-step /
sandwich SE is unreliable. With fixed there is no label switching or
scale ambiguity, so element-wise SE/CI of are valid even for . The
bootstrap SE/CI become primary and the p-value is a two-sided bootstrap
p-value.wild.dist selects the multiplier distribution
("rademacher", "mammen", "exp"),
orthogonal to method; wild.unit
("element" / "column") the granularity. Raw
draws are returned in $C.boot.draws so any identifiable
functional (e.g. a contrast or fitted curve) and its percentile band can
be formed. nmfkc.net.inference() inherits the mode by
delegation.R/inference-boot.R (.wild.multipliers,
.boot.onestep, .boot.refit,
.refit.C.MU, .boot.summarize). The previously
duplicated one-step loop in nmfkc.inference(),
nmfre() / nmfre.inference(),
nmfae.inference() and nmfae.signed.inference()
now all call the shared .boot.onestep() (behaviour
unchanged; nmfae.signed uses project = FALSE
for signed ).nmfkc.net.DOT():
default layout is now "neato"layout choices are reordered by recommendation
(neato, fdp, twopi,
circo, dot), so the default changes from
"fdp" to "neato", which separates community
graphs more clearly. Raising threshold (e.g. 0.2–0.3)
further declutters weak membership edges.nmfkc.net.DOT() mis-detected type = "bi"
as "tri"$type
field and fell back to all.equal(C, diag(Q)), which fails
when C carries dimnames (it reports a names mismatch). A
type = "bi" fit was therefore treated as
"tri", drawing the inter-class interaction layer that the
bi model (with ) should not have. Detection now uses $type
first (falling back to the dimnames-safe identity check), so
"bi" correctly draws no inter-class edges.nmfkc.bicv()
/ nmfkc.consensus(): leaner signatures... (same safe
defaults): nmfkc.bicv() is now
nmfkc.bicv(Y, rank, ...) (nfolds = 2 per Owen
& Perry, plus seed, nnls.maxit, via
...), and nmfkc.consensus() is
nmfkc.consensus(Y, A, rank, nrun, keep.consensus, ...)
(seed, pac.range via ...).
Existing named-argument calls are unaffected.nmfkc.ard():
simpler, safer interfacenmfkc.ard(Y, rank, nrun, plot, ...); everything else
(prior, seed, a, b,
maxit, epsilon, tol) moves into
... with the same safe defaults, so a typical call is just
nmfkc.ard(Y, rank = K).nrun now defaults to 10 (was
1): ARD is a sensitive point estimate, and several restarts
give a stable modal rank by default.b is an empirical energy scale, not the paper’s
method-of-moments value (Eq. 38).nmfkc.ard():
better default prior scaleb is now the initial per-component energy
scale (nrow(Y) + ncol(Y)) / K * mean(Y) instead of a fixed
0.001 * mean(Y). The old fixed fraction over-pruned
(winner-take-all collapse onto one dominant component) when
(F + N)/K was large; the new scale-aware default recovers
genuine low-rank structure stably (e.g. a clean rank-3 signal: relevance
1, 0.99, 0.87, 0, ..., all restarts agree).nmfkc.ard(): ARD rank determination (Tan & Fevotte
2013, prototype)L2
half-normal / L1 exponential) that drives unsupported
components to zero. The number of surviving components is the estimated
rank – no rank scan. Returns an "nmfkc.ard" object with
print and a relevance-bar plot. Plain NMF
only; a sensitive point estimate (depends on prior / start / init), so a
complement to the CV / consensus engines, not a sole criterion.nmfkc.consensus(): consensus-clustering rank selection
(Brunet 2004)nmfkc.ecv / nmfkc.bicv. For each
rank it runs NMF nrun times from random initializations
(X.init = "runif"), builds the consensus matrix from the
per-run hard clusterings, and returns two stability scores per rank:
cophenetic (cophenetic correlation coefficient, Brunet et
al. 2004) and dispersion (Kim & Park 2007, in
[0,1]). Unlike the CV engines, a good rank
maximizes stability. Optional
keep.consensus = TRUE returns the consensus matrices.pac, the Proportion of Ambiguous
Clustering (Senbabaoglu et al. 2014; fraction of consensus entries in
the ambiguous interval pac.range, default
(0.1, 0.9)). Lower is better and it is more sensitive than
the often-saturated cophenetic. The
print/criteria-plot show all three
metrics."nmfkc.consensus" object with
print and plot methods: plot(cs)
(type = "criteria") draws the stability curves;
plot(cs, type = "heatmap", rank = ...) draws the consensus
matrix heatmap(s) reordered by hierarchical clustering (default = all
ranks in a n2mfrow grid; mfrow
overridable).nmfkc.bicv(): bi-cross-validation for rank
selectionnmfkc.ecv: it returns the held-out error per rank
(objfunc, sigma) and nothing more. Holds out a
row-block and a column-block at once, fits NMF only on the
retained block, and predicts the held-out block by folding the held-out
rows/columns onto the fixed factors via non-negative regression (no
information leakage, unlike element-wise nmfkc.ecv).
nfolds = 2 (leave out half rows / half columns) per Owen
& Perry’s recommendation.*.rank:
eff.rank.idx shown for context (no best marker)eff.rank.idx, green) is drawn for context only and no
longer carries a “Best (Max)” marker: it is a factor-utilization
diagnostic (most even relative to the random null), not a predictive
rank optimum. The recommended rank is driven solely by the ECV minimum
and the R-squared elbow.*.rank:
broken-stick-corrected effective-rank index*.rank criteria table gains
effective.rank.expected (the broken-stick /
uniform-Dirichlet null exp(H_Q - 1), H_Q = the
Q-th harmonic number) and
effective.rank.index, the [0, 1] index
(effective.rank - expected) / (Q - expected) (clamped). The
index anchors 0 at the random null and 1 at perfect evenness, removing
the small-rank inflation of the raw effective.rank / Q. Its
maximum is a meaningful rank, so the diagnostics plot now draws this
corrected index (green, eff.rank.idx) with a restored “Best
(Max)” marker in place of the raw ratio.*.rank
results gain plot() / print()
methodsnmfkc.rank(),
nmfkc.net.rank(), nmfkc.signed.rank(),
nmfae.rank(), nmfae.signed.rank()) now return
a classed object ("nmf.rank"). plot() redraws
the three-criterion diagnostics plot (honouring main,
xlab, ylab, lwd) and
print() shows the recommended rank, the per-criterion best
ranks, and the criteria table. As before the constructor draws
immediately when plot = TRUE; the $rank.best
and $criteria fields are unchanged, so existing code keeps
working.nmf.cluster.flow(): cluster-flow diagram across
ranksnmf.cluster.flow() and
nmf.cluster.criteria() now treat the supplied
fits as a generic (kept in the given order, sorted by
rank), so the same rank fitted as different models is also supported.
Both gain a names argument for the x-axis tick labels
(default: each result’s $rank), and in
nmf.cluster.flow() the reference argument is
now the (1-based position) of the result that defines the colours – not
a rank value – defaulting to the central result
floor(length(fits) / 2) + 1 (e.g. the 2nd of 2 or 3
results).$ARI, length ), summarizing how much the hard clustering
changes from one rank to the next.nmf.cluster.flow() now inserts a gap of one average
cluster () between clusters in the per-rank layout and sizes each grey
box exactly to the minimum/maximum position of its members, so the
cluster boxes are clearly separated with the gaps maximized. Each rank
is normalized to the full height independently.2, 3 with no 1) – this is correct and
consistent with the fit, and the labels are not renumbered.nmf.cluster.flow() now returns a classed object with a
dedicated plot() method, so the diagram can be (re)drawn
with plot(fl, col = , lwd = , xlab = , ylab = , main = ) –
the colour vector (indexed by reference cluster), line width, axis
labels and title are all honoured. The constructor still draws
immediately by default (plot = TRUE) and forwards graphical
arguments to the plot method; use plot = FALSE to build the
object and plot it later. Its print() method shows the
adjacent-rank ARI and the full cluster table.nmf.cluster.flow(fits, reference = ) takes a list of
models fitted at different ranks (any non-negative MU family) and draws
an alluvial / Sankey-style diagram of how the hard sample clustering
changes with the rank : each individual flows left-to-right across the
ranks (x-axis), its vertical position is set by its cluster (clusters
reordered per rank by a barycenter heuristic to reduce crossings), and
lines are coloured by the cluster at the rank – so one can watch the
reference clusters split or merge. At every rank a translucent grey box
is drawn of each cluster’s members with the cluster number centred
inside, so the grouping and labels are visible at all ranks (not only
the reference). The default line palette is now a strong, well-separated
qualitative set (ColorBrewer , no pale colours) and can be overridden
with . Returns (invisibly) the table with rows = individuals, columns =
rank, entries = cluster number.nmf.cluster.criteria(): sample-clustering quality across
ranksnmf.cluster.criteria(fits, Y) takes a (one per rank; a
single fit is also accepted) and reports the clustering-quality criteria
silhouette, CPCC, and dist.cor
for each rank, returning a per-rank $criteria table
(mirroring nmf.cluster.flow()). It has plot()
(line plot of the three criteria vs rank) and print() (the
table) methods, and draws immediately when plot = TRUE.
Works for any family (nmfkc, nmfkc.signed,
nmfae, nmfae.signed, nmfkc.net,
nmfre, nmf.sem/nmf.ffb; the last
needs the exogenous block via Y2). These are
clustering-stability diagnostics, deliberately separate
from the rank-selection *.rank functions (r.squared /
effective rank / ECV).nmf.cluster.criteria()
detects this from the actual coefficient: when it is non-negative the
hard-label silhouette (and cluster sizes) are returned;
when it is signed silhouette is NA while the
distance-based CPCC and dist.cor are still
computed. (ARI is not reported here – it compares two clusterings,
e.g. across ranks or resamples, so it is not a single-fit
quantity.)nmfkc.rank() no longer carries ARI,
silhouette, CPCC, or dist.cor in
its criteria table – those clustering-stability metrics now
live in nmf.cluster.criteria(). All five
*.rank functions return the same five
columns (rank, effective.rank,
effective.rank.ratio, r.squared,
sigma.ecv). Per-rank fits use detail = "fast",
so the expensive O(N^2) distance computations are skipped during rank
selection. rank.best is unchanged. The *.rank
functions now emit a one-line message pointing to
nmf.cluster.criteria() for clustering quality.nmfkc.net.rank(), nmfkc.signed.rank(),
nmfae.rank() (paired ) and nmfae.signed.rank()
(paired) bring nmfkc.rank-style rank selection to the other
multiplicative-update models. Each reports the three criteria that are
well defined for every family – r.squared, the effective
rank (utilization), and the element-wise CV error sigma.ecv
– and returns list(rank.best, criteria).
(nmf.ffb / nmfre are not covered: they do not
support the element masking that ECV needs.)nmfkc.rank() plot simplified and
unified. All *.rank functions now share one
back-end .rank.finish() and draw the same concise
three-criterion figure: r.squared (red),
eff.rank (green), and sigma.ecv (blue, right
axis), each as a line with points, rank-number labels, and a highlighted
best marker – “Best (Elbow)” for the R-squared knee, “Best (Peak)” for
the effective-rank utilization, and “Best (Min)” for the CV minimum.
nmfkc.rank() still computes ARI,
silhouette, CPCC, and dist.cor
into its criteria table, but no longer plots them.*.rank functions gain a
detail argument matching nmfkc.rank:
"full" (default) runs the element-wise CV and reports
sigma.ecv; "fast" skips the (expensive) CV, so
the plot shows only r.squared and eff.rank and
the recommended rank falls back to the R-squared elbow.nmfkc.ecv(), nmfae.ecv(),
nmfkc.signed.ecv(), nmfae.signed.ecv()) now
build their folds through a single internal helper
.ecv.make.folds(), removing four near-identical copies of
the fold-partitioning loop. nmfkc.net.ecv() keeps its
symmetric upper-triangle folds..ecv.run(labels, nfolds, run_one, progress): the
single-rank ones (nmfkc.ecv(),
nmfkc.net.ecv(), nmfkc.signed.ecv()) and the
-grid ones (nmfae.ecv(), nmfae.signed.ecv()).
Each supplies a model-specific run_one(i, k) closure (mask
fold, refit config i, return held-out loss) and an optional
progress callback; .ecv.run() handles the config-by-fold
loop, the
objfunc/sigma/objfunc.fold
aggregation, and naming. This removes the last copies of the CV-loop
machinery, including the per-grid reshaping in
nmfae.ecv().objfunc, sigma,
objfunc.fold, names/labels) are byte-for-byte identical to
before, verified across EU and KL losses, the symmetric (upper-triangle)
case, and both paired and full grids..print.fit.statistics() and
.print.structure.diagnostics() render the “Statistics” /
“Goodness of fit” and “Structure Diagnostics” blocks for
summary.nmfkc(), summary.nmfae(), and
summary.nmfkc.net() (incl. the signed variant). Labels are
padded to a common width so values are column-aligned, fields absent
from a given model are skipped automatically
(e.g. nmfkc.net has no residual SE), and any future fit
statistic or sparsity row is now added in one place instead of
per-summary.summary() now reports the Effective
Rank as x.xx / Q (NN.N%) – the absolute value,
the nominal rank, and the utilization ratio
effective.rank / Q as a percentage – for
nmfkc(), nmfkc.net(), nmfae(),
nmf.ffb() / nmf.sem(), and
nmfre() — previously only nmfkc() showed it.
Each is computed by the new shared internal helper
.effective.rank(B) from the model’s natural
coefficient/score matrix: the coefficients (nmfkc), the
latent encoding (nmfae), the node membership
(nmfkc.net), the latent scores (nmf.ffb), and
the BLUP scores (nmfre). NA at .silhouette is now computed in the original data
space. It used to be evaluated on the rank- B.prob
simplex, whose dimension changes with ; that made it monotone in (always
favouring the smallest rank) and hid genuine cluster structure. It is
now the standard mean silhouette width over dist(t(Y)) (the
fixed original-data sample distances) with the per-sample hard labels —
the k-means convention. On data with real clusters it now shows an
interior optimum (e.g. the road-OD network peaks at the same rank as the
cross-validation minimum).CPCC is now the classic cophenetic correlation
of dist(t(B)). It used to be computed from the
soft co-membership t(B.prob) %*% B.prob, which was nearly
flat across . It is now
cor(dist(t(B)), cophenetic(hclust(dist(t(B))))) — how well
a hierarchical clustering of the rank- coefficient distances reproduces
those distances (Sokal & Rohlf). It now varies with and recovers an
interior optimum.ICp, AIC, and
BIC from nmfkc()’s
criterion list, from summary.nmfkc(), and from
nmfkc.rank()’s table. Empirically (across three real
datasets) ICp was monotone increasing (always selecting )
and AIC monotone decreasing (always selecting the largest
); for NMF, where the parameter count grows as , these information
criteria do not have a usable interior optimum, so they were misleading
rather than informative..silhouette.simple()
(centroid-approximate, took a B.prob matrix) was replaced
by .silhouette.mean(D, labels), which returns the exact
mean silhouette width from a distance matrix and labels.nmfkc()Y.symmetric = "bi" / "tri" option (deprecated in
v0.7.x) has been removed from nmfkc() and
nmfkc.ecv(). Symmetric NMF of network data now lives
exclusively in the dedicated nmfkc.net() /
nmfkc.net.ecv() functions, which use the correct Frobenius
bilateral-gradient updates. Passing Y.symmetric to
nmfkc() or nmfkc.ecv() now stops with a
message pointing to the replacement:
nmfkc.net(Y, rank, type = "tri") (types "tri",
"bi", "signed"). This also removes the bi/tri
code branches (cube-root damping, fixed C = I, tri
C-update, upper-triangle CV folds) from nmfkc(),
simplifying the core function.nmfkc() now reports
criterion$effective.rank, the effective
rank of the fit: exp of the Shannon entropy of the
explained-variance distribution
p_k = var(B[k, ]) / sum_j var(B[j, ]). By the trace
identity sum_k var(B[k, ]) = tr(Cov(B)), each
p_k is the exact fraction of the total coefficient variance
carried by factor k, so the entropy is a genuine additive
decomposition (variances add; standard deviations do not, which is why
variance — not sd — is the natural partner for the entropy here). It
ranges in [1, Q] and counts how many latent factors
actively shape across-sample variation (dead, zero-variance factors drop
out). This is the PCA-style explained-variance /
effective-dimensionality measure and reuses the
exp(entropy) functional form of Roy & Vetterli
(2007).summary.nmfkc() prints
Effective Rank: x.xx / Q.nmfkc.rank() adds an effective.rank column
to its criteria table. When effective rank plateaus well below the
nominal rank, the extra factors are not carrying additional coefficient
variance — a signal that the rank is over-specified.nmfkc.rank(plot = TRUE) overlays an
eff.rank curve (effective rank divided by nominal rank, in
[0, 1], solid green line) on the diagnostics plot. A peak
in this utilization curve marks the rank at which the latent factors
carry the most evenly distributed variance.B.prob.sd.min and
B.prob.entropy.mean from nmfkc()’s
criterion list, from summary.nmfkc(), and from
nmfkc.rank()’s criteria table and plot. All three
B.prob.* peakedness metrics are monotone in the rank
Q, so they carry no peak/elbow signal for rank selection
(verified empirically); the principled rank signals are ECV, the
R-squared elbow, and the new effective.rank
utilization.B.prob.max.mean (clustering crispness) is retained, but
only in summary.nmfkc() (“Clustering Crispness”) and the
criterion list. At a fixed Q it remains a
useful confidence check — the mean dominant-cluster membership — before
treating B.cluster as hard labels. It is no longer shown in
nmfkc.rank() (cross-Q), where its
1/Q baseline shift makes it misleading.summary.nmfkc() no longer prints “Clustering Entropy”
(it duplicated the crispness information).nmfkc(), nmfae(),
nmfae.signed(), nmfkc.net(),
nmfkc.signed(), nmfre()) now returns three
goodness-of-fit summaries on the same scale, computed by the new
internal helper .r.squared.all():
r.squared: Pearson (scale-invariant, in ). Unchanged
from before.r.squared.uncentered: . Baseline = the zero matrix
(natural for non-negative factorizations without an intercept); matches
the “uncentered R²” of intercept-free regression.r.squared.centered: . Baseline = per-row mean; the
standard (“centered”) multivariate- regression ; equals 0 when the model
predicts the row mean.Y.weights == 0
masking (the standard NA-hold-out convention). For nmfre()
the same three variants are also reported on the fixed-only prediction
as r.squared.fixed.*. Displayed by all
summary.* methods.nmfkc.net(): r.squared now correctly
excludes weight-zero (NA-masked) entries when Y.weights is
supplied or auto-masking is in effect, matching the convention used by
nmfkc(), nmfae(), nmfae.signed(),
and nmfkc.signed(). Previously the correlation was computed
over the full matrix including replaced-NA cells, giving a distorted
r.squared.nmfkc(): removed Examples 3 & 4 (deprecated
Y.symmetric = "bi"/"tri"); the documentation now points
users to \link{nmfkc.net}() for symmetric NMF.summary.nmf.sem(): example code, @param,
and @seealso updated to use the canonical
nmf.ffb name (the S3 method continues to dispatch correctly
via c("nmf.ffb", "nmf.sem") inheritance).nmf-sem-with-nmfkc.Rmd vignette code now
reference the canonical nmf.ffb.* aliases
(nmf.ffb(), nmf.ffb.cv(),
nmf.ffb.DOT()) instead of the legacy nmf.sem.*
names. Both names continue to work; the change only affects what users
see on the GitHub Pages homepage and in the vignette source.nmf.ffb* family added as the canonical alias for
nmf.sem* (Satoh 2025, arXiv:2512.18250 adopts “NMF-FFB” —
Non-negative Matrix Factorization with Feed-Forward + Feedback — as the
model’s canonical name). nmf.sem* continues to work and
shares the same return classes (c("nmf.ffb", "nmf.sem") and
c("nmf.ffb.inference", "nmf.sem.inference", ...)), so
existing scripts are unaffected.nmf.sem.inference() / nmf.ffb.inference():
replaced the legacy 1-step Newton wild bootstrap with a full
X-fixed pair bootstrap. Resamples columns of (Y1, Y2), refits
(C1, C2) with X held at the original fit, and reports per-element
support_rate = mean(|c_b| > threshold) together with
percentile CIs. Significance markers (* / ** /
*** at sup > 0.95 / 0.99 / 0.999) follow the lavaan
convention. Both Theta_1 (feedback) and Theta_2 (exogenous) are
inference targets (previous version covered only Theta_2).nmf.sem() / nmf.ffb(): now runs
nmfkc(Y1, A = Y2) internally by default
when X.init is a string method, forwarding
X.init, X.L2.ortho, epsilon,
maxit, seed. The feedforward fit is used both
as the X warm-start and as the baseline for SC.map.
nmfkc.baseline = FALSE opts out.nmf.sem.inference(): fixed dimension bug in the
Leontief identity matrix (I_mat <- diag(Q) should have
been diag(P1)); previously every replicate was silently
marked invalid when P1 != Q.nmfkc.net(): now auto-masks NA entries of
Y (parity with the other four NMF variants); previously
errored at the min(Y) < 0 check when Y
contained NA.nmfkc(): Fixed C matrix asymmetry in tri-symmetric NMF
(Y.symmetric = "tri"). The C update was using stale B and
XB computed from the old X; now B and XB are recomputed after X is
updated. Also fixed column reordering to permute both rows and columns
of C. Previously the relative asymmetry could reach ~46%; now it is at
machine precision (~1e-14).Y.weights semantics unified to lm()-style
weighted least squares across nmfkc(),
nmfae(), nmfkc.net(),
nmfkc.signed(), nmfae.signed(): loss is now
sum(W * (Y - Yhat)^2) (linear in W, matching
lm()’s weights argument). Binary masks (W ∈
{0, 1}; the standard ECV / NA-mask case) are unaffected since W =
W^2."maximum iterations (N) reached..." warning when
maxit is exhausted without meeting the relative- tolerance
criterion (previously silent in nmfae,
nmfae.signed, nmfkc.net,
nmfkc.signed, nmfre, and
nmf.sem).maxit = 5000 as the default
(was 5000 / 20000 / 50000 inconsistently). Together with the maxit
warning above, users see explicit feedback when 5000 is insufficient and
can opt into a larger cap..init_X_method() for X
initialization via "nndsvd" / "kmeans" /
"kmeansar" / "runif" / numeric matrix. All NMF
families now use the same dispatch logic; previous ad-hoc inline
implementations are removed.nmf.sem() returns SC.map (input-output
structural fidelity: correlation between the equilibrium operator and
the feedforward baseline mapping; Satoh 2025 §4.SC.map) automatically
when nmfkc.baseline is supplied or computed
internally.summary.nmf.sem(): rewritten to display the
full-bootstrap inference output — separate Theta_1 / Theta_2 blocks with
Estimate | CI_low | CI_high | support | Pr(>0) | sig,
plus a bootstrap meta-info header.coef.nmf.sem(): now returns a long-format data frame
with rows for every entry of both C1 and C2
(Type | Basis | Covariate | Estimate); previously returned
only the C2 matrix when no inference had been run. Schema matches the
inference-augmented output for uniformity.plot.nmf.sem(): default trace is now
objfunc.full (loss + penalties — the actual
monotonically-decreasing quantity that the multiplicative updates
minimize) instead of objfunc (reconstruction only). New
argument which = "full" | "reconstruction" | "both".nmf.sem.DOT(): significance stars now appear on Theta_1
(feedback Y1 → F) edges in addition to Theta_2 (exogenous Y2 → F); X (F
→ Y1) edges remain unstarred since the basis is not the inference
target.plot.nmfae.ecv(): Heatmap cell text color is now always
black for better readability on light-colored cells.nmfkc(): X.init = "runif" now supports
nstart > 1 for multi-start initialization. Multiple
random starting points are evaluated with 10 standard NMF iterations,
and the best (lowest Frobenius error) is selected.nmfae(), nmfre(): r.squared
is now computed as cor(Y, fitted)^2 (squared correlation
between observed and fitted values), consistent with
nmfkc(). Previously nmfae() used
1 - SS_res/SS_tot and nmfre() used the same
regression-style R-squared, which can behave unexpectedly for
intercept-free non-negative models.nmfkc.kernel.beta.nearest.med(): added a
candidates argument controlling the bandwidth grid.
Options: "7points" (new default,
t = {-1,-2/3,-1/3,0,1/3,2/3,1}), "4points"
(t = {-1/2, 0, 1/2, 1}), or a user-supplied numeric vector
of values. Previously the grid silently differed between the no-landmark
(Uk = NULL; 4 points) and landmark (7 points)
branches.nmfkc.signed(): NMF-KC with signed
covariate/coefficient. Model with , (signed), real-valued. Uses Ding et
al. (2010) sign-splitting + Direct MU; may also contain negative entries
(semi-NMF regression). Supports Y.weights for element-wise
masking.nmfkc.signed.cv(), nmfkc.signed.ecv():
column-wise and element-wise k-fold CV for rank selection on signed
data.nmfae.signed(): Three-layer autoencoder with . preserve
soft clustering on both decoder and encoder sides while the bottleneck
can carry negative weights (e.g., anti-correlated properties). Hybrid
warm-start (from nmfae()) + Direct MU with
multi-restart.nmfae.signed.ecv(): element-wise CV for (decoder-rank,
encoder-rank) selection.nmfae.signed.inference(): sandwich SE + wild bootstrap
for (no non-negativity projection on since it is signed).predict.*.signed(),
plot.*.signed(), summary.*.signed(), and
nmfae.signed.rename() helper.nmfkc.net(): Single unified entry point for symmetric
NMF of network data, with type = "tri" | "bi" | "signed".
All three variants use the Frobenius-full bilateral gradient (supersedes
the one-sided approximation in nmfkc(Y.symmetric = ...)).
type = "signed" supports signed via Ding et al. (2010)
sign-splitting, preserving for soft clustering while allowing
inter-cluster repulsion. The returned object’s fields are uniform across
types: and are for tri/bi, and populated matrices for signed. is always
populated (identity for bi, non-negative for tri, signed for
signed).nmfkc.net.ecv(): Element-wise cross-validation with
upper-triangle folds (mirrored to the lower triangle to prevent symmetry
leakage). Unified entry point for
type = "tri" | "bi" | "signed" (calls
nmfkc.net() with the matching type for each
fold).nmfkc.net.DOT(): Graphviz DOT visualization for
symmetric NMF networks. Displays basis-to-node membership edges and
inter-basis interaction edges (C matrix) with significance stars. Now
has signed parameter (auto-detected from class) to render
negative C entries as dashed edges.nmfkc.net.inference(): Statistical inference for
symmetric NMF. Wrapper around nmfkc.inference() with
A = t(X). Returns off-diagonal C coefficients with sandwich
SE and wild bootstrap.nmfkc(Y, Y.symmetric = "bi"|"tri"):
Deprecated in favor of
nmfkc.net(Y, type = "bi"|"tri"). The old implementation
uses a one-sided gradient approximation that empirically converges for
but is theoretically incorrect and does not extend to signed . The
deprecated branch still works in v0.6.8 (with a deprecation warning) and
will be removed in a future release.nmf.sem.DOT(): weight_scale_y2f →
weight_scale_c2, weight_scale_fy1 →
weight_scale_x1 (matrix-name-based naming, consistent with
nmfae.DOT() and nmfkc.DOT()).nmf.sem.DOT(): sig.level moved to after
threshold for consistency with other .DOT
functions.@title /
@description updated to use NMF-FFB as the
canonical model name (with “(formerly NMF-SEM)” attached on first
mention for discoverability of the legacy term). File names
(R/nmf.sem.R,
vignettes/nmf-sem-with- nmfkc.Rmd,
man/nmf.sem.Rd), function names (nmf.sem*),
and S3 classes ("nmf.sem") are unchanged so URLs and
existing scripts continue to work.fitted.nmfae() and residuals.nmfae()
S3 methods; previously fitted() on an nmfae
object silently returned NULL because the wrong field name
($XB instead of $Y1hat) was used.Basis / Covariate columns (was
Factor/Exogenous in
nmf.sem.inference(),
Decoder/Encoder in
nmfae.inference()).wild.B = 500,
wild.seed = 123 across all inference functions..DOT functions renamed to
result for consistency.nfolds, seed,
shuffle) moved to ... in
nmfkc.ecv(), nmfae.ecv(),
nmfae.cv(), nmf.sem.cv(); div
also accepted for backward compatibility.nmfkc.criterion(): Extracted criterion computation from
nmfkc() as a standalone exported function. Supports
detail = "full" / "fast" /
"minimal" to control computation cost.nmfre.inference(): Separated statistical inference from
nmfre() optimization. Returns coefficient table with SE,
z-values, and p-values via wild bootstrap.nmf.sem.inference(): Statistical inference for the C2
parameter matrix in NMF-SEM. Uses sandwich SE and wild bootstrap.coef(), fitted(),
residuals() for all model classes (nmfkc,
nmfae, nmfre, nmf.sem).plot() for nmfre and
nmf.sem (convergence diagnostics).summary.nmf.sem(): Stability diagnostics, fit
statistics, and C2 coefficient table.nmfkc(), nmfkc.rank():
save.time / save.memory →
detailnmfae(): Q → rank,
R → rank.encodernmfre(): Q → rank,
dfU.cap.rate → df.ratenmfre.dfU.scan(), nmfkc.ar.degree.cv():
Q → ranknmfkc.residual.plot(): Y_XB_palette →
fitted.palette, E_palette →
residual.palettenmfkc.kernel.beta.nearest.med():
block_size → block.size,
sample_size → sample.sizehide.isolated option added to all .DOT
functions (default TRUE).nmf.sem.DOT(): Added sig.level parameter;
C2 edges decorated with significance stars.nmfkc(): Added X.restriction = "none"
option and X.init = "kmeansar" initialization.@section Lifecycle: Experimental added to
nmfae().mc.cores parallel option from
nmfae.ecv() for CRAN compliance.T shadowing TRUE in
information criterion computation.nmfkc.ecv() to use KL divergence for evaluation
when method="KL".save.time=TRUE) to
nmfkc.ecv() inner calls.nmfkc.rank() elbow normalization
when R-squared values are identical.rank → Q)
in nmfkc.rank() call to nmfkc.ecv().nmf.sem.split() when P=2.n.exogenous in
nmf.sem.split().summary.nmfkc() and
print.summary.nmfkc().@return for plot.nmfkc() and
predict.nmfkc().@return items (method,
n.missing, n.total, rank,
mae) to nmfkc().T/F with
TRUE/FALSE.1:length() with seq_along().nmf.sem.cv() defaults with
nmf.sem().Harmonized all DOT-generating functions
(nmf.sem.DOT, nmfkc.DOT,
nmfkc.ar.DOT) for consistent structure, naming conventions,
and visualization logic.
Standardized node and edge formatting rules, including unified cluster behavior, color schemes, and edge-scaling conventions.
Implemented threshold-aware coefficient labeling so that displayed numerical precision aligns with the visualization threshold, preventing misleadingly detailed labels.
Removed unused or redundant DOT fragments and improved compatibility across Graphviz engines.
Enhanced layout readability through consistent indentation, node
grouping, and suppression of isolated nodes in specific visualization
modes (e.g., type = "YA" in
nmfkc.DOT).
Refactored and expanded internal DOT helper functions
(.nmfkc_dot_format_coef,
.nmfkc_dot_digits_from_threshold,
.nmfkc_dot_cluster_nodes, etc.) for better maintainability
and uniform behavior.
New Function: Implemented
nmfkc.ecv() for Element-wise Cross-Validation (Wold’s
CV).
rank to evaluate multiple
ranks simultaneously.Missing Value & Weight Support:
nmfkc() and nmfkc.cv() now fully support
missing values (NA) and observation weights via the hidden
argument Y.weights (passed through ...).Y contains NAs, they are automatically
detected and masked (assigned a weight of 0) during optimization.Rank Selection Diagnostics
(nmfkc.rank):
save.time defaults to FALSE, enabling the
robust Element-wise CV calculation by default.Argument Standardization:
rank across all
functions (nmfkc, nmfkc.cv,
nmfkc.ecv, nmfkc.rank).Q is still supported for backward
compatibility but internally mapped to rank.Summary Improvements:
summary() and print() methods to
report:
Other Improvements:
nmfkc.ar() to ensure the
input Y has no missing values (as they cannot be propagated
to the covariate matrix A in VAR models).nmfkc.residual.plot() layout margins for better
visibility of titles.Regularization Update:
The regularization scheme has been revised from L2
(ridge) to L1 (lasso-type) penalties.
gamma now controls the L1 penalty on the
coefficient matrix ( B = C A ), promoting sparsity in
sample-wise coefficients.lambda has been added to control the
L1 penalty on the parameter matrix ( C ), encouraging
sparsity in the shared template structure....) to
nmfkc() and related functions.Function Signature Simplification:** Many less-frequently used
arguments in nmfkc() (e.g., gamma,
X.restriction, X.init) and in
nmfkc.cv() (e.g., div, seed) have
been moved into the ellipsis (...) for a cleaner function
signature.
Performance Improvement: The internal function
.silhouette.simple was vectorized and optimized to reduce
computational cost, particularly for the calculation of
a(i) and b(i).
Removed the fast.calc option from the
nmfkc() function.
Added the X.init argument to the
nmfkc() function, allowing selection between
'kmeans' and 'nndsvd' initialization
methods.
The penalty term has been changed from tr(CC') to
tr(BB') = tr(CAA'C').
Implemented the internal .z and xnorm
functions.
Added the fast.calc option to the nmfkc()
function.
Optimized internal calculations for improved performance.
Updated citation("nmfkc") and added AIC/BIC to the
output.
Implemented the nmfkc.ar.stationarity()
function.
Modified the z() function.
Used crossprod() for faster matrix
multiplication.
Implemented the nmfkc.ar.DOT() function.
Added logic to sort the columns of X to form a unit
matrix in special cases.
Implemented nmfkc.kernel.beta.cv() and
nmfkc.ar.degree.cv() functions.
Set the default column names of X to
Basis1, Basis2, etc.
Added X.prob and X.cluster to the
return object.
Skipped CPCC and silhouette calculations when
save.time = TRUE.
Added a prototype for the nmfkc.ar()
function.
Added the criterion argument to the
nmfkc() function to support multiple criteria.
Updated the nmfkc.rank() function.
Added the criterion argument to the
nmfkc.rank() function.
Implemented the save.time argument.
Implemented the nmfkc.rank() function.
Implemented the nstart option from the
kmeans() function.
Added an experimental implementation of the
nmfkc.rank() function.
Removed zero-variance columns and rows with a warning.
Added source and references to the documentation.
Renamed several components for clarity:
nmfkcreg to nmfkccreate.kernel to nmfkc.kernelnmfkcreg.cv to nmfkc.cvP to B.probcluster to B.clusterunit to X.columntrace to print.tracedims to print.dimsAdded the r.squared argument to the
nmfkcreg.cv() function.
In nmfkcreg():
dims argument to check matrix sizes.unit argument to normalize the basis matrix
columns.Modified the create.kernel() function to support
prediction.
Updated examples on GitHub.
Removed the YHAT return value; use XB
instead.
Added the cluster return value for hard
clustering.