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nmfkc 0.6.7

Bug Fixes

• Added 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.

Naming Unification (old names remain usable for backward compatibility)

• Coefficient tables: all inference functions now use Basis / Covariate columns (was Factor/Exogenous in nmf.sem.inference(), Decoder/Encoder in nmfae.inference()).
• Wild bootstrap defaults unified: wild.B = 500, wild.seed = 123 across all inference functions.
• First argument of all .DOT functions renamed to result for consistency.
• CV tuning parameters (nfolds, seed, shuffle) moved to ... in nmfkc.ecv(), nmfae.ecv(), nmfae.cv(), nmf.sem.cv(); div also accepted for backward compatibility.

nmfkc 0.6.6

New Functions

• 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.
• S3 methods coef(), fitted(), residuals() for all model classes (nmfkc, nmfae, nmfre, nmf.sem).
• S3 methods plot() for nmfre and nmf.sem (convergence diagnostics).
• summary.nmf.sem(): Stability diagnostics, fit statistics, and C2 coefficient table.

Parameter Renames (old names remain usable for backward compatibility)

• nmfkc(), nmfkc.rank(): save.time / save.memory → detail
• nmfae(): Q → rank, R → rank.encoder
• nmfre(): Q → rank, dfU.cap.rate → df.rate
• nmfre.dfU.scan(), nmfkc.ar.degree.cv(): Q → rank
• nmfkc.residual.plot(): Y_XB_palette → fitted.palette, E_palette → residual.palette
• nmfkc.kernel.beta.nearest.med(): block_size → block.size, sample_size → sample.size

Other Improvements

• hide.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.
• Added arXiv/DOI references to roxygen documentation for all main functions.
• @section Lifecycle: Experimental added to nmfae().
• Removed mc.cores parallel option from nmfae.ecv() for CRAN compliance.

nmfkc 0.6.0

Bug Fixes

• Fixed variable T shadowing TRUE in information criterion computation.
• Fixed nmfkc.ecv() to use KL divergence for evaluation when method="KL".
• Added performance flags (save.time=TRUE) to nmfkc.ecv() inner calls.
• Fixed zero-division in nmfkc.rank() elbow normalization when R-squared values are identical.
• Fixed parameter name mismatch (rank → Q) in nmfkc.rank() call to nmfkc.ecv().
• Fixed descending loop in nmf.sem.split() when P=2.
• Added input validation for n.exogenous in nmf.sem.split().

Documentation

• Added roxygen documentation for summary.nmfkc() and print.summary.nmfkc().
• Added @return for plot.nmfkc() and predict.nmfkc().
• Added missing @return items (method, n.missing, n.total, rank, mae) to nmfkc().

Code Quality

• Replaced T/F with TRUE/FALSE.
• Replaced 1:length() with seq_along().
• Changed default font from Meiryo to Arial in DOT functions.
• Aligned nmf.sem.cv() defaults with nmf.sem().

nmfkc 0.5.8

Graphviz DOT Output Consolidation and Cleanup

• 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).

  • This function randomly masks elements of the observation matrix to evaluate structural reconstruction error.
  • It provides a statistically robust criterion for rank selection, avoiding the monotonic error decrease often seen in standard column-wise CV.
  • Supports vector input for 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 ...).
  • If Y contains NAs, they are automatically detected and masked (assigned a weight of 0) during optimization.

• Rank Selection Diagnostics (nmfkc.rank):

  • Dual-Axis Visualization: The plot now displays fitting metrics (\(R^2\), etc.) on the left axis and ECV Sigma (RMSE) on the right axis (blue line).
  • Automatic Best Rank labeling: The plot explicitly marks the “Best” rank based on two criteria:
    • Elbow: Geometric elbow point of the \(R^2\) curve.
    • Min: Minimum error point of the Element-wise CV.
  • save.time defaults to FALSE, enabling the robust Element-wise CV calculation by default.

• Argument Standardization:

  • Unified the rank argument name to rank across all functions (nmfkc, nmfkc.cv, nmfkc.ecv, nmfkc.rank).
  • The legacy argument Q is still supported for backward compatibility but internally mapped to rank.

• Summary Improvements:

  • Updated summary() and print() methods to report:
    • Sparsity of Basis (\(X\)) and Coefficients (\(B\)).
    • Clustering Entropy (indicating “Crisp” vs “Ambiguous” clustering).
    • Clustering Crispness (Mean Max Probability).
    • Number and percentage of missing values in \(Y\).

• Other Improvements:

  • Added a validation check in nmfkc.ar() to ensure the input Y has no missing values (as they cannot be propagated to the covariate matrix A in VAR models).
  • Refined nmfkc.residual.plot() layout margins for better visibility of titles.
  • Updated documentation to reflect all changes.

• 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.
    
  • A new argument lambda has been added to control the L1 penalty on the parameter matrix ( C ), encouraging sparsity in the shared template structure.
    Both parameters can be passed through the ellipsis (...) 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 nmfkc
  • create.kernel to nmfkc.kernel
  • nmfkcreg.cv to nmfkc.cv
  • P to B.prob
  • cluster to B.cluster
  • unit to X.column
  • trace to print.trace
  • dims to print.dims

• Added the r.squared argument to the nmfkcreg.cv() function.

• In nmfkcreg():

  • Added the dims argument to check matrix sizes.
  • Added the 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.

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.
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