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Changelog

Source: NEWS.md

nmfkc 0.5.5

  • 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 (R2R^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 R2R^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 (XX) and Coefficients (BB).
      • Clustering Entropy (indicating “Crisp” vs “Ambiguous” clustering).
      • Clustering Crispness (Mean Max Probability).
      • Number and percentage of missing values in YY.
  • 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.