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nnR 0.2.0

New features

• Added Pnm() for the general neural-network polynomials in Definition 2.27 of Rafi, Padgett, and Nakarmi (2024).
• Added column-batch evaluation to inst() and reusable realizations through realize_nn().
• Added stk_many() and nn_sum_many() for efficient n-ary algebraic operations. Unequal-depth realization identities are documented as ReLU-specific because their padding uses tunnel networks.
• Generalized MC() from one-dimensional inputs to the full multidimensional construction in Definition 2.39, including the valid one-sample case.
• Added validate_nn() and repaired draw_nn().

Correctness and API

• is_nn() now verifies numeric, finite, dimensionally connected layers.
• Sum(n, k) now supports every positive n and k, as its definition requires.
• Prd(q, eps) now allocates the square-network error budget using eps / (2^(q - 1) + 1).
• Approximation scaling is computed in the log domain and extreme parameters now fail with a direct floating-point-range error instead of producing non-finite network weights.
• Composition reports incompatible end widths directly.
• Infix algebraic operators are now explicitly exported, while implementation helpers remain internal.
• The previously auto-exported implementation objects A, B, A_k, C_k, and ck are no longer public. Use Phi() or Phi_k() instead.

Performance

• Power networks are constructed iteratively without duplicate recursion.
• Taylor polynomials reuse a single power sequence and n-ary sum.
• Activations and realization are vectorized over complete matrices.
• Copy, sum, identity, tunnel, norm, maximum, and maximum-convolution constructors avoid repeated matrix growth.
• Removed package-internal source() calls and duplicate infix implementations.
• Added an R CMD check workflow and modernized artifact upload for the paper workflow.

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.