feat: add analytical derivative to all 1D shapes

[lpatiny]

What

Adds an analytical derivative(x) to every Shape1D class plus standalone <shape>Derivative(...) functions, so consumers can build an exact Jacobian instead of approximating it with finite differences.

• New required Shape1DClass.derivative(x) returning { fct, dx, parameters }, where parameters are the partials w.r.t. each shape parameter in getParameters() order. Exposed via the new exported Shape1DDerivative type.
• Standalone functions next to each fct: gaussianDerivative, lorentzianDerivative, pseudoVoigtDerivative, lorentzianDispersiveDerivative, generalizedLorentzianDerivative, pseudoVoigtTCHDerivative.
• pseudoVoigtDerivative and pseudoVoigtTCHDerivative inline their component math so the hot path allocates a single object (instead of 3 / 2).

Why

ml-spectra-fitting's optimize() can pass these as an analytical jacobianFunction to Levenberg–Marquardt. Measured end-to-end speedup vs the current finite-difference behavior: ~4× (mixed shapes, 42 params) to ~5.8× (all pseudoVoigt, 48 params), same iteration count and final error.

Tests

Each derivative is validated against central finite differences, and each class's derivative() is checked to return parameters in getParameters() order. npm test passes (108 tests, lint, types, prettier).

Note: requires a release of ml-levenberg-marquardt with jacobianFunction support for the downstream analytical-Jacobian path to take effect.

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 93.18%. Comparing base (0ce4bd1) to head (b96b738).

Additional details and impacted files

@@            Coverage Diff             @@
##             main      #92      +/-   ##
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+ Coverage   91.88%   93.18%   +1.29%     
==========================================
  Files          23       23              
  Lines         813      968     +155     
  Branches      194      206      +12     
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+ Hits          747      902     +155     
  Misses         65       65              
  Partials        1        1              

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