feat: v0.4.0 package restructuring — split into 4 modules

Separate monolithic beta_spectrum package into:
  beta_spectrum   — pure theory (components, spectrum, CLI, visualize)
  exp_data        — experimental data handling (spectrum, calibration,
                    fitters, corrections)
  fitter          — analysis framework (model, fit_engine, extractor,
                    result)
  beta_spectrum.visualize — plotting (BetaSpectrumAnalyzer)

Phase 1 — exp_data module:
  ExpSpectrum dataclass with metadata, normalization, serialization
  EnergyCalibrator (linear/quadratic calibration)
  GaussianFitter + PeakFitter for calibration sources
  DeadTimeCorrection, PileUpCorrection, BackgroundSubtractor
  43 new tests, ruff + mypy clean

Phase 2 — fitter module:
  SpectrumModel wraps BetaSpectrum + DetectorResponse with convolution
  SpectrumFitter with χ² minimization, covariance, profile likelihood,
  confidence intervals, robust loss functions
  CWExtractor + GVAExtractor for C(W) and g_V/g_A extraction
  AnalysisResult + FitSummary for physics-specific results
  CurveFitter general-purpose callable model fitter
  34 new tests, ruff + mypy clean

Phase 3 — clean up beta_spectrum:
  Remove cw_extractor.py and fitter.py (moved to fitter/)
  Clean __init__.py to export only theory classes
  Add backward-compat methods to CWExtractor
  Fix BetaSpectrumAnalyzer._get_branch_spectrum returning BetaSpectrum
  objects instead of numpy arrays (plot fix)

Phase 4 — package structure:
  Auto-discovered by setuptools (4 packages)
  All imports updated across 26 source files
  Black formatting applied
  322 tests pass (262 original + 60 new)
  ruff + mypy clean on all source files

Bug fixes:
  - Fix multi-branch spectrum plotting: universal component keys
    (Fermi, Screening, etc.) don't have per-branch prefix; fixed
    fallback lookup.
  - Fix negative phase space beyond endpoint: BetaSpectrum returns
    negative values for W > W0; clamp branch spectra to zero.

tests/fitter/test_result.py

@@ -0,0 +1,100 @@
+"""Tests for fitter.result."""
+
+import numpy as np
+
+from fitter.result import AnalysisResult, FitSummary
+
+
+class MockFitResult:
+    """Mock FitResult for testing AnalysisResult."""
+
+    def __init__(self):
+        self.success = True
+        self.chi2 = 12.5
+        self.n_points = 100
+        self.n_free = 3
+        self.parameters = np.array([5.0, 0.1, 2.0])
+        self.covariance = np.array(
+            [
+                [0.01, 0.0, 0.0],
+                [0.0, 0.001, 0.0],
+                [0.0, 0.0, 0.04],
+            ]
+        )
+        self.model_values = np.ones(100)
+        self.residuals = np.zeros(100)
+        self.message = "Optimization terminated successfully."
+
+    @property
+    def chi2_per_dof(self):
+        return self.chi2 / (self.n_points - self.n_free)
+
+    @property
+    def p_value(self):
+        from scipy.stats import chi2
+
+        return 1.0 - chi2.cdf(self.chi2, self.n_points - self.n_free)
+
+    @property
+    def parameters_with_errors(self):
+        errors = np.sqrt(np.diag(self.covariance))
+        return {
+            f"p{i}": (float(self.parameters[i]), float(errors[i]))
+            for i in range(len(self.parameters))
+        }
+
+
+class TestFitSummary:
+    """Test FitSummary."""
+
+    def test_str(self):
+        summary = FitSummary(
+            nuclide="Tc99",
+            converged=True,
+            chi2_per_dof=1.234,
+            p_value=0.0567,
+            parameters={"norm": (5.0, 0.1), "bg": (0.1, 0.01)},
+        )
+        text = str(summary)
+        assert "Tc99" in text
+        assert "1.234" in text
+        assert "5.0" in text
+        assert "Converged: True" in text
+
+
+class TestAnalysisResult:
+    """Test AnalysisResult."""
+
+    def test_summary(self):
+        mock = MockFitResult()
+        result = AnalysisResult(
+            fit_result=mock,
+            nuclide="Tc99",
+            metadata={"param_names": ["norm", "bg", "endpoint"]},
+        )
+        summary = result.summary
+        assert summary.nuclide == "Tc99"
+        assert summary.converged is True
+        assert summary.chi2_per_dof > 0
+
+    def test_to_dict(self):
+        mock = MockFitResult()
+        result = AnalysisResult(
+            fit_result=mock,
+            nuclide="Tc99",
+            gv_eff=0.97,
+            ga_eff=0.57,
+            endpoint_keV=294.0,
+        )
+        d = result.to_dict()
+        assert d["nuclide"] == "Tc99"
+        assert d["gv_eff"] == 0.97
+        assert d["ga_eff"] == 0.57
+        assert d["endpoint_keV"] == 294.0
+        assert d["converged"] is True
+
+    def test_empty_result(self):
+        mock = MockFitResult()
+        result = AnalysisResult(fit_result=mock)
+        summary = result.summary
+        assert summary.nuclide == ""