Implement ZTEST: One-Sample t-Test Statistic with validation tests

- Added Ztest class to compute the one-sample t-statistic using sample standard deviation with Bessel correction.
- Implemented validation tests for Ztest to ensure accuracy against manual calculations and PineScript.
- Updated documentation for Ztest, detailing its mathematical foundation, performance profile, and common pitfalls.
- Adjusted NDepend badges to reflect changes in code metrics after implementation.
- Updated missing indicators report to reflect the completion of statistical indicators, including ZTEST.

lib/statistics/mode/Mode.md

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+# MODE: Statistical Mode (Most Frequent Value)
+
+> "The mode is the value that appears most frequently in a data set — the only measure of central tendency that tells you what's actually popular, not what's average."
+
+## Introduction
+
+The **Mode** is a rolling statistical indicator that identifies the most frequently occurring value within
+a sliding window of recent observations. Unlike the mean and median, which find the center of a
+distribution through arithmetic, the mode finds it through frequency counting. For financial data
+this means identifying price levels where the market has spent the most time — a concept with direct
+implications for support/resistance identification.
+
+## Historical Context
+
+The mode predates formal statistics. Early astronomers used it to identify the "true" value among
+repeated measurements. In modern finance, the concept maps directly to volume profile analysis
+(price-at-time histograms) and Point of Control (POC) calculations, though those typically bin
+continuous data while this implementation uses exact value comparison matching the PineScript reference.
+
+## Mathematical Foundation
+
+Given a window of $n$ values $\{x_1, x_2, \ldots, x_n\}$:
+
+$$\text{Mode} = \arg\max_{v} \sum_{i=1}^{n} \mathbf{1}(x_i = v)$$
+
+Where $\mathbf{1}(x_i = v)$ is the indicator function returning 1 when $x_i = v$.
+
+**Special cases:**
+
+- Single value in window: returns that value
+- All values distinct ($n > 1$): returns `NaN` (no mode exists)
+- Multimodal (tie): returns the smallest mode (first in sorted order)
+
+## Architecture
+
+### Sorted Window Approach
+
+The implementation maintains a sorted buffer using `BinarySearch` + `Array.Copy` for O(N) insert/remove.
+After each update, a single linear scan of the sorted buffer identifies the longest consecutive run
+of equal values. This is more efficient than a dictionary approach for small-to-medium periods because
+it avoids hashing overhead and GC pressure from dictionary internals.
+
+### State Management
+
+| Component | Purpose |
+|-----------|---------|
+| `RingBuffer _buffer` | Circular buffer tracking insertion order (for sliding window eviction) |
+| `double[] _sortedBuffer` | Values maintained in sorted order for O(N) mode finding |
+| `double[] _p_sortedBuffer` | Snapshot for `isNew=false` bar correction rollback |
+
+### Complexity
+
+| Operation | Time | Space |
+|-----------|------|-------|
+| `Update` (streaming) | O(N) | O(N) |
+| `Batch` (span) | O(M·N) | O(N) |
+
+Where N = period, M = total data points.
+
+## Parameters
+
+| Parameter | Type | Default | Description |
+|-----------|------|---------|-------------|
+| `period` | `int` | — | Rolling window size (must be > 0) |
+
+## Usage
+
+```csharp
+// Streaming mode
+var mode = new Mode(14);
+TValue result = mode.Update(new TValue(DateTime.UtcNow, price));
+
+// Batch mode
+TSeries results = Mode.Batch(series, 14);
+
+// Span mode (zero-allocation output)
+Mode.Batch(sourceSpan, outputSpan, 14);
+```
+
+## Interpretation
+
+| Condition | Meaning |
+|-----------|---------|
+| Mode = specific value | Market spent most time at this price level |
+| Mode = NaN | All values unique — no dominant price level |
+| Mode stable across windows | Strong support/resistance at that level |
+| Mode shifting | Distribution center is moving |
+
+## Common Pitfalls
+
+1. **Continuous data produces NaN**: Floating-point prices with many decimals rarely repeat exactly. Mode is most useful for rounded/discretized data (e.g., tick prices, integer values).
+2. **Bimodal ties**: When multiple values share the highest frequency, the smallest value wins (first in sorted order). This is deterministic but may not match all statistical software.
+3. **Period = 1**: Always returns the input value (trivially the mode).
+4. **NaN inputs**: NaN values are stored in the buffer. If a window contains NaN duplicates, NaN could become the mode — this matches the PineScript behavior.
+5. **Performance**: O(N) per update due to sorted buffer maintenance. For very large periods (>1000), consider if mode is the right tool.
+
+## Validation
+
+Self-consistency validation only — no external library provides rolling mode.
+Verified against Wolfram Alpha for static datasets.
+
+| Test | Status |
+|------|--------|
+| Wolfram Alpha {1,2,2,3,3,3,4} | ✔️ mode = 3 |
+| Batch == Streaming == Span | ✔️ |
+| Bar correction (isNew=false) | ✔️ |
+
+## References
+
+- PineScript reference: `mode.pine` (exact value comparison, map-based counting)
+- Wolfram MathWorld: [Statistical Mode](https://mathworld.wolfram.com/Mode.html)