docs(algorithms,types): standardize JSDoc @typeparam to @template

Mearman · Mearman · commit 99c3d0e377c3 · 2026-01-11T20:38:53.000Z
Replace non-standard @typeparam with standard @template tag for generic type documentation across algorithms and types packages. This improves compatibility with documentation generators.
diff --git a/packages/algorithms/src/clustering/infomap.ts b/packages/algorithms/src/clustering/infomap.ts
@@ -48,8 +48,8 @@ interface Transition {
  *
  * Infomap uses information theory to find communities by minimizing the
  * description length (map equation) of random walks on the network.
- * @typeParam N - Node type
- * @typeParam E - Edge type
+ * @template N - Node type
+ * @template E - Edge type
  * @param graph - Input graph (directed or undirected)
  * @param options - Optional configuration
  * @param options.weightFn - Weight function for edges (default: all edges weight 1.0)
diff --git a/packages/algorithms/src/clustering/label-propagation.ts b/packages/algorithms/src/clustering/label-propagation.ts
@@ -30,8 +30,8 @@ import type { WeightFunction } from '../types/weight-function';
  * Fast semi-supervised clustering algorithm that propagates labels through
  * the network based on neighbor voting. Nodes iteratively adopt the most
  * frequent label among their neighbors.
- * @typeParam N - Node type
- * @typeParam E - Edge type
+ * @template N - Node type
+ * @template E - Edge type
  * @param graph - Input graph (directed or undirected)
  * @param options - Optional configuration
  * @param options.weightFn - Weight function for edges (default: all edges weight 1.0)
diff --git a/packages/algorithms/src/clustering/leiden.ts b/packages/algorithms/src/clustering/leiden.ts
@@ -40,8 +40,8 @@ interface InternalCommunity {
  *
  * The Leiden algorithm improves upon Louvain by adding a refinement phase that
  * guarantees all detected communities are connected subgraphs.
- * @typeParam N - Node type
- * @typeParam E - Edge type
+ * @template N - Node type
+ * @template E - Edge type
  * @param graph - Input graph (directed or undirected)
  * @param options - Optional configuration
  * @param options.weightFn - Weight function for edges (default: all edges weight 1.0)
diff --git a/packages/algorithms/src/clustering/louvain.ts b/packages/algorithms/src/clustering/louvain.ts
@@ -119,8 +119,8 @@ export const shuffle = <T>(array: T[], seed?: number): T[] => {
  *
  * The Louvain method is a greedy optimization method that attempts to optimize
  * the modularity of a partition of the network.
- * @typeParam N - Node type
- * @typeParam E - Edge type
+ * @template N - Node type
+ * @template E - Edge type
  * @param graph - Input graph (directed or undirected)
  * @param options - Optional configuration (combines legacy and optimization parameters)
  * @param options.weightFn - Weight function for edges (default: all edges weight 1.0) [Legacy]
diff --git a/packages/algorithms/src/decomposition/biconnected.ts b/packages/algorithms/src/decomposition/biconnected.ts
@@ -276,8 +276,8 @@ const extractRemainingComponent = (state: TarjanState): BiconnectedComponent<str
  * 3. Detect articulation points: low[child] ≥ disc[v]
  * 4. Extract components by popping edge stack on backtrack
  * 5. Handle disconnected graphs (DFS from each unvisited node)
- * @typeParam N - Node type
- * @typeParam E - Edge type
+ * @template N - Node type
+ * @template E - Edge type
  * @param graph - Undirected graph to analyze
  * @returns Result containing biconnected components and articulation points, or error
  * @example
diff --git a/packages/algorithms/src/decomposition/core-periphery.ts b/packages/algorithms/src/decomposition/core-periphery.ts
@@ -49,8 +49,8 @@ export interface CorePeripheryOptions {
  * Identifies a dense core of highly connected nodes and a sparse periphery.
  * Core nodes have high coreness scores (> threshold) and are densely connected to each other.
  * Periphery nodes have low coreness scores and are sparsely connected.
- * @typeParam N - Node type
- * @typeParam E - Edge type
+ * @template N - Node type
+ * @template E - Edge type
  * @param graph - Input graph (directed or undirected)
  * @param options - Optional parameters (threshold, iterations, epsilon)
  * @returns Result with core-periphery structure or error
diff --git a/packages/algorithms/src/graph/graph.ts b/packages/algorithms/src/graph/graph.ts
@@ -9,8 +9,8 @@ import { Err,Ok, type Result } from '../types/result';
 /**
  * Generic graph data structure supporting both directed and undirected graphs.
  * Uses adjacency list representation for efficient neighbor lookup (O(1) average case).
- * @typeParam N - Node type (must extend Node interface with id and type fields)
- * @typeParam E - Edge type (must extend Edge interface with source, target fields)
+ * @template N - Node type (must extend Node interface with id and type fields)
+ * @template E - Edge type (must extend Edge interface with source, target fields)
  * @example
  * ```typescript
  * type WorkNode = { id: string; type: 'work'; title: string };
diff --git a/packages/algorithms/src/hierarchical/clustering.ts b/packages/algorithms/src/hierarchical/clustering.ts
@@ -294,8 +294,8 @@ const buildDendrogram = <N extends Node>(nodes: N[], merges: MergeStep[], height
 
 /**
  * Perform hierarchical clustering on a graph.
- * @typeParam N - Node type
- * @typeParam E - Edge type
+ * @template N - Node type
+ * @template E - Edge type
  * @param graph - Input graph (directed or undirected)
  * @param options - Configuration options
  * @param options.linkage - Linkage method: 'single', 'complete', or 'average' (default: 'average')
diff --git a/packages/algorithms/src/metrics/cluster-quality.ts b/packages/algorithms/src/metrics/cluster-quality.ts
@@ -28,8 +28,8 @@ import { calculateModularity } from './modularity';
  * - 1.0 = complete graph (all possible edges present)
  * - 0.0 = no internal edges
  * - Higher density indicates tighter community structure
- * @typeParam N - Node type
- * @typeParam E - Edge type
+ * @template N - Node type
+ * @template E - Edge type
  * @param graph - Input graph
  * @param clusterNodes - Set of nodes in the cluster
  * @returns Density score in range [0.0, 1.0]
@@ -93,8 +93,8 @@ export const calculateDensity = <N extends Node, E extends Edge>(graph: Graph<N,
 
 /**
  * Calculate average density across multiple clusters.
- * @typeParam N - Node type
- * @typeParam E - Edge type
+ * @template N - Node type
+ * @template E - Edge type
  * @param graph - Input graph
  * @param clusters - Array of node sets representing clusters
  * @returns Average density score
@@ -130,8 +130,8 @@ export const calculateAverageDensity = <N extends Node, E extends Edge>(graph: G
  * Range: [0.0, 1.0]
  * - Higher coverage means most edges are within communities
  * - Lower coverage means many inter-community edges
- * @typeParam N - Node type
- * @typeParam E - Edge type
+ * @template N - Node type
+ * @template E - Edge type
  * @param graph - Input graph
  * @param clusters - Array of node sets representing clusters
  * @returns Coverage ratio
@@ -191,8 +191,8 @@ export const calculateCoverageRatio = <N extends Node, E extends Edge>(graph: Gr
  *
  * Computes modularity, average conductance, average density, coverage ratio,
  * and cluster count for a complete clustering.
- * @typeParam N - Node type
- * @typeParam E - Edge type
+ * @template N - Node type
+ * @template E - Edge type
  * @param graph - Input graph
  * @param communities - Array of communities
  * @returns Aggregated ClusterMetrics
@@ -234,8 +234,8 @@ export const calculateClusterMetrics = <N extends Node, E extends Edge>(graph: G
  * Update ClusterMetrics with per-community density values.
  *
  * Modifies community objects to include their individual density scores.
- * @typeParam N - Node type
- * @typeParam E - Edge type
+ * @template N - Node type
+ * @template E - Edge type
  * @param graph - Input graph
  * @param communities - Array of communities (modified in place)
  * @example
diff --git a/packages/algorithms/src/metrics/conductance.ts b/packages/algorithms/src/metrics/conductance.ts
@@ -22,8 +22,8 @@ import type { Edge,Node } from '../types/graph';
  * - Lower values indicate better cluster quality (fewer boundary edges)
  * - φ < 0.1 typically indicates strong cluster cohesion
  * - φ > 0.5 indicates weak cluster structure
- * @typeParam N - Node type
- * @typeParam E - Edge type
+ * @template N - Node type
+ * @template E - Edge type
  * @param graph - Input graph
  * @param clusterNodes - Set of nodes in the cluster
  * @returns Conductance score in range [0.0, 1.0]
@@ -112,8 +112,8 @@ export const calculateConductance = <N extends Node, E extends Edge>(graph: Grap
 
 /**
  * Calculate average conductance across multiple clusters.
- * @typeParam N - Node type
- * @typeParam E - Edge type
+ * @template N - Node type
+ * @template E - Edge type
  * @param graph - Input graph
  * @param clusters - Array of node sets representing clusters
  * @returns Average conductance score
@@ -146,8 +146,8 @@ export const calculateAverageConductance = <N extends Node, E extends Edge>(grap
  * Calculate weighted average conductance (weighted by cluster size).
  *
  * Larger clusters have more influence on the average.
- * @typeParam N - Node type
- * @typeParam E - Edge type
+ * @template N - Node type
+ * @template E - Edge type
  * @param graph - Input graph
  * @param clusters - Array of node sets representing clusters
  * @returns Weighted average conductance score
diff --git a/packages/algorithms/src/metrics/modularity.ts b/packages/algorithms/src/metrics/modularity.ts
@@ -25,8 +25,8 @@ import type { Edge,Node } from '../types/graph';
  * - Higher values indicate stronger community structure
  * - Q > 0.3 typically indicates significant community structure
  * - Q < 0 indicates worse than random partitioning
- * @typeParam N - Node type
- * @typeParam E - Edge type
+ * @template N - Node type
+ * @template E - Edge type
  * @param graph - Input graph
  * @param communities - Community assignments
  * @returns Modularity score in range [-0.5, 1.0]
@@ -155,8 +155,8 @@ export const calculateModularity = <N extends Node, E extends Edge>(graph: Graph
  * Calculate modularity contribution for a single community.
  *
  * Used for incremental modularity updates during optimization.
- * @typeParam N - Node type
- * @typeParam E - Edge type
+ * @template N - Node type
+ * @template E - Edge type
  * @param graph - Input graph
  * @param community - Community to evaluate
  * @param totalEdges - Total number of edges in graph
diff --git a/packages/algorithms/src/partitioning/spectral.ts b/packages/algorithms/src/partitioning/spectral.ts
@@ -24,8 +24,8 @@ import type { WeightFunction } from '../types/weight-function';
  *
  * Divides graph into k balanced partitions by analyzing the spectral properties
  * of the graph Laplacian matrix.
- * @typeParam N - Node type
- * @typeParam E - Edge type
+ * @template N - Node type
+ * @template E - Edge type
  * @param graph - Input graph (directed or undirected)
  * @param k - Number of partitions to create
  * @param options - Optional configuration
diff --git a/packages/algorithms/src/types/algorithm-results.ts b/packages/algorithms/src/types/algorithm-results.ts
@@ -3,7 +3,7 @@ import { type Edge,type Node } from './graph';
 /**
  * Result from graph traversal algorithms (DFS, BFS).
  * Contains visit order, parent relationships, and timing metadata.
- * @typeParam N - Node type
+ * @template N - Node type
  * visitOrder - Nodes in the order they were visited
  * parents - Parent relationship map (node ID → parent ID or null for root)
  * discovered - Discovery timestamps (DFS only, undefined for BFS)
@@ -34,8 +34,8 @@ export interface TraversalResult<N extends Node> {
 /**
  * Path from source to destination node.
  * Contains ordered nodes, connecting edges, and total weight.
- * @typeParam N - Node type
- * @typeParam E - Edge type
+ * @template N - Node type
+ * @template E - Edge type
  * nodes - Nodes in path order (source to destination)
  * edges - Edges connecting consecutive nodes
  * totalWeight - Sum of edge weights
@@ -67,7 +67,7 @@ export interface Path<N extends Node, E extends Edge> {
 /**
  * Connected component of nodes.
  * Represents a maximal set of mutually reachable nodes.
- * @typeParam N - Node type
+ * @template N - Node type
  * id - Component identifier (0-indexed)
  * nodes - Nodes in this component
  * size - Number of nodes in component
@@ -97,8 +97,8 @@ export interface Component<N extends Node> {
 /**
  * Information about a detected cycle.
  * Contains the nodes and edges that form the cycle.
- * @typeParam N - Node type
- * @typeParam E - Edge type
+ * @template N - Node type
+ * @template E - Edge type
  * nodes - Nodes forming the cycle
  * edges - Edges connecting the cycle nodes
  * @invariant nodes.length >= 2 (at least two nodes form a cycle)
diff --git a/packages/algorithms/src/types/clustering-types.ts b/packages/algorithms/src/types/clustering-types.ts
@@ -495,7 +495,7 @@ export type HierarchicalError =
 
 /**
  * Result of Louvain community detection.
- * @typeParam T - Node identifier type (typically string)
+ * @template T - Node identifier type (typically string)
  * @remarks
  * This interface defines the data structure returned by the Louvain algorithm.
  * The actual function returns `Result<LouvainResult<string>, ClusteringError>`.
diff --git a/packages/algorithms/src/types/option.ts b/packages/algorithms/src/types/option.ts
@@ -1,7 +1,7 @@
 /**
  * Option type for optional values.
  * Discriminated union with `some` field for pattern matching.
- * @typeParam T - Value type
+ * @template T - Value type
  * @example
  * ```typescript
  * const option: Option<number> = Some(42);
@@ -18,7 +18,7 @@ export type Option<T> =
 
 /**
  * Create an Option with a value present.
- * @typeParam T - Value type
+ * @template T - Value type
  * @param value - The present value
  * @returns Option with some: true
  * @example
@@ -31,7 +31,7 @@ export const Some = <T>(value: T): Option<T> => ({ some: true, value });
 
 /**
  * Create an Option with no value.
- * @typeParam T - Value type (inferred from context)
+ * @template T - Value type (inferred from context)
  * @returns Option with some: false
  * @example
  * ```typescript
diff --git a/packages/algorithms/src/types/result.ts b/packages/algorithms/src/types/result.ts
@@ -1,8 +1,8 @@
 /**
  * Result type for operations that can fail.
  * Discriminated union with `ok` field for pattern matching.
- * @typeParam T - Success value type
- * @typeParam E - Error type
+ * @template T - Success value type
+ * @template E - Error type
  * @example
  * ```typescript
  * const result: Result<number, string> = Ok(42);
@@ -19,7 +19,7 @@ export type Result<T, E> =
 
 /**
  * Create a successful Result.
- * @typeParam T - Success value type
+ * @template T - Success value type
  * @param value - The success value
  * @returns Result with ok: true
  * @example
@@ -32,7 +32,7 @@ export const Ok = <T>(value: T): Result<T, never> => ({ ok: true, value });
 
 /**
  * Create a failed Result.
- * @typeParam E - Error type
+ * @template E - Error type
  * @param error - The error value
  * @returns Result with ok: false
  * @example
diff --git a/packages/algorithms/src/utils/csr.ts b/packages/algorithms/src/utils/csr.ts
@@ -11,8 +11,8 @@ import { type Edge,type Node } from '../types/graph';
 
 /**
  * Compressed Sparse Row (CSR) graph representation.
- * @typeParam N - Node type (must extend Node interface)
- * @typeParam E - Edge type (must extend Edge interface)
+ * @template N - Node type (must extend Node interface)
+ * @template E - Edge type (must extend Edge interface)
  * @remarks
  * CSR stores the graph in three typed arrays for optimal memory access:
  *
@@ -100,8 +100,8 @@ export interface CSRGraph<N extends Node, E extends Edge> {
 
 /**
  * Convert a Graph to Compressed Sparse Row (CSR) format.
- * @typeParam N - Node type
- * @typeParam E - Edge type
+ * @template N - Node type
+ * @template E - Edge type
  * @param graph - Input graph to convert
  * @returns CSR representation of the graph
  * @throws {RangeError} If graph is too large for typed arrays (>4 billion nodes or edges)
diff --git a/packages/types/src/traversal-types.ts b/packages/types/src/traversal-types.ts
@@ -22,8 +22,8 @@ export type WeightableEdgeProperty = 'score' | 'weight';
 /**
  * Generic weight function signature.
  * Allows weight calculation based on edge and optionally source/target nodes.
- * @typeParam N - Node type
- * @typeParam E - Edge type
+ * @template N - Node type
+ * @template E - Edge type
  * @example
  * ```typescript
  * // Simple edge property weight
@@ -38,8 +38,8 @@ export type WeightFunction<N, E> = (edge: E, sourceNode: N, targetNode: N) => nu
 
 /**
  * Configuration for how edge weights are calculated.
- * @typeParam N - Node type (default: GraphNodeRecord)
- * @typeParam E - Edge type (default: GraphEdgeRecord)
+ * @template N - Node type (default: GraphNodeRecord)
+ * @template E - Edge type (default: GraphEdgeRecord)
  * @example
  * ```typescript
  * // Use score property as weight
@@ -98,8 +98,8 @@ export type TraversalDirection = 'outbound' | 'inbound' | 'both';
  *
  * Combines filtering, weight configuration, and traversal parameters
  * for flexible graph exploration.
- * @typeParam N - Node type (default: GraphNodeRecord)
- * @typeParam E - Edge type (default: GraphEdgeRecord)
+ * @template N - Node type (default: GraphNodeRecord)
+ * @template E - Edge type (default: GraphEdgeRecord)
  * @example
  * ```typescript
  * // Find shortest weighted path through authors only
