Make EigenSNP optional

Cargo.toml

@@ -70,6 +70,8 @@ backend_mkl_system = ["dep:ndarray-linalg", "ndarray-linalg/intel-mkl-system"]
 backend_faer = ["dep:faer"]
 faer_links_ndarray_static_openblas = ["backend_faer", "backend_openblas"]
 
+eigensnp = [] # Requires nightly Rust
+
 # --- Utility Features ---
 jemalloc = ["dep:jemalloc-ctl"]
 enable-eigensnp-diagnostics = []

README.md

@@ -118,10 +118,16 @@ Loads a PCA model from a file previously saved with `save_model`. The loaded mod
 
 EigenSNP is a sophisticated Principal Component Analysis algorithm specifically designed for large-scale genomic datasets, such as those found in biobanks or population-scale studies. It efficiently handles datasets where the number of SNPs (features) significantly exceeds the number of samples.
 
+To use EigenSNP, enable the `eigensnp` feature in your `Cargo.toml` (note that this currently requires the use of nightly Rust):
+```toml
+[dependencies]
+efficient_pca = { version = "*", features = ["eigensnp"] }
+```
+
 ### Key Features
 
 * 🧬 **Genomic-Optimized**: Designed for SNP data with linkage disequilibrium (LD) block structure
-* ⚡ **Scalable**: Uses randomized SVD (RSVD) and memory-efficient f32 precision for large datasets  
+* ⚡ **Scalable**: Uses randomized SVD (RSVD) and memory-efficient f32 precision for large datasets
 * 🔧 **Highly Configurable**: Extensive tuning parameters for different dataset characteristics
 * 📊 **Multi-Stage Algorithm**: Local eigenSNP basis learning → condensed features → global PCA → refinement
 * 🔍 **Diagnostics**: Optional detailed diagnostics for algorithm analysis (with feature flag)

src/lib.rs

@@ -1,17 +1,20 @@
-#![feature(portable_simd)]
+#![cfg_attr(feature = "eigensnp", feature(portable_simd))]
 #![doc = include_str!("../README.md")] // Crate-level documentation
 
 pub mod diagnostics;
-pub mod eigensnp;
 pub mod linalg_backends; // Consolidated module
 pub mod pca;
 
+#[cfg(feature = "eigensnp")]
+pub mod eigensnp;
+
 #[cfg(feature = "enable-eigensnp-diagnostics")]
 pub mod eigensnp_tests;
 
 pub use pca::PCA;
 
 // Re-export key items from the eigensnp module for users of the EigenSNP functionality.
+#[cfg(feature = "eigensnp")]
 pub use eigensnp::{
     CondensedFeatureId, EigenSNPCoreAlgorithm, EigenSNPCoreAlgorithmConfig, EigenSNPCoreOutput,
     LdBlockListId, LdBlockSpecification, PcaReadyGenotypeAccessor, PcaSnpId, PrincipalComponentId,