[ENH]: Addition of initial ASV benchmarking suite with synthetic data

[KaranSinghDev]

Fixes #471

This PR implements a formal benchmarking infrastructure using Airspeed Velocity (asv). This fulfills the long-standing request mentioned in #99 to provide a mechanism for tracking performance history and preventing speed regressions.

Implementation

1. Infrastructure: Added asv.conf.json configured for isolated virtualenv builds and direct pip installation for cross-platform stability.
2. Synthetic Data Strategy: Benchmarks generate data in-memory using numpy to avoid dependencies on pandas or external CSV files and ensures we measure algorithmic throughput rather than disk I/O speed.
3. Thread Isolation: Explicitly set OMP_NUM_THREADS=1 and MKL_NUM_THREADS=1 to ensure timing remains consistent across different hardware configurations.
4. Initial Suites:
   • LinearGAMFit: Measures fitting, prediction, and gridsearch latency.
   • PoissonGAMFit: Benchmarks the performance of the iterative PIRLS loop using a stable mathematical signal to ensure rapid convergence and prevent timeouts.

Verification

I have thoroughly verified in an isolated environment. All benchmarks execute in under 300ms, ensuring the suite is fast enough for CI/CD integration.

Sample Local Output:

· Discovering benchmarks
· Running 4 total benchmarks (1 commits * 1 environments * 4 benchmarks)
[ 62.50%] ··· bench_fit.LinearGAMFit.time_fit           83.3±10ms
[ 75.00%] ··· bench_fit.LinearGAMFit.time_gridsearch    280±200ms
[ 87.50%] ··· bench_fit.LinearGAMFit.time_predict       17.8±4ms
[100.00%] ··· bench_fit.PoissonGAMFit.time_fit         202±100ms

The tests have been done on Python 3.10/3/13. Linux (Ubuntu), with CPU: i7 12700h.