V1.1.0

[Grochocinski]

Overview

This PR overhauls the mrg32k3a random number generator to significantly improve performance, reduce redundancy, and modernize the internal structure. Highlights:

• 30–40% faster distribution sampling and RNG calls
• ~95% test coverage across all streams and variate generators
• Removed outdated components and streamlined internal logic
• Added GitHub actions to ensure compatibility and consistency between Python and Numpy versions

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Key Changes

mrg32k3a

• Replaced math-heavy logic with faster, minimal-overhead equivalents using Python's built-in types and operators
• Precomputed transition matrices (A[1|2]p[47|94|141]) and constants (mrgm1_plus_1, etc.) to eliminate redundant computation
• Internal stream states now use np.ndarray for efficient slicing and modular matrix math, while seed() still accepts a Python tuple
• Centralized and deduplicated modular state transitions via _advance_state() and _apply_transition() helpers
• Simplified stream/substream/subsubstream advancement logic
• Ensured consistency between return values and type hints throughout the codebase

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Testing

• Expanded test suite to include:
  • Stream reset and advancement behavior
  • Random variate correctness and distribution validation
  • Vector generation summation and bounds checking
• Tests reorganized by class for better structure and readability
• Achieved ~95% line coverage of generator logic
• Added GitHub actions to automatically run the testing suite with all supported versions of Python (minus 3.5 which had dependency issues)

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Performance Summary

Component	Change
RNG core (random())	+32%
BSM (normal quantile approx)	+16%
Normal Variate Generation	+18%
Lognormal Variate Generation	+17%
Poisson Variate Generation	+22%
Gumbel Variate Generation	+39%
Binomial Variate Generation	+57%
Integer random vector (simplex)	+16%
Continuous random vector	+13–14%
Stream advancement (start_*)	+7–9%
Reset methods (reset_*)	−93% to −110% (See note below)
Start Fixed S_SS_SSS	+6–17%

Note on regressions:
The reset_*() methods show a microbenchmark-level regression due to the addition of a tuple() conversion from NumPy arrays. This cost is on the order of microseconds, and resets are typically infrequent in most applications. Overall performance remains excellent even in scenarios with frequent resets.

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Summary

This refactor delivers a leaner, faster, and more maintainable implementation of mrg32k3a, with significant speedups in all key random variate generation paths and much broader test coverage. Ready for review and merge.

[Grochocinski]

Speed Up Testing Results (TLDR ~30% Improvement)

# Values	Processor (Architecture)	Old Avg (sec)	New Avg (sec)	Improvement
1M	Snapdragon X1P-42-100 (ARM64)	2.79	1.88	32.54%
1M	AMD 5900X (AMD64)	1.29	0.91	29.91%
10M	Snapdragon X1P-42-100 (ARM64)	13.59	9.35	33.10%
10M	AMD 5900X (AMD64)	2.79	1.88	31.14%

sample size of 10 runs each, executed in randomized order

[Grochocinski]

Alright, I think it's actually good to go at this point (passes all tests / Ruff formatting/linting checks / type checks, as well as showing substantial performance uplift and having easy-to-read code)