ppsds 0.0.2 (private beta)

A private beta, not on PyPI. The second beta: the package stops being only about DRPP. DRPP
answers what can a predictor guarantee when the model is uncertain — a lower bound. This release
adds the other side of that question, how well could anything predict a system known exactly —
an upper bound — and a way to feel the difference before reading any of it. It also rebuilds the
documentation around the question of who each page is for.

Added

• ppsds.predictability — the epsilon-logarithm score's reference values:
  predictability_upper_bound (the best expected score any predictor could reach, a property of
  the system alone), approximation_value (the O(epsilon) approximation for a given
  predictor), differential_entropy / cross_entropy, and trajectory aggregation
  (cumulative_average, across_trajectories).
• EpsilonLogarithmScore — the neighbourhood scoring rule, replacing the previous zero-caller
  stub. It is vectorised, callable, and dispatches on the law it is given: logpdf at
  epsilon = 0, a box_integral method for pmfs, a closed form for diagonal Gaussians, and CDF
  inclusion-exclusion as the general fallback. These paths are tested against each other, not
  against stored constants.
• ppsds.numerics.atom_pmf.AtomPmf — a predictive law as weighted atoms. Its support need not
  contain the true distribution's, which is the point: under the neighbourhood score a sparse
  pmf is a usable prediction, while under the ordinary logarithm score it is -inf almost
  surely.
• ppsds.probabilistic_predictor.pmf — a second kind of predictor beside drpp:
  StaticAtomPmfPredictor and DriftShiftedAtomPmfPredictor, which predict with a pmf you
  supply rather than one a model derives.
• ppsds.dynamics.known_sds.AdditiveDriftSds and ppsds.environment.KnownSdsEnv — a system that
  is known exactly (drift plus a named noise law, non-Gaussian included) and a gymnasium
  environment that steps it. Env still serves scene 2; KnownSdsEnv is the exception.
• Examples reproducing the predictability paper's figure (examples/understand_predictability/) and
  running your own pmf bets as an experiment (experiments/pmf_betting/).
• An interactive page at https://xuzerken.github.io/ppsds/examples/understand_ppsds_by_pmf_betting/ that scores a hand of bets live,
  and a README animation of it. The page replays trajectories exported from
  experiments/pmf_betting/presets.py, and checks its own JavaScript against vectors computed by
  ppsds on every load.
• EpsilonLogarithmScore, AtomPmf, the pmf predictors and the predictability functions are now
  re-exported from the top level.
• docs/assets/map.png — a route map across the four sections, used by the README, the site home
  and tutorials/map.md.
• docs/assets/paper-drpp.png and docs/assets/paper-predictability.png — each paper's own
  vector figure rasterised separately for its Research page.

Changed

• The documentation site is organised as four kinds of node. Home, then Examples (stories
  you can run), Experiments (the same questions at paper scale), Research (the common
  preliminaries and one page per paper, each with its own citation), Tutorials (topics treated
  on their own terms), and API. The old flat shelf (Concepts, Predictability, Examples,
  Developer Guide, Citation) is gone; concepts.md became the Research preliminaries and the
  separate citation page was retired because every paper page now ends with its citation.
• The batch experiments moved out of examples/ into experiments/:
  experiments/drpp/, experiments/pmf_betting/ and experiments/predictability/. Each example
  and experiment directory now carries its own environment.yml, so one page can be run without
  installing the rest. examples/understand_ppsds_by_pmf_betting/ keeps its README: its runnable
  front is the no-install game page.
• The README sells routes, not a page list. It opens with a two-column game block (the
  animation beside the rules that map onto the package), a one-row-per-reader entry table, and
  cross-section routes through Examples, Experiments, Research and Tutorials. The old
  The examples and A predictor on your own system sections were removed: both duplicated pages
  the site now owns.
• The citation and licence sections were merged; contributing is a reader type in the entry table
  rather than a separate section.

Notes

• No number moved. The DRPP experiment was compared against the pre-move commit with
  tools/ab_compare.py; every compared array is bit-identical.
• The epsilon-logarithm score is discontinuous at epsilon = 0, and this is a property of
  the definition rather than an artefact: at exactly zero it is log p(x), while for any
  epsilon > 0 it carries a d log(2 epsilon) volume term and runs to -inf as epsilon
  shrinks. Subtract that term to recover the ordinary logarithm score; do not take a limit.
• multivariate_uniform gained a batch-aware pdf / cdf and an optional rng for rvs. The
  default rvs path is unchanged, and the DRPP numbers are bit-identical.