Changes in version 0.9.1 (2026-02-27)                  

New Generics

  - as_dist() — S3 generic for converting objects (e.g., fitted models)
    into dist objects. Identity method for dist; designed as an
    extension point for downstream packages

Unified Fallback Layer

  - realized_dist subclass of empirical_dist — preserves provenance
    (source distribution, sample count) when materializing via Monte
    Carlo
  - ensure_realized() internal memoized entry point — all MC fallback
    methods now share cached samples (calling cdf(e) + density(e) on the
    same edist no longer draws independent samples)
  - conditional.dist and rmap.dist now route through ensure_realized()
    for consistent provenance

New Simplification Rules

  - Uniform(a,b) + c → Uniform(a+c, b+c) (location shift)
  - Uniform(a,b) - c → Uniform(a-c, b-c) (location shift)
  - c * Uniform(a,b) → Uniform(min(ca,cb), max(ca,cb)) for c ≠ 0
  - c * Weibull(k,λ) → Weibull(k, c*λ) for c > 0
  - c * ChiSq(df) → Gamma(df/2, 1/(2c)) for c > 0
  - c * LogNormal(μ,σ) → LogNormal(μ+log(c), σ) for c > 0
  - LogNormal * LogNormal → LogNormal(μ₁+μ₂, √(σ₁²+σ₂²))
  - -Uniform(a,b) → Uniform(-b, -a) (unary negation)

New Operators

  - /.dist — Division operator: dist / scalar delegates to scalar
    multiplication rules; scalar / dist and dist / dist create edist

MVN Algebra

  - conditional.mvn — closed-form Schur complement conditioning with
    given_indices/given_values, or predicate-based MC fallback
  - affine_transform(x, A, b) — compute AX + b for normal/MVN
    distributions (exact)

Mixture Multivariate

  - marginal.mixture — marginal of mixture is mixture of marginals
    (exact)
  - conditional.mixture — Bayes' rule weight update for mixture-of-MVN
    conditioning, with predicate-based MC fallback

Limiting Distributions

  - clt(base_dist) — CLT limiting distribution: Normal(0, Var) or MVN(0,
    Σ)
  - lln(base_dist) — LLN degenerate limit: Normal(μ, 0) or MVN(μ, 0)
  - delta_clt(base_dist, g, dg) — delta method with user-supplied
    derivative/Jacobian
  - normal_approx(x) — moment-matching normal approximation of any
    distribution

                        Changes in version 0.2.0                        

New Distributions

  - gamma_dist(shape, rate) — Gamma distribution with hazard/survival
    functions
  - weibull_dist(shape, scale) — Weibull distribution with closed-form
    hazard
  - chi_squared(df) — Chi-squared distribution with hazard/survival
    functions
  - uniform_dist(min, max) — Uniform distribution on [min, max]
  - beta_dist(shape1, shape2) — Beta distribution on (0, 1)
  - lognormal(meanlog, sdlog) — Log-normal distribution with
    hazard/survival
  - poisson_dist(lambda) — Poisson distribution with exact expectation
    via truncated summation
  - mixture(components, weights) — Mixture distributions with law of
    total variance

New Operators and Algebra

  - *.dist — Scalar multiplication (c * dist, dist * c, dist * dist)
  - ^.dist — Power operator (dist ^ n)
  - Math.dist — Group generic for exp(), log(), sqrt(), abs(), etc.
  - Summary.dist — Group generic for sum(), prod(), min(), max()
  - Extended +.dist and -.dist for numeric location shifts

Simplification Rules

  - c * Normal(mu, v) simplifies to Normal(c*mu, c^2*v)
  - c * Gamma(a, r) simplifies to Gamma(a, r/c) for c > 0
  - c * Exponential(r) simplifies to Gamma(1, r/c) for c > 0
  - Normal(mu, v) + c simplifies to Normal(mu+c, v)
  - Gamma(a1, r) + Gamma(a2, r) simplifies to Gamma(a1+a2, r) (same
    rate)
  - Exp(r) + Exp(r) simplifies to Gamma(2, r) (same rate)
  - ChiSq(d1) + ChiSq(d2) simplifies to ChiSq(d1+d2)
  - Poisson(l1) + Poisson(l2) simplifies to Poisson(l1+l2)
  - Normal(0,1)^2 simplifies to ChiSq(1)
  - exp(Normal(mu, v)) simplifies to LogNormal(mu, sqrt(v))
  - log(LogNormal(ml, sl)) simplifies to Normal(ml, sl^2)
  - min(Exp(r1), ..., Exp(rk)) simplifies to Exp(sum(r))

New Infrastructure

  - realize() generic — materialize any distribution to empirical_dist
    by sampling
  - Auto-fallback methods for edist: cdf, density, sup, conditional,
    rmap, inv_cdf
  - countable_set R6 class for countably infinite support (Poisson)
  - inv_cdf.empirical_dist — quantile function for empirical
    distributions

Improvements

  - Informative error messages in all constructors (replaced stopifnot)
  - format() methods for all distribution types
  - Standardized print() methods delegating to format()
  - Fixed vcov.exponential — was returning rate instead of 1/rate^2
  - Fixed sampler.edist crash when n=1
  - conditional.empirical_dist gives informative error on zero matches
  - Zero-variance guard in expectation_data() CI computation

                 Changes in version 0.1.0 (2025-12-17)                  

  - Initial CRAN release.

Features

  - Core distribution types: normal, mvn, exponential, empirical_dist
  - Expression distributions (edist) for lazy composition of
    distributions
  - Algebraic operations (+, -) on distributions with automatic
    simplification
  - Support classes: finite_set, interval for representing distribution
    domains
  - Generic methods: sampler, mean, vcov, density, cdf, params
  - Monte Carlo estimation for expectation, conditional, and rmap
    operations