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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
packagesrealized_dist subclass of empirical_dist —
preserves provenance (source distribution, sample count) when
materializing via Monte Carloensure_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 provenanceUniform(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 ≠ 0c * Weibull(k,λ) → Weibull(k, c*λ) for c
> 0c * ChiSq(df) → Gamma(df/2, 1/(2c)) for c
> 0c * LogNormal(μ,σ) →
LogNormal(μ+log(c), σ) for c > 0LogNormal * LogNormal →
LogNormal(μ₁+μ₂, √(σ₁²+σ₂²))-Uniform(a,b) → Uniform(-b, -a) (unary
negation)/.dist — Division operator: dist / scalar
delegates to scalar multiplication rules; scalar / dist and
dist / dist create edistconditional.mvn — closed-form Schur complement
conditioning with given_indices/given_values,
or predicate-based MC fallbackaffine_transform(x, A, b) — compute AX + b for
normal/MVN distributions (exact)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 fallbackclt(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/Jacobiannormal_approx(x) — moment-matching normal approximation
of any distributiongamma_dist(shape, rate) — Gamma distribution with
hazard/survival functionsweibull_dist(shape, scale) — Weibull distribution with
closed-form hazardchi_squared(df) — Chi-squared distribution with
hazard/survival functionsuniform_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/survivalpoisson_dist(lambda) — Poisson distribution with exact
expectation via truncated summationmixture(components, weights) — Mixture distributions
with law of total variance*.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()+.dist and -.dist for numeric
location shiftsc * Normal(mu, v) simplifies to
Normal(c*mu, c^2*v)c * Gamma(a, r) simplifies to
Gamma(a, r/c) for c > 0c * Exponential(r) simplifies to
Gamma(1, r/c) for c > 0Normal(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))realize() generic — materialize any distribution to
empirical_dist by samplingedist: cdf,
density, sup, conditional,
rmap, inv_cdfcountable_set R6 class for countably infinite support
(Poisson)inv_cdf.empirical_dist — quantile function for
empirical distributionsstopifnot)format() methods for all distribution typesprint() methods delegating to
format()vcov.exponential — was returning
rate instead of 1/rate^2sampler.edist crash when n=1conditional.empirical_dist gives informative error on
zero matchesexpectation_data() CI
computationnormal, mvn,
exponential, empirical_distedist) for lazy composition
of distributions+, -) on
distributions with automatic simplificationfinite_set, interval for
representing distribution domainssampler, mean,
vcov, density, cdf,
paramsexpectation,
conditional, and rmap operationsThese binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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