Enum IssueCode 

pub enum IssueCode {
    HighDependence,
    LowEffectiveSamples,
    StationaritySuspect,
    DiscreteTimer,
    SmallSampleDiscrete,
    HighGeneratorCost,
    LowUniqueInputs,
    QuantilesFiltered,
    ThresholdClamped,
    HighWinsorRate,
    ThresholdElevated,
    SlabDominant,
    LambdaMixingPoor,
    KappaMixingPoor,
    LikelihoodInflated,
}

Issue codes for programmatic handling of quality problems.

Variants

HighDependence

High autocorrelation reduces effective sample size.

LowEffectiveSamples

Effective sample size is too low for reliable inference.

StationaritySuspect

Timing distribution appears to drift during measurement.

DiscreteTimer

Timer has low resolution, using discrete mode.

SmallSampleDiscrete

Sample count is small for discrete mode bootstrap.

HighGeneratorCost

Generator cost differs between classes.

LowUniqueInputs

Low entropy in random inputs (possible API misuse).

QuantilesFiltered

Some quantiles were filtered from analysis.

ThresholdClamped

Threshold was clamped to timer resolution.

HighWinsorRate

High fraction of samples were winsorized.

ThresholdElevated

User’s threshold was elevated due to measurement floor.

SlabDominant

Slab component dominates posterior (v5.2 mixture prior).

The wide “slab” prior component has higher posterior weight than the narrow component, indicating evidence strongly favors a large effect (well above the threshold). This is informational, not a problem.

DEPRECATED: v5.4 uses Student’s t prior with Gibbs sampling instead of mixture prior. This code is kept for backwards compatibility.

LambdaMixingPoor

v5.4: Gibbs sampler’s lambda chain did not mix well.

The latent scale variable λ showed poor mixing (CV < 0.1 or ESS < 20), indicating the posterior may be unreliable. This typically occurs with very small or very large effects where the posterior is concentrated.

KappaMixingPoor

v5.6: Gibbs sampler’s kappa chain did not mix well.

The likelihood precision variable κ showed poor mixing (CV < 0.1 or ESS < 20), indicating the posterior may be unreliable.

LikelihoodInflated

v5.6: Likelihood covariance was inflated (kappa_mean < 0.3).

The robust t-likelihood inflated covariance by ~1/κ_mean to accommodate data that doesn’t match the estimated Σₙ. Effect estimates remain valid but uncertainty was increased for robustness.

Trait Implementations

impl Clone for IssueCode

fn clone(&self) -> IssueCode

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for IssueCode

fn fmt(&self, f: &mut Formatter<'_>) -> Result<(), Error>

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for IssueCode

fn deserialize<__D>( __deserializer: __D, ) -> Result<IssueCode, <__D as Deserializer<'de>>::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Hash for IssueCode

fn hash<__H>(&self, state: &mut __H)

where __H: Hasher,

Feeds this value into the given Hasher.

fn hash_slice<H>(data: &[Self], state: &mut H)

where H: Hasher, Self: Sized,

Feeds a slice of this type into the given Hasher.

impl PartialEq for IssueCode

fn eq(&self, other: &IssueCode) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl Serialize for IssueCode

fn serialize<__S>( &self, __serializer: __S, ) -> Result<<__S as Serializer>::Ok, <__S as Serializer>::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl Copy for IssueCode

impl Eq for IssueCode

impl StructuralPartialEq for IssueCode