Enum IssueCode 

pub enum IssueCode {
    DependenceHigh,
    PrecisionLow,
    DiscreteMode,
    ThresholdIssue,
    FilteringApplied,
    StationarityIssue,
    NumericalIssue,
    LikelihoodInflated,
}

Issue codes for programmatic handling of quality problems.

Consolidated to 8 categories per spec §6.1 (v6.0).

Variants

DependenceHigh

High temporal dependence reduces effective sample size.

Covers: high autocorrelation, low effective sample size. The block bootstrap accounts for this, but it means more samples were needed to reach the same confidence level.

PrecisionLow

Low measurement precision due to setup issues.

Covers: small sample count for discrete mode, generator cost asymmetry, low entropy in random inputs. These affect measurement quality but results are still valid.

DiscreteMode

Timer has low resolution, using discrete mode.

The timer resolution is coarse enough that many samples have identical values. The bootstrap handles this, but sensitivity is reduced.

ThresholdIssue

Threshold was adjusted due to measurement limitations.

Covers: threshold elevated due to measurement floor, threshold clamped to timer resolution. The effective threshold may differ from the user-requested threshold.

FilteringApplied

Outlier filtering was applied to the data.

Covers: high winsorization rate, quantiles filtered from analysis. Some data points were trimmed as outliers. This is normal but excessive filtering may indicate environmental issues.

StationarityIssue

Stationarity of timing distribution is suspect.

The timing distribution may have changed during measurement, violating the i.i.d. assumption. This can occur due to CPU frequency scaling, thermal throttling, or concurrent processes.

NumericalIssue

Numerical issues in Gibbs sampler.

Covers: lambda chain poor mixing, kappa chain poor mixing. The MCMC chains showed poor convergence (CV < 0.1 or ESS < 20). Results may be less reliable.

LikelihoodInflated

Likelihood covariance was inflated for robustness.

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 (kappa_mean < 0.3).

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

Formats the value using the given formatter.

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

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Hash for IssueCode

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

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::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl Copy for IssueCode

impl Eq for IssueCode

impl StructuralPartialEq for IssueCode