stats

type ANOVAResultComponent struct {
	SumOfSquares float64
	DF           int
	F            float64
	P            float64
	EtaSquared   float64
}

type AgglomerativeMethod string

type AlternativeHypothesis string

type BartlettTestResult struct {
	ChiSquare        float64 // Chi-square statistic
	DegreesOfFreedom int     // Degrees of freedom
	PValue           float64 // P-value
	SampleSize       int     // Sample size
}

type ChiSquareTestResult struct {

	// a DataTable representing the contingency table([2]float64{observed, expected})
	ContingencyTable *insyra.DataTable
	// contains filtered or unexported fields
}

type CorrelationMethod int

type CorrelationResult struct {
	// contains filtered or unexported fields
}

type DBSCANOptions struct {
	BorderPoints *bool
}

type DBSCANResult struct {
	Cluster []int
	IsSeed  []bool
}

type EffectSizeEntry struct {
	Type  string  // "cohen_d", "hedges_g", "glass_delta", etc.
	Value float64 // Effect size value
}

type ExponentialRegressionResult struct {
	Intercept              float64
	Slope                  float64
	Residuals              []float64
	RSquared               float64
	AdjustedRSquared       float64
	StandardErrorIntercept float64
	StandardErrorSlope     float64
	TValueIntercept        float64
	TValueSlope            float64
	PValueIntercept        float64
	PValueSlope            float64

	ConfidenceIntervalIntercept [2]float64
	ConfidenceIntervalSlope     [2]float64
}

type FTestResult struct {
	DF2 float64 // degree of freedom for the second group
	// contains filtered or unexported fields
}

type FactorAnalysisOptions struct {
	Count      FactorCountSpec
	Extraction FactorExtractionMethod
	Rotation   FactorRotationOptions
	Scoring    FactorScoreMethod
	MaxIter    int     // Optional: default 100
	MinErr     float64 // Optional: default 0.001 (R's min.err)

	// OptimFactr controls the L-BFGS-B convergence tolerance used by ML
	// and MINRES factor extraction: the optimizer terminates when the
	// relative function change drops below OptimFactr * machine epsilon.
	// Defaults to 1e7 (≈2.2e-9 absolute), matching R psych::fa /
	// stats::optim's "moderate accuracy" default. Use 1 for machine
	// precision (≈2.2e-16) when you want the true stationary point on
	// near-Heywood / flat objective surfaces; the default terminates
	// prematurely on those problems and settles at a non-stationary
	// boundary point. Use 1e12 for low precision (faster).
	OptimFactr float64

	// OptimMaxIter caps L-BFGS-B iterations for ML / MINRES extraction.
	// Defaults to 100 (matching R stats::optim default). Increase for
	// ill-conditioned problems where the optimizer would otherwise hit
	// the iteration cap before converging.
	OptimMaxIter int
}

type FactorAnalysisResult struct {
	Loadings             insyra.IDataTable   // Loading matrix (variables x factors)
	UnrotatedLoadings    insyra.IDataTable   // Unrotated loading matrix (variables x factors)
	Structure            insyra.IDataTable   // Structure matrix (variables x factors)
	Uniquenesses         insyra.IDataTable   // Uniqueness vector (p x 1)
	Communalities        insyra.IDataTable   // Communality table (p x 1: Extraction)
	SamplingAdequacy     insyra.IDataTable   // KMO overall index and per-variable MSA values
	BartlettTest         *BartlettTestResult // Bartlett's test of sphericity summary
	Phi                  insyra.IDataTable   // Factor correlation matrix (m x m), nil for orthogonal
	RotationMatrix       insyra.IDataTable   // Rotation matrix (m x m), nil if no rotation
	Eigenvalues          insyra.IDataTable   // Eigenvalues vector (p x 1)
	ExplainedProportion  insyra.IDataTable   // Proportion explained by each factor (m x 1)
	CumulativeProportion insyra.IDataTable   // Cumulative proportion explained (m x 1)
	Scores               insyra.IDataTable   // Factor scores (n x m), nil if not computed
	ScoreCoefficients    insyra.IDataTable   // Factor score coefficient matrix (variables x factors)
	ScoreCovariance      insyra.IDataTable   // Factor score covariance matrix (factors x factors)

	Converged         bool
	RotationConverged bool
	Iterations        int
	CountUsed         int
	Messages          []string
}

type FactorCountMethod string

type FactorCountSpec struct {
	Method         FactorCountMethod
	FixedK         int     // Optional: used when Method is CountFixed
	EigenThreshold float64 // Optional: default 1.0 for CountKaiser
	MaxFactors     int     // Optional: 0 means no limit
}

type FactorExtractionMethod string

type FactorModel struct {
	FactorAnalysisResult
}

type FactorRotationMethod string

type FactorRotationOptions struct {
	Method           FactorRotationMethod
	Kappa            float64          // Optional: Promax power (default 4)
	Delta            float64          // Optional: Oblimin gamma (default 0)
	GeominEpsilon    float64          // Optional: Geomin delta (default 0.01)
	Restarts         int              // Optional: random orthonormal starts for GPA rotations (default 10)
	VarimaxAlgorithm VarimaxAlgorithm // Optional: "kaiser" (psych default) or "gparotation"
}

type FactorScoreMethod string

type FriedmanTestResult struct {
	NSubjects   int
	KConditions int
	// contains filtered or unexported fields
}

type GLMFamily string

type GLMLink string

type GLMOptions struct {
	Family          GLMFamily
	Link            GLMLink
	ConfidenceLevel float64
	MaxIter         int
	Tolerance       float64
	Offset          insyra.IDataList
	Weights         insyra.IDataList
}

type GLMResult struct {
	Family              GLMFamily
	Link                GLMLink
	Coefficients        []float64
	StandardErrors      []float64
	ZValues             []float64
	PValues             []float64
	ConfidenceIntervals [][2]float64
	LinearPredictors    []float64
	FittedValues        []float64
	Residuals           []float64
	PearsonResiduals    []float64
	DevianceResiduals   []float64
	Deviance            float64
	NullDeviance        float64
	LogLikelihood       float64
	NullLogLikelihood   float64
	AIC                 float64
	BIC                 float64
	PearsonChi2         float64
	Dispersion          float64
	DFResidual          int
	Iterations          int
	Converged           bool
	ConfidenceLevel     float64
	// contains filtered or unexported fields
}

type HierarchicalResult struct {
	Merge      [][2]int
	Height     []float64
	Order      []int
	Labels     []string
	Method     AgglomerativeMethod
	DistMethod string
}

type KMeansOptions struct {
	NStart  int
	IterMax int
	Seed    *int64
}

type KMeansResult struct {
	Cluster     []int
	Centers     insyra.IDataTable
	TotSS       float64
	WithinSS    []float64
	TotWithinSS float64
	BetweenSS   float64
	Size        []int
	Iter        int
	IFault      int
}

type KNNAlgorithm string

type KNNClassificationResult struct {
	Predictions   insyra.IDataList
	Classes       insyra.IDataList
	Probabilities insyra.IDataTable
}

type KNNNeighborsResult struct {
	Indices   [][]int
	Distances [][]float64
}

type KNNOptions struct {
	Weighting KNNWeighting
	Algorithm KNNAlgorithm
	LeafSize  int
}

type KNNRegressionResult struct {
	Predictions []float64
}

type KNNWeighting string

type KruskalWallisResult struct {
	NTotal       int
	GroupRankSum []float64 // sum of ranks per group, in input order
	// contains filtered or unexported fields
}

type KurtosisMethod int

type LinearRegressionResult struct {
	Slope                  float64
	Intercept              float64
	StandardError          float64
	StandardErrorIntercept float64
	TValue                 float64
	TValueIntercept        float64
	PValue                 float64
	PValueIntercept        float64

	ConfidenceIntervalIntercept [2]float64
	ConfidenceIntervalSlope     [2]float64

	Coefficients        []float64
	StandardErrors      []float64
	TValues             []float64
	PValues             []float64
	ConfidenceIntervals [][2]float64

	Residuals        []float64
	RSquared         float64
	AdjustedRSquared float64
}

type LogarithmicRegressionResult struct {
	Intercept              float64
	Slope                  float64
	Residuals              []float64
	RSquared               float64
	AdjustedRSquared       float64
	StandardErrorIntercept float64
	StandardErrorSlope     float64
	TValueIntercept        float64
	TValueSlope            float64
	PValueIntercept        float64
	PValueSlope            float64

	ConfidenceIntervalIntercept [2]float64
	ConfidenceIntervalSlope     [2]float64
}

type LogisticRegressionOptions struct {
	ConfidenceLevel  float64
	MaxIter          int
	Tolerance        float64
	PositiveClass    any
	SeparationPolicy SeparationPolicy
	Ridge            float64
}

type LogisticRegressionResult struct {
	Coefficients        []float64
	StandardErrors      []float64
	ZValues             []float64
	PValues             []float64
	ConfidenceIntervals [][2]float64
	OddsRatios          []float64
	OddsRatioCIs        [][2]float64
	LinearPredictors    []float64
	FittedProbabilities []float64
	Residuals           []float64
	PearsonResiduals    []float64
	DevianceResiduals   []float64
	Deviance            float64
	NullDeviance        float64
	LogLikelihood       float64
	NullLogLikelihood   float64
	AIC                 float64
	BIC                 float64
	McFaddenR2          float64
	CoxSnellR2          float64
	NagelkerkeR2        float64
	DFResidual          int
	Iterations          int
	Converged           bool
	SeparationDetected  bool
	Penalized           bool
	Ridge               float64
	PositiveClass       any
	ClassLabels         []any
	ConfidenceLevel     float64
	// contains filtered or unexported fields
}

type MannWhitneyUResult struct {
	U1     float64
	U2     float64
	Z      float64 // standardized z (asymptotic path); NaN for exact
	Method string  // "exact" or "asymptotic"
	// contains filtered or unexported fields
}

type OneWayANOVAResult struct {
	Factor  ANOVAResultComponent
	Within  ANOVAResultComponent
	TotalSS float64
}

type PCAResult struct {
	Components        insyra.IDataTable // component loadings matrix
	Eigenvalues       []float64
	ExplainedVariance []float64
}

type PoissonRegressionOptions struct {
	ConfidenceLevel float64
	MaxIter         int
	Tolerance       float64
	Offset          insyra.IDataList
	DispersionCheck bool
}

type PoissonRegressionResult struct {
	Coefficients           []float64
	StandardErrors         []float64
	ZValues                []float64
	PValues                []float64
	ConfidenceIntervals    [][2]float64
	IncidenceRateRatios    []float64
	IRRConfidenceIntervals [][2]float64
	LinearPredictors       []float64
	FittedRates            []float64
	FittedValues           []float64
	Residuals              []float64
	PearsonResiduals       []float64
	DevianceResiduals      []float64
	Deviance               float64
	NullDeviance           float64
	LogLikelihood          float64
	NullLogLikelihood      float64
	AIC                    float64
	BIC                    float64
	PearsonChi2            float64
	DispersionStatistic    float64
	OverDispersed          bool
	DFResidual             int
	Iterations             int
	Converged              bool
	ConfidenceLevel        float64
	// contains filtered or unexported fields
}

type PolynomialRegressionResult struct {
	Coefficients        []float64
	Degree              int
	Residuals           []float64
	RSquared            float64
	AdjustedRSquared    float64
	StandardErrors      []float64
	TValues             []float64
	PValues             []float64
	ConfidenceIntervals [][2]float64
}

type PredictType string

type RepeatedMeasuresANOVAResult struct {
	Factor  ANOVAResultComponent
	Subject ANOVAResultComponent
	Within  ANOVAResultComponent
	TotalSS float64
}

type SeparationPolicy string

type SilhouettePoint struct {
	Cluster  int
	Neighbor int
	SilWidth float64
}

type SilhouetteResult struct {
	Points            []SilhouettePoint
	AverageSilhouette float64
}

type SkewnessMethod int

type TTestResult struct {
	Mean     *float64 // mean of the first group (or the only group)
	Mean2    *float64 // mean of the second group (nil if not applicable)
	MeanDiff *float64 // mean difference (only for paired t-test)
	N        int      // sample size of the first group (or the only group or paired group)
	N2       *int     // sample size of the second group (nil if not applicable)
	// contains filtered or unexported fields
}

type TwoWayANOVAResult struct {
	FactorA     ANOVAResultComponent
	FactorB     ANOVAResultComponent
	Interaction ANOVAResultComponent
	Within      ANOVAResultComponent
	TotalSS     float64
}

type VarimaxAlgorithm string

type WilcoxonTestResult struct {
	Z          float64 // standardized z (asymptotic path); NaN for exact
	Method     string  // "exact" or "asymptotic"
	NEffective int     // number of nonzero |d_i| used (zeros dropped under "wilcox")
	// contains filtered or unexported fields
}

type ZTestResult struct {
	Mean  float64  // mean of the first group (or the only group)
	Mean2 *float64 // mean of the second group (nil if not applicable)
	N     int      // sample size of the first group (or the only group)
	N2    *int     // sample size of the second group (nil if not applicable)
	// contains filtered or unexported fields
}