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 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 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 OneWayANOVAResult struct {
	Factor  ANOVAResultComponent
	Within  ANOVAResultComponent
	TotalSS float64
}

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

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

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

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 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
}