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sicore package

This package consists of core functions commonly used in selective inference.

The Japanese version README is here.

Installation

This package requires python 3.10 or higher and automatically installs any dependent packages. If you want to use tensorflow and pytorch's tensor, please install the framework manually.

$ pip install sicore

Uninstall :

$ pip uninstall sicore

API Reference

Deteiled API reference is here.

List of functions

The following fuctions are imported by from sicore import *

Statistical Inference

  • NaiveInferenceNorm : Naive statistical inference for the test statistic following a normal distribution.
  • SelectiveInferenceNorm : Selective statistical inference for the test statistic following a normal distribution.
    • Parametric SI and Over-Conditioning provided.
    • Parametric SI offers the following three types of methods.
      • Calculation of p-value with specified guaranteed accuracy.
      • Determining if the null hypothesis is rejected or not.
      • Performing a parametric search of the entire specified range.
    • Inference results are returned as a data class.
  • NaiveInferenceChi : Naive statistical inference for the test statistic following a chi distribution.
  • SelectiveInferenceChi : Selective statistical inference for the test statistic following a chi distribution.
    • Parametric SI and Over-Conditioning provided.
    • Parametric SI offers the following three types of methods.
      • Calculation of p-value with specified guaranteed accuracy.
      • Determining if the null hypothesis is rejected or not.
      • Performing a parametric search of the entire specified range.
    • Inference results are returned as a data class.

Truncated Distribution Provides computation with arbitrary precision using mpmath for multiple truncated intervals.

  • tn_cdf() : truncated standard normal distribution
  • tc_cdf() : truncated chi distribution

Evaluation Function

  • type1_error_rate()
  • power()

Figure Drawing

  • pvalues_hist() : Draws a histogram of p-values.
  • pvalues_qqplot() : Draws a uniform Q-Q plot of p-values.

Interval Operations

  • intervals.intersection() : Computes the intersection of two sets of intervals.
  • intervals.intersection_all() : Computes the intersection of set of intervals.
  • intervals.union_all() : Computes the union of set of intervals.
  • intervals.not_() : Computes the complement of set of intervals with real numbers as the whole set.

Utility

  • OneVec : Generates a vector that is 1 at the specified index and 0 otherwise.
  • poly_lt_zero() : Calculation of the intervals for which the polynomial is less than or equal to 0.
  • polytope_to_interval() : Converts a selection event given in quadratic form into truncated intervals.
  • construct_projection_matrix() : Constructs a projection matrix from a basis given as a list of vectors to the subspace it spans.

Others

Execute code test :

$ pytest tests/