Statistics-Tool-Box

This is a single header file inspired by stb.h by Sean Barrett with a bunch of useful statistical functions. It is developed by Gerben Voshol.

Functions included are:

• stb_moderated_ttest, stb_cosine_similarity, RSE Normalization (stb_calc_geometric_scaling_factors and stb_meanvar_counts_to_common_scale)
• stb_shannon (Shannon's diversity index, Pilou evenness, stb_simpson (Simpson's Diversity Index), stb_jaccard (Jaccard similarity index), stb_bray_curtis (Bray–Curtis dissimilarity) and stb_create_htable a simple basic hash table
• stb_pdf_hypgeo hypergeometric distribution probability density function, speedup stb_log_factorial using lookup table
• stb_fisher2x2 simple fisher exact test for 2x2 contigency tables
• stb_pdf_binom and stb_pdf_pois, the binomial and poison probability density functions
• stb_polygamma, stb_trigamma_inverse gamme functions and stb_fit_f_dist for moment estimation of the scaled F-distribution
• stb_qnorm and stb_qnorm_with_reference (also matrix variants) quantile normalization between columns with and without a reference
• stb_neugas Neural gas clustering algorithm
• stb_pca Principal Component Analysis
• stb_csm (confident sequence method) for monte-carlo simulations
• stb_kmeans k-means++ classical data clustering
• stb_qsort (Quicksort), could be used to replace current sorting method
• stb_cdf_gumbel, stb_pdf_gumbel, stb_icdf_gumbel and stb_est_gumbel, the (inverse) cumulative/probability density functions for the gumbel distribution and the ML estimator of the gumbel parameters
• stb_kendall (Kendall's Rank correlation)
• stb_jenks Initial port of O(k×n×log(n)) Jenks-Fisher algorithm originally created by Maarten Hilferink
• stb_logistic_regression_L2 simple L2-regularized logistic regression
• stb_spearman (Spearman's Rank correlation)
• stb_invert_matrix, stb_transpose_matrix, stb_matrix_multiply, ..., stb_multi_linear_regression and stb_multi_logistic_regression
• stb_ksample_anderson_darling, stb_2sample_anderson_darling, (one sample) stb_anderson_darling
• stb_expfit (Exponential fitting), stb_polyfit (Polynomial fitting), stb_powfit (Power curve fitting), stb_linfit (Liniear fitting)
• stb_trap, stb_trapezoidal (returns the integral (area under the cruve) of a given function and interval)
• stb_lagrange (polynomial interpolation), stb_sum (Neumaier summation algorithm)
• stb_mann_whitney, stb_kruskal_wallis (Unfinished, needs a better way to handle Dunn's post-hoc test)
• stb_combinations
• stb_allocmat (simple allocation of 2d array, but might not work on all systems?!)
• stb_fgetln, stb_fgetlns
• stb_pcg32 (PCG-XSH-RR) and stb_xoshiro512 (xoshiro512**) Pseudo Random Number Generators
• stb_anova (One-Way Anova with Tukey HSD test and Scheffe T-statistics method (post-hoc) (Unfinished))
• stb_quartiles
• stb_histogram (very simple histogram), stb_print_histogram, ...
• stb_factorial
• stb_meanvar
• stb_ttest, stb_uttest
• stb_ftest,
• stb_benjamini_hochberg
• stb_chisqr, stb_chisqr_matrix, stb_gtest, stb_gtest_matrix,

Installation

Run:

$ npm i stb_stats.c

And then include stb_stats.h as follows:

// main.c
#define STB_STATS_IMPLEMENTATION
#include "node_modules/stb_stats.c/stb_stats.h"

int main() { /* ... */ }

And then compile with clang or gcc as usual.

$ clang main.c  # or, use gcc
$ gcc   main.c

You may also use a simpler approach:

// main.c
#define STB_STATS_IMPLEMENTATION
#include <stb_stats.h>

int main() { /* ... */ }

If you add the path node_modules/stb_stats.c to your compiler's include paths.

$ clang -I./node_modules/stb_stats.c main.c  # or, use gcc
$ gcc   -I./node_modules/stb_stats.c main.c

Citation

If you use this Tool-Box in a publication, please reference:

Voshol, G.P. (2022). STB: A simple Statistics Tool Box (Version 1.23) [Software]. Available from https://github.com/gerbenvoshol/Statistics-Tool-Box