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Examples

Sample programs demonstrating each feature of the statcpp library are provided in the examples/ directory.

Sample Program List

Basic Statistics & Dispersion

Filename Description
example_basic_statistics.cpp Basic statistics: mean, median, mode, etc.
example_dispersion_spread.cpp Dispersion: variance, standard deviation, range, etc.
example_order_statistics.cpp Quartiles, percentiles, five-number summary
example_shape_of_distribution.cpp Distribution shape: skewness, kurtosis
example_frequency_distribution.cpp Frequency distribution, histogram

Correlation & Covariance

Filename Description
example_correlation_covariance.cpp Correlation coefficients, covariance computation

Special Functions

Filename Description
example_special_functions.cpp Gamma function, beta function, error function, etc.
example_numerical_utils.cpp Numerical utilities (Kahan summation, etc.)

Probability Distributions

Filename Description
example_random_engine.cpp Random number generation engine
example_continuous_distributions.cpp Continuous distributions: normal, t, F, etc.
example_discrete_distributions.cpp Discrete distributions: binomial, Poisson, etc.

Statistical Estimation & Tests

Filename Description
example_estimation.cpp Confidence interval estimation
example_parametric_tests.cpp Parametric tests: t-test, z-test, etc.
example_nonparametric_tests.cpp Nonparametric tests: Wilcoxon, Mann-Whitney, etc.
example_effect_size.cpp Effect sizes: Cohen's d, correlation ratio, etc.
example_power_analysis.cpp Power analysis, sample size calculation

Resampling & Regression Analysis

Filename Description
example_resampling.cpp Bootstrap, permutation test
example_linear_regression.cpp Simple and multiple regression analysis
example_anova.cpp Analysis of variance (ANOVA)
example_glm.cpp Generalized linear models (GLM)

Multivariate Analysis & Clustering

Filename Description
example_multivariate.cpp Multivariate analysis
example_clustering.cpp k-means, hierarchical clustering
example_distance_metrics.cpp Distance metrics: Euclidean, Manhattan, etc.

Time Series & Categorical Data

Filename Description
example_time_series.cpp Time series analysis
example_categorical.cpp Categorical data analysis

Data Processing & Robust Statistics

Filename Description
example_data_wrangling.cpp Data transformation, missing value handling
example_missing_data.cpp Advanced missing data handling
example_robust.cpp Robust statistics (outlier-resistant statistics)
example_survival.cpp Survival analysis

Other

Filename Description
example_model_selection.cpp Model selection (AIC, BIC, etc.)

Building and Running Examples

Method 1: Using Shell Script (macOS/Linux)

Build and run all sample programs at once:

cd Examples
./build.sh

This script will:

  1. Compile all .cpp files
  2. Generate executables
  3. Run each program and display output

Method 2: Compile Individually

Build only a specific sample program:

cd Examples

# Using GCC
g++ -std=c++17 -I../include example_basic_statistics.cpp -o example_basic_statistics

# Using Clang
clang++ -std=c++17 -I../include example_basic_statistics.cpp -o example_basic_statistics

# Run
./example_basic_statistics

Method 3: Using CMake

Build all examples with CMake:

cd Examples
mkdir build
cd build
cmake ..
cmake --build .

# Run
./example_basic_statistics
./example_dispersion_spread
# ...

Understanding the Sample Code

Each sample program is structured as follows:

  1. Header Inclusion: Include header files for the features being used
  2. Data Preparation: Define sample data
  3. Function Calls: Perform calculations using statcpp functions
  4. Result Display: Output the calculation results

Basic Example: example_basic_statistics.cpp

#include <iostream>
#include <vector>
#include "statcpp/basic_statistics.hpp"

int main() {
    // 1. Data preparation
    std::vector<double> data = {1.0, 2.0, 3.0, 4.0, 5.0};

    // 2. Function calls
    double avg = statcpp::mean(data.begin(), data.end());
    double total = statcpp::sum(data.begin(), data.end());
    std::size_t n = statcpp::count(data.begin(), data.end());

    // 3. Result display
    std::cout << "Count: " << n << std::endl;
    std::cout << "Sum: " << total << std::endl;
    std::cout << "Mean: " << avg << std::endl;

    return 0;
}

Customization

The sample programs are designed for learning purposes. You can customize them for your own data and use cases as follows:

Changing Data

// Replace sample data with your own data
std::vector<double> data = {1.0, 2.0, 3.0, 4.0, 5.0};
// ↓
std::vector<double> data = {/* your data */};

Reading Data from File

#include <fstream>
#include <vector>

std::vector<double> read_data(const std::string& filename) {
    std::vector<double> data;
    std::ifstream file(filename);
    double value;
    while (file >> value) {
        data.push_back(value);
    }
    return data;
}

int main() {
    auto data = read_data("data.txt");
    double avg = statcpp::mean(data.begin(), data.end());
    // ...
}

Computing Multiple Statistics at Once

#include "statcpp/basic_statistics.hpp"
#include "statcpp/dispersion_spread.hpp"
#include "statcpp/order_statistics.hpp"

void analyze_data(const std::vector<double>& data) {
    double avg = statcpp::mean(data.begin(), data.end());
    double sd = statcpp::stddev(data.begin(), data.end());

    auto sorted_data = data;
    std::sort(sorted_data.begin(), sorted_data.end());
    double med = statcpp::median(sorted_data.begin(), sorted_data.end());

    std::cout << "Mean: " << avg << std::endl;
    std::cout << "Standard Deviation: " << sd << std::endl;
    std::cout << "Median: " << med << std::endl;
}

Troubleshooting

Compile Error: Header Not Found

Verify that the include path is correctly specified:

g++ -std=c++17 -I/path/to/statcpp/include example.cpp -o example

Runtime Error: invalid_argument

Many statistical functions throw exceptions for empty data or invalid arguments. Verify that your data is appropriate:

std::vector<double> data = {/* data */};
if (data.empty()) {
    std::cerr << "Data is empty" << std::endl;
    return 1;
}

double avg = statcpp::mean(data.begin(), data.end());

Forgetting to Sort

Functions that compute median or quartiles require sorted data:

std::vector<double> data = {5.0, 2.0, 8.0, 1.0, 3.0};

// Sorting required
std::sort(data.begin(), data.end());

double med = statcpp::median(data.begin(), data.end());

Next Steps