< section id="introduction" class="level1">

Introduction

Hello, fellow R enthusiasts! I’m thrilled to share some fantastic updates to the TidyDensity package. These updates bring a wealth of new features, functions, and enhancements, making distribution analysis more comprehensive and efficient. Let’s dive into the details!

< section id="new-features" class="level2">

New Features

< section id="negative-binomial-distribution" class="level3">

Negative Binomial Distribution

• util_negative_binomial_aic(): Calculate the Akaike Information Criterion (AIC) for the negative binomial distribution. This function aids in model selection, helping you determine the best-fitting model for your data.

< section id="zero-truncated-negative-binomial-distribution" class="level3">

Zero-Truncated Negative Binomial Distribution

• util_zero_truncated_negative_binomial_param_estimate(): Estimate the parameters of the zero-truncated negative binomial distribution.
• util_zero_truncated_negative_binomial_aic(): Calculate the AIC for the zero-truncated negative binomial distribution.
• util_zero_truncated_negative_binomial_stats_tbl(): Create a summary table for the zero-truncated negative binomial distribution.

< section id="zero-truncated-poisson-distribution" class="level3">

Zero-Truncated Poisson Distribution

• util_zero_truncated_poisson_param_estimate(): Estimate the parameters of the zero-truncated Poisson distribution.
• util_zero_truncated_poisson_aic(): Calculate the AIC for the zero-truncated Poisson distribution.
• util_zero_truncated_poisson_stats_tbl(): Create a summary table for the zero-truncated Poisson distribution.

< section id="f-distribution" class="level3">

F Distribution

• util_f_param_estimate(): Estimate the parameters for the F distribution.
• util_f_aic(): Calculate the AIC for the F distribution.

< section id="zero-truncated-geometric-distribution" class="level3">

Zero-Truncated Geometric Distribution

• util_zero_truncated_geometric_param_estimate(): Estimate the parameters of the zero-truncated geometric distribution.
• util_zero_truncated_geometric_aic(): Calculate the AIC for the zero-truncated geometric distribution.
• util_zero_truncated_geometric_stats_tbl(): Create a summary table for the zero-truncated geometric distribution.

< section id="triangular-distribution" class="level3">

Triangular Distribution

• util_triangular_aic(): Calculate the AIC for the triangular distribution.

< section id="t-distribution" class="level3">

T Distribution

• util_t_param_estimate(): Estimate the parameters of the T distribution.
• util_t_aic(): Calculate the AIC for the T distribution.

< section id="pareto-type-i-distribution" class="level3">

Pareto Type I Distribution

• util_pareto1_param_estimate(): Estimate the parameters of the Pareto Type I distribution.
• util_pareto1_aic(): Calculate the AIC for the Pareto Type I distribution.
• util_pareto1_stats_tbl(): Create a summary table for the Pareto Type I distribution.

< section id="paralogistic-distribution" class="level3">

Paralogistic Distribution

• util_paralogistic_param_estimate(): Estimate the parameters of the paralogistic distribution.
• util_paralogistic_aic(): Calculate the AIC for the paralogistic distribution.
• util_paralogistic_stats_tbl(): Create a summary table for the paralogistic distribution.

< section id="inverse-weibull-distribution" class="level3">

Inverse Weibull Distribution

• util_inverse_weibull_param_estimate(): Estimate the parameters of the Inverse Weibull distribution.
• util_inverse_weibull_aic(): Calculate the AIC for the Inverse Weibull distribution.
• util_inverse_weibull_stats_tbl(): Create a summary table for the Inverse Weibull distribution.

< section id="inverse-pareto-distribution" class="level3">

Inverse Pareto Distribution

• util_inverse_pareto_param_estimate(): Estimate the parameters of the Inverse Pareto distribution.
• util_inverse_pareto_aic(): Calculate the AIC for the Inverse Pareto distribution.
• util_inverse_pareto_stats_tbl(): Create a summary table for the Inverse Pareto distribution.

< section id="inverse-gamma-distribution" class="level3">

Inverse Gamma Distribution

• util_inverse_burr_param_estimate(): Estimate the parameters of the Inverse Gamma distribution.
• util_inverse_burr_aic(): Calculate the AIC for the Inverse Gamma distribution.
• util_inverse_burr_stats_tbl(): Create a summary table for the Inverse Gamma distribution.

< section id="generalized-pareto-distribution" class="level3">

Generalized Pareto Distribution

• util_generalized_pareto_param_estimate(): Estimate the parameters of the Generalized Pareto distribution.
• util_generalized_pareto_aic(): Calculate the AIC for the Generalized Pareto distribution.
• util_generalized_pareto_stats_tbl(): Create a summary table for the Generalized Pareto distribution.

< section id="generalized-gamma-distribution" class="level3">

Generalized Gamma Distribution

• util_generalized_beta_param_estimate(): Estimate the parameters of the Generalized Gamma distribution.
• util_generalized_beta_aic(): Calculate the AIC for the Generalized Gamma distribution.
• util_generalized_beta_stats_tbl(): Create a summary table for the Generalized Gamma distribution.

< section id="zero-truncated-binomial-distribution" class="level3">

Zero-Truncated Binomial Distribution

• util_zero_truncated_binomial_stats_tbl(): Create a summary table for the Zero Truncated binomial distribution.
• util_zero_truncated_binomial_param_estimate(): Estimate the parameters of the Zero Truncated binomial distribution.
• util_zero_truncated_binomial_aic(): Calculate the AIC for the Zero Truncated binomial distribution.

< section id="minor-improvements-and-fixes" class="level2">

Minor Improvements and Fixes

• util_negative_binomial_param_estimate(): Updated to use optim() for parameter estimation, enhancing accuracy and efficiency.
• quantile_normalize(): Added names to columns when .return_tibble = TRUE for better readability and usability.

< section id="conclusion" class="level2">

Conclusion

These updates significantly expand the functionality of TidyDensity, providing more tools for robust distribution analysis. Whether you’re working with standard or specialized distributions, these new functions and improvements will streamline your workflow and enhance your analytical capabilities.

I encourage you to explore these new features and see how they can benefit your projects. As always, your feedback is invaluable, so please share your thoughts and experiences with these updates. Happy coding!