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util_negative_binomial_aic() to calculate the AIC for the negative binomial distribution.util_zero_truncated_negative_binomial_param_estimate() to estimate the parameters of the zero-truncated negative binomial distribution. Add function util_zero_truncated_negative_binomial_aic() to calculate the AIC for the zero-truncated negative binomial distribution. Add function util_zero_truncated_negative_binomial_stats_tbl() to create a summary table of the zero-truncated negative binomial distribution.util_zero_truncated_poisson_param_estimate() to estimate the parameters of the zero-truncated Poisson distribution. Add function util_zero_truncated_poisson_aic() to calculate the AIC for the zero-truncated Poisson distribution. Add function util_zero_truncated_poisson_stats_tbl() to create a summary table of the zero-truncated Poisson distribution.util_f_param_estimate() and util_f_aic() to estimate the parameters and calculate the AIC for the F distribution.util_zero_truncated_geometric_param_estimate() to estimate the parameters of the zero-truncated geometric distribution. Add function util_zero_truncated_geometric_aic() to calculate the AIC for the zero-truncated geometric distribution. Add function util_zero_truncated_geometric_stats_tbl() to create a summary table of the zero-truncated geometric distribution.util_triangular_aic() to calculate the AIC for the triangular distribution.util_t_param_estimate() to estimate the parameters of the T distribution. Add function util_t_aic() to calculate the AIC for the T distribution.util_pareto1_param_estimate() to estimate the parameters of the Pareto Type I distribution. Add function util_pareto1_aic() to calculate the AIC for the Pareto Type I distribution. Add function util_pareto1_stats_tbl() to create a summary table of the Pareto Type I distribution.util_paralogistic_param_estimate() to estimate the parameters of the paralogistic distribution. Add function util_paralogistic_aic() to calculate the AIC for the paralogistic distribution. Add fnction util_paralogistic_stats_tbl() to create a summary table of the paralogistic distribution.util_inverse_weibull_param_estimate() to estimate the parameters of the Inverse Weibull distribution. Add function util_inverse_weibull_aic() to calculate the AIC for the Inverse Weibull distribution. Add function util_inverse_weibull_stats_tbl() to create a summary table of the Inverse Weibull distribution.util_inverse_pareto_param_estimate() to estimate the parameters of the Inverse Pareto distribution. Add function util_inverse_pareto_aic() to calculate the AIC for the Inverse Pareto distribution. Add Function util_inverse_pareto_stats_tbl() to create a summary table of the Inverse Pareto distribution.util_inverse_burr_param_estimate() to estimate the parameters of the Inverse Gamma distribution. Add function util_inverse_burr_aic() to calculate the AIC for the Inverse Gamma distribution. Add function util_inverse_burr_stats_tbl() to create a summary table of the Inverse Gamma distribution.util_generalized_pareto_param_estimate() to estimate the parameters of the Generalized Pareto distribution. Add function util_generalized_pareto_aic() to calculate the AIC for the Generalized Pareto distribution. Add function util_generalized_pareto_stats_tbl() to create a summary table of the Generalized Pareto distribution.util_generalized_beta_param_estimate() to estimate the parameters of the Generalized Gamma distribution. Add function util_generalized_beta_aic() to calculate the AIC for the Generalized Gamma distribution. Add function util_generalized_beta_stats_tbl() to create a summary table of the Generalized Gamma distribution.util_zero_truncated_binomial_stats_tbl() to create a summary table of the Zero Truncated binomial distribution. Add function util_zero_truncated_binomial_param_estimate() to estimate the parameters of the Zero Truncated binomial distribution. Add function util_zero_truncated_binomial_aic() to calculate the AIC for the Zero Truncated binomial distribution.util_negative_binomial_param_estimate() to add the use of optim() for parameter estimation..return_tibble = TRUE for quantile_normalize()None
quantile_normalize() to normalize data using quantiles.check_duplicate_rows() to check for duplicate rows in a data frame.util_chisquare_param_estimate() to estimate the parameters of the chi-square distribution.tidy_mcmc_sampling() to sample from a distribution using MCMC. This outputs the function sampled data and a diagnostic plot.util_dist_aic() functions to calculate the AIC for a distribution.tidy_multi_single_dist() to respect the .return_tibble parametertidy_multi_single_dist() to exclude the .return_tibble parameter from returning in the distribution parameters.mcmc where applicable.tidy_distribution_comparison() to include the new AIC calculations from the dedicated util_dist_aic() functions.tidy_multi_single_dist() to be modified in that it now requires the user to pass the parameter of .return_tibbl with either TRUE or FALSE as it was introduced into the tidy_ distribution functions which now use data.table under the hood to generate data.|> pipe instead of the %>% which has caused a need to update the minimum R version to 4.1.0tidy_triangular()util_triangular_param_estimate()util_triangular_stats_tbl()triangle_plot()tidy_autoplot()cvar() and csd() to a vectorized approach from @kokbent which speeds these up by over 100xtidy_ distribution functions to generate data using data.table this in many instances has resulted in a speed up of 30% or more.dplyr::cur_data() as it was deprecated in dplyr in favor of using dplyr::pick()tidy_triangular() to all autoplot functions.tidy_multi_dist_autoplot() the .plot_type = "quantile" did not work.cskewness() to take advantage of vectorization with a speedup of 124x faster.ckurtosis() with vectorization to improve speed by 121x per benchmark testing.None
convert_to_ts() which will convert a tidy_ distribution into a time series in either ts format or tibble you can also have it set to wide or long by using .pivot_longer set to TRUE and .ret_ts set to FALSEutil_burr_stats_tbl()util_burr_param_estimate()None
util_burr_param_estimate()tidy_distribution_comparison() to add a parameter of .round_to_place which allows a user to round the parameter estimates passed to their corresponding distribution parameters.None
tidy_bernoulli()util_bernoulli_param_estimate()util_bernoulli_stats_tbl()tidy_stat_tbl() to fix tibble output so it no longer ignores passed arguments and fix data.table to directly pass … arguments.tidy_bernoulli() to autoplot.tidy_stat_tbl()dist_type_extractor() which is used for several functions in the library.dist_type_extractor()util_dist_stats_tbl() functions to use dist_type_extractor()autoplot functions for tidy_bernoulli()dist_type_extractor()tidy_stat_tbl() to use dist_type_extractor()p and q calculations.None
bootstrap_density_augment()bootstrap_p_vec() and bootstrap_p_augment()bootstrap_q_vec() and bootstrap_q_augment()cmean() chmean() cgmean() cmedian() csd() ckurtosis() cskewness() cvar()bootstrap_stat_plot()tidy_stat_tbl() Fix #281 adds the parameter of .user_data_table which is set to FALSE by default. If set to TRUE will use [data.table::melt()] for the underlying work speeding up the output from a benchmark test of regular tibble at 72 seconds to data.table. at 15 seconds.prop check in tidy_bootstrap()bootstrap_density_augment() output.None
tidy_normal() to list of tested distributions. Add AIC from a linear model for metric, and add stats::ks.test() as a metric.None
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tidy_distribution_summary_tbl()purrr::compact() on the list of distributions passed in order to prevent the issue occurring in #212tidy_distribution_comparison() more robust in terms of handling bad or erroneous data.tidy_multi_single_dist() which helps it to work with other functions like tidy_random_walk()None
color_blind() td_scale_fill_colorblind() and td_scale_color_colorblind()ci_lo() and ci_hi()tidy_bootstrap()bootstrap_unnest_tbl()tidy_distribution_comparison()_autoplot functions to include cumulative mean MCMC chart by taking advantage of the .num_sims parameter of tidy_ distribution functions.tidy_empirical() to add a parameter of .distribution_typetidy_empirical() is now again plotted by _autoplot functions..num_sims parameter to tidy_empirical()ci_lo() and ci_hi() to all stats tbl functions.distribution_family_type to discrete for tidy_geometric()None
tidy_four_autoplot() - This will auto plot the density, qq, quantile and probability plots to a single graph.util_weibull_param_estimate()util_uniform_param_estimate()util_cauchy_param_estimate()tidy_t() - Also add to plotting functions.tidy_mixture_density()util_geometric_stats_tbl()util_hypergeometric_stats_tbl()util_logistic_stats_tbl()util_lognormal_stats_tbl()util_negative_binomial_stats_tbl()util_normal_stats_tbl()util_pareto_stats_tbl()util_poisson_stats_tbl()util_uniform_stats_tbl()util_cauchy_stats_tbl()util_t_stats_tbl()util_f_stats_tbl()util_chisquare_stats_tbl()util_weibull_stats_tbl()util_gamma_stats_tbl()util_exponential_stats_tbl()util_binomial_stats_tbl()util_beta_stats_tbl()p calculation in tidy_poisson() that will now produce the correct probability chart from the auto plot functions.p calculation in tidy_hypergeometric() that will no produce the correct probability chart from the auto plot functions.tidy_distribution_summary_tbl() function did not take the output of tidy_multi_single_dist()ggplot2::xlim(0, max_dy) to ggplot2::ylim(0, max_dy)q columntidy_gamma() parameter of .rate to .scale Fixtidy_autoplot_functions to incorporate this change. Fixutil_gamma_param_estimate()to sayscaleinstead ofrate` in the returned estimated parameters.None
.geom_smooth is set to TRUE that ggplot2::xlim(0, max_dy) is set.tidy_multi_single_dist() failed on distribution with single parameter like tidy_poisson()tidy_ distribution functions to add an attribute of either discrete or continuous that helps in the autoplot process.tidy_autoplot() to use histogram or lines for density plot depending on if the distribution is discrete or continuous.tidy_multi_dist_autoplot() to use histogram or lines for density plot depending on if the distribution is discrete or continuous.None
tidy_binomial()tidy_geometric()tidy_negative_binomial()tidy_zero_truncated_poisson()tidy_zero_truncated_geometric()tidy_zero_truncated_binomial()tidy_zero_truncated_negative_binomial()tidy_pareto1()tidy_pareto()tidy_inverse_pareto()tidy_random_walk()tidy_random_walk_autoplot()tidy_generalized_pareto()tidy_paralogistic()tidy_inverse_exponential()tidy_inverse_gamma()tidy_inverse_weibull()tidy_burr()tidy_inverse_burr()tidy_inverse_normal()tidy_generalized_beta()tidy_multi_single_dist()tidy_multi_dist_autoplot()tidy_combine_distributions()tidy_kurtosis_vec(), tidy_skewness_vec(), and tidy_range_statistic()util_beta_param_estimate()util_binomial_param_estimate()util_exponential_param_estimate()util_gamma_param_estimate()util_geometric_param_estimate()util_hypergeometric_param_estimate()util_lognormal_param_estimate()tidy_scale_zero_one_vec()tidy_combined_autoplot()util_logistic_param_estimate()util_negative_binomial_param_estimate()util_normal_param_estimate()util_pareto_param_estimate()util_poisson_param_estimate()crayon, rstudioapi, and cli from Suggests to Imports due to pillar no longer importing..geom_rug to tidy_autoplot() function.geom_point to tidy_autoplot() function.geom_smooth to tidy_autoplot() function.geom_jitter to tidy_autoplot() functiontidy_autoplot() for when the distribution is tidy_empirical() the legend argument would fail.tidy_empirical()_pkgdown.yml file to update site.param_grid, param_grid_txt, and dist_with_params to the attributes of all tidy_ distribution functions.... as a grouping parameter to tidy_distribution_summary_tbl()dist_type a factor for tidy_combine_distributions()None
tidy_normal()tidy_gamma()tidy_beta()tidy_poisson()tidy_autoplot()tidy_distribution_summary_tbl()tidy_empirical()tidy_uniform()tidy_exponential()tidy_logistic()tidy_lognormal()tidy_weibull()tidy_chisquare()tidy_cauchy()tidy_hypergeometric()tidy_f()None
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NEWS.md file to track changes to the package.None
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