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rinvgausstidy_geometric()None
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
None
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
None
NEWS.md file to track changes to the
package.None
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.