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_type
tidy_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 Fix
tidy_autoplot_functions to incorporate this change. Fix
util_gamma_param_estimate()to say
scaleinstead of
rate` 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