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