NEWS.md
get_confidence_interval()
now uses column names (‘lower_ci’ and ‘upper_ci’) in output that are consistent with other infer functionality (#317).get_confidence_interval()
can now produce bias-corrected confidence intervals by setting type = "bias-corrected"
. Thanks to @davidbaniadam for the initial implementation (#237, #318)!chi_squared
and anova
(#268)hypothesize()
(hypothesise()
) (#271)order
argument (#275, #281)gss
dataset used in examples (#282)stat = "ratio of props"
and stat = "odds ratio"
to calculate
(#285)prop_test()
, a tidy interface to prop.test()
(#284, #287)visualize()
for compatibility with ggplot2
v3.3.0 (#289)dplyr
v1.0.0generate()
when response variable is named x
(#299)two-sided
and two sided
as aliases for two_sided
for the direction
argument in get_p_value()
and shade_p_value()
(#302)t_test()
and t_stat()
ignoring the order
argument (#310)shade_confidence_interval()
now plots vertical lines starting from zero (previously - from the bottom of a plot) (#234).shade_p_value()
now uses “area under the curve” approach to shading (#229).chisq_test()
to take arguments in a response/explanatory format, perform goodness of fit tests, and default to the approximation approach (#241).chisq_stat()
to do goodness of fit (#241).hypothesize()
clearer by adding the options for the point null parameters to the function signature (#242).infer
class more systematically (#219).vdiffr
for plot testing (#221).get_pvalue()
and visualize()
more aligned (#205).p_value()
(use get_p_value()
instead) (#180).conf_int()
(use get_confidence_interval()
instead) (#180).visualize()
(use new functions shade_p_value()
and shade_confidence_interval()
instead) (#178).shade_p_value()
- {ggplot2}-like layer function to add information about p-value region to visualize()
output. Has alias shade_pvalue()
.shade_confidence_interval()
- {ggplot2}-like layer function to add information about confidence interval region to visualize()
output. Has alias shade_ci()
.NULL
value in left hand side of formula in specify()
(#156) and type
in generate()
(#157).set_params()
(#165).calculate()
to not depend on order of p
for type = "simulate"
(#122).visualize()
to not depend on method and data volume.visualize()
work for “One sample t” theoretical type with method = "both"
.stat = "sum"
and stat = "count"
options to calculate()
(#50).t_stat()
to use ...
so var.equal
worksvar.equal = TRUE
for specify() %>% calculate(stat = "t")
paste()
handling (#155)conf_int
logical argument and conf_level
argument to t_test()
shade_color
argument in visualize()
to be pvalue_fill
instead since fill color for confidence intervals is also added nowvisualize()
direction = "between"
to get the green shadingconf_int()
function for computing confidence interval provided a simulation-based method with a stat
variable
get_ci()
and get_confidence_interval()
are aliases for conf_int()
get_ci()
insteadp_value()
function for computing p-value provided a simulation-based method with a stat
variable
get_pvalue()
is an alias for p_value()
get_pvalue()
insteadparams
being set in hypothesize
with specify() %>% calculate()
shortcuttype
argument automatically in generate()
based on specify()
and hypothesize()
type
is given differently than expectedspecify() %>% calculate()
for getting observed statistics.
visualize()
works with either a 1x1 data frame or a vector for its obs_stat
argumentstat = "t"
workingcalculate()
into smaller functions to reduce complexitymu
is given in hypothesize()
but stat = "median"
is provided in calculate()
and other similar mis-specificationschisq_stat()
and t_stat()
to match with specify() %>% calculate()
framework
formula
order
argument to t_stat()
t_test()
by passing in the mu
argument to t.test
from hypothesize()
pkgdown
page to include ToDo’s using {dplyr} example!!
instead of UQ()
since UQ()
is deprecated in {rlang} 0.2.0CONDUCT.md
, CONTRIBUTING.md
, and TO-DO.md
t_test()
and chisq_test()
that use a formula interface and provide an intuitive wrapper to t.test()
and chisq.test()
stat = "z"
and stat = "t"
optionsvisualize()
to prescribe colors to shade and use for observed statistics and theoretical density curvesvisualize()
if number of unique values for generated statistics is smallmethod = "theoretical"
method = "randomization"
to method = "simulation"
visualize()
alone and as overlay with current implementations being
order
argument in calculate()
specify()
.pkgdown
site materials