NEWS.md
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