infer 0.5.3

Breaking changes

• get_confidence_interval() now uses column names (‘lower_ci’ and ‘upper_ci’) in output that are consistent with other infer functionality (#317).

New functionality

• get_confidence_interval() can now produce bias-corrected confidence intervals by setting type = "bias-corrected". Thanks to @davidbaniadam for the initial implementation (#237, #318)!

Other

• Fix CRAN check failures related to long double errors.

infer 0.5.2

• Warn the user when a p-value of 0 is reported (#257, #273)
• Added new vignettes: chi_squared and anova (#268)
• Updates to documentation and existing vignettes (#268)
• Add alias for hypothesize() (hypothesise()) (#271)
• Subtraction order no longer required for difference-based tests–a warning will be raised in the case that the user doesn’t supply an order argument (#275, #281)
• Add new messages for common errors (#277)
• Increase coverage of theoretical methods in documentation (#278, #280)
• Drop missing values and reduce size of gss dataset used in examples (#282)
• Add stat = "ratio of props" and stat = "odds ratio" to calculate (#285)
• Add prop_test(), a tidy interface to prop.test() (#284, #287)
• Updates to visualize() for compatibility with ggplot2 v3.3.0 (#289)
• Fix error when bootstrapping with small samples and raise warnings/errors when appropriate (#239, #244, #291)
• Fix unit test failures resulting from breaking changes in dplyr v1.0.0
• Fix error in generate() when response variable is named x (#299)
• Add two-sided and two sided as aliases for two_sided for the direction argument in get_p_value() and shade_p_value() (#302)
• Fix t_test() and t_stat() ignoring the order argument (#310)

infer 0.5.1

• Updates to documentation and other tweaks

infer 0.5.0

Breaking changes

• 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).

Other

• Updated chisq_test() to take arguments in a response/explanatory format, perform goodness of fit tests, and default to the approximation approach (#241).
• Updated chisq_stat() to do goodness of fit (#241).
• Make interface to hypothesize() clearer by adding the options for the point null parameters to the function signature (#242).
• Manage infer class more systematically (#219).
• Use vdiffr for plot testing (#221).

infer 0.4.1

• Added Evgeni Chasnovski as author for his incredible work on refactoring the package and providing excellent support.

infer 0.4.0

Breaking changes

• Changed method of computing two-sided p-value to a more conventional one. It also makes get_pvalue() and visualize() more aligned (#205).

Deprecation changes

• Deprecated p_value() (use get_p_value() instead) (#180).
• Deprecated conf_int() (use get_confidence_interval() instead) (#180).
• Deprecated (via warnings) plotting p-value and confidence interval in visualize() (use new functions shade_p_value() and shade_confidence_interval() instead) (#178).

New functions

• 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().

Other

• Account for NULL value in left hand side of formula in specify() (#156) and type in generate() (#157).
• Update documentation code to follow tidyverse style guide (#159).
• Remove help page for internal set_params() (#165).
• Fully use {tibble} (#166).
• Fix calculate() to not depend on order of p for type = "simulate" (#122).
• Reduce code duplication (#173).
• Make transparency in visualize() to not depend on method and data volume.
• Make visualize() work for “One sample t” theoretical type with method = "both".
• Add stat = "sum" and stat = "count" options to calculate() (#50).

infer 0.3.1

• Stop using package {assertive} in favor of custom type checks (#149)
• Fixed t_stat() to use ... so var.equal works
• With the help of @echasnovski, fixed var.equal = TRUE for specify() %>% calculate(stat = "t")
• Use custom functions for error, warning, message, and paste() handling (#155)

infer 0.3.0

• Added conf_int logical argument and conf_level argument to t_test()
• Switched shade_color argument in visualize() to be pvalue_fill instead since fill color for confidence intervals is also added now
• Shading for Confidence Intervals in visualize()
  • Green is default color for CI and red for p-values
  • direction = "between" to get the green shading
  • Currently working only for simulation-based methods
• Implemented conf_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()
  • Converted longer confidence interval calculation code in vignettes to use get_ci() instead
    
• Implemented p_value() function for computing p-value provided a simulation-based method with a stat variable
  • get_pvalue() is an alias for p_value()
  • Converted longer p-value calculation code in vignettes to use get_pvalue() instead
• Implemented Chi-square Goodness of Fit observed stat depending on params being set in hypothesize with specify() %>% calculate() shortcut
• Removed “standardized” slope \(t\) since its formula is different than “standardized” correlation and there is no way currently to give one over the other
• Implemented correlation with bootstrap CI and permutation hypothesis test
• Filled the type argument automatically in generate() based on specify() and hypothesize()
  • Added message if type is given differently than expected
• Implemented specify() %>% calculate() for getting observed statistics.
  • visualize() works with either a 1x1 data frame or a vector for its obs_stat argument
  • Got stat = "t" working
• Refactored calculate() into smaller functions to reduce complexity
• Produced error if mu is given in hypothesize() but stat = "median" is provided in calculate() and other similar mis-specifications
• Tweaked chisq_stat() and t_stat() to match with specify() %>% calculate() framework
  • Both work in the one sample and two sample cases by providing formula
  • Added order argument to t_stat()
• Added implementation of one sample t_test() by passing in the mu argument to t.test from hypothesize()
• Tweaked pkgdown page to include ToDo’s using {dplyr} example

infer 0.2.0

• Switched to !! instead of UQ() since UQ() is deprecated in {rlang} 0.2.0
• Added many new files: CONDUCT.md, CONTRIBUTING.md, and TO-DO.md
• Updated README file with more development information
• Added wrapper functions t_test() and chisq_test() that use a formula interface and provide an intuitive wrapper to t.test() and chisq.test()
• Created stat = "z" and stat = "t" options
• Added many new arguments to visualize() to prescribe colors to shade and use for observed statistics and theoretical density curves
• Added check so that a bar graph created with visualize() if number of unique values for generated statistics is small
• Added shading for method = "theoretical"
• Implemented shading for simulation methods w/o a traditional distribution
  • Use percentiles to determine two-tailed shading
• Changed method = "randomization" to method = "simulation"
• Added warning when theoretical distribution is used that assumptions should be checked
  
• Added theoretical distributions to visualize() alone and as overlay with current implementations being
  • Two sample t
  • ANOVA F
  • One proportion z
  • Two proportion z
  • Chi-square test of independence
  • Chi-square Goodness of Fit test
  • Standardized slope (t)

infer 0.1.1

• Added additional tests
• Added order argument in calculate()
• Fixed bugs post-CRAN release
• Automated travis build of pkgdown to gh-pages branch

infer 0.1.0

• Altered the way that successes are indicated in an infer pipeline. They now live in specify().
• Updated documentation with examples
• Created pkgdown site materials
  • Deployed to https://infer.netlify.com

infer 0.0.1

• Implemented the “intro stats” examples for randomization methods