An R package for automated inferential testing (for group differences) and reporting based on parametric assumptions, which are tested automatically for test selection.

Installation

You can install the development version of writR from GitHub with:

# install.packages("devtools")
devtools::install_github("matcasti/writR")

Summary of available tests using `autest()` function

For paired samples designs

Nº of groups	Type	Test	Function in `R`
2	`type = 'p'`: parametric.	Student’s t-test.	`stats::t.test`
2	`type = 'r'`: robust.	Yuen’s test for trimmed means.	`WRS2::yuend`
2	`type = 'np'`: non-parametric.	Wilcoxon signed-rank test.	`stats::wilcox.test`
> 2	`type = 'p'`: parametric.	One-way repeated measures ANOVA (rmANOVA).	`afex::aov_ez`
> 2	`type = 'p'`: parametric.	rmANOVA with Greenhouse-Geisser correction.	`afex::aov_ez`
> 2	`type = 'p'`: parametric.	rmANOVA with Huynh-Feldt correction.	`afex::aov_ez`
> 2	`type = 'r'`: robust.	Heteroscedastic rmANOVA for trimmed means.	`WRS2::rmanova`
> 2	`type = 'np'`: non-parametric.	Friedman rank sum test.	`stats::friedman.test`

For independent samples design

Nº of groups	Type	Test	Function in `R`
2	`type = 'p'`: parametric.	Student’s t-test.	`stats::t.test`
2	`type = 'p'`: parametric.	Welch’s t-test.	`stats::t.test`
2	`type = 'r'`: robust.	Yuen’s test for trimmed means.	`WRS2::yuen`
2	`type = 'np'`: non-parametric.	Mann-Whitney U test.	`stats::wilcox.test`
> 2	`type = 'p'`: parametric.	Fisher’s One-way ANOVA.	`stats::oneway.test`
> 2	`type = 'p'`: parametric.	Welch’s One-way ANOVA.	`stats::oneway.test`
> 2	`type = 'np'`: non-parametric.	Kruskal-Wallis one-way ANOVA.	`stats::kruskal.test`
> 2	`type = 'r'`: robust.	Heteroscedastic one-way ANOVA for trimmed means.	`WRS2::t1way`

Corresponding Post-Hoc tests for Nº groups > 2

Design	Type	Test	Function in `R`
Paired	`type = 'p'`: parametric.	Student’s t-test.	`stats::pairwise.t.test`
Paired	`type = 'np'`: non-parametric.	Conover-Iman all-pairs comparison test.	`PMCMRplus::durbinAllPairsTest`
Paired	`type = 'r'`: robust.	Yuen’s test for trimmed means (see Wilcox, 2012, p. 385).	`WRS2::rmmcp`
Independent	`type = 'p'`: parametric + `var.equal = TRUE`.	Student’s t-test.	`stats::pairwise.t.test`
Independent	`type = 'p'`: parametric + `var.equal = FALSE`.	Games-Howell test.	`PMCMRplus::gamesHowellTest`
Independent	`type = 'np'`: non-parametric.	Dunn’s test.	`PMCMRplus::kwAllPairsDunnTest`
Independent	`type = 'r'`: robust.	Yuen’s test for trimmed means (see Mair and Wilcox).	`WRS2::lincon`

Available effect sizes

Nº of groups	Test	Effect size
2	Parametric	Cohens’d
2	Parametric	Hedges’g
2	Non-parametric	Rank-biserial correlation
2	Robust	Algina-Keselman-Penfield robust standardized difference
> 2	Parametric	Eta-squared
> 2	Parametric	Omega-squared
> 2	Non-parametric	Epsilon-squared
> 2	Robust	Explanatory measure of effect size

Automated testing

By default, autest(), checks automatically the assumptions of the data based on the parameters supplied for test selection.

library(writR) # Load the writR package 

set.seed(123) # for reproducibility
diets <- data.frame(
    weight = c(rnorm(n = 100/2, mean = 70, sd = 7)   # Treatment
             , rnorm(n = 100/2, mean = 66, sd = 7) ) # Control
  , diet = gl(n = 2, k = 100/2, labels = c('Treatment', 'Control') ) )
  
result <- autest( 
  data = diets, 
  x = "diet", # independent variable
  y = "weight", # dependent variable
  type = 'auto', # default
)

print(result) # Detailed statistical results
#>         y    x statistic df    p.value             method alternative  estimate
#> 1: weight diet  2.508551 98 0.01376398  Two Sample t-test   two.sided 0.4978591
#>    conf.level conf.low conf.high effectsize n_obs
#> 1:       0.95 0.101465 0.8917915   Hedges_g   100

Inline results in APA style

The core function: autest() by default return a list of length 14 with detailed statistics, if inline results are desired, the lablr() function can be used.

An example using same data as before:

The analysis of the effects of the treatment, shows that experimental group had greater weight than control, inline$full.

translates into this:

The analysis of the effects of the treatment, shows that experimental group had greater weight than control, t(98) = 2.51, p = 0.014, g = 0.50, CI95
0.10, 0.89
.

It also let you perform centrality and dispersion statistics for inline results by using the cent_disp() function. The next example illustrates its usage:

data <- datasets::ToothGrowth

result <- with(data, tapply(
  X = len, 
  INDEX = list(supp, dose),
  FUN = function(var) {
    cent_disp(x = var, markdown = TRUE)
  }))

as.data.frame(result)
#>                       0.5                      1                      2
#> OJ *M* = 13.2, *SD* = 4.5 *M* = 22.7, *SD* = 3.9 *M* = 26.1, *SD* = 2.7
#> VC    *M* = 8, *SD* = 2.7 *M* = 16.8, *SD* = 2.5 *M* = 26.1, *SD* = 4.8

The effect of vitamin C on tooth growth was explored in Guinea Pigs, were the group using orange juice (OJ) demonstrated similar values (result['OJ','2']) than vitamin C (VC) group (result['VC','2']) in tooth length (TL) at 2 miligrams/day. However, at doses of 0.5 miligrams/day, the OJ group did show greater TL (result['OJ','0.5']) than VC group (result['VC','0.5']).

translates into this:

The effect of vitamin C on tooth growth was explored in Guinea Pigs, were the group using orange juice (OJ) demonstrated similar values (M = 26.1, SD = 2.7) than vitamin C (VC) group (M = 26.1, SD = 4.8) in tooth length (TL) at 2 miligrams/day. However, at doses of 0.5 miligrams/day, the OJ group did show greater TL (M = 13.2, SD = 4.5) than VC group (M = 8, SD = 2.7).

You can also set your own custom expressions using glue syntax like this:

cent_disp(
  x = data$len, 
  str.a = "The median for length was {median} mm (MAD = {mad}, IQR = {IQR})",
  k = 1 # For 1 decimal places
)
#> The median for length was 19.2 mm (MAD = 9, IQR = 12.2)

It allows you to use any function available in your global environment or in attached packages, even custom functions:

q25 <- function(i) quantile(i, 0.25)[[1L]]
q75 <- function(j) quantile(j, 0.75)[[1L]]

cent_disp(
  x = data$len,
  str.a = "The median for length was {median} mm (IQR = [{q25}, {q75}])",
  k = 1
)
#> The median for length was 19.2 mm (IQR = [13.1, 25.3])

Paired samples design

For paired designs you need to set paired = TRUE, and then, based on the numbers of groups detected after removing missing values, the test will run depending on the parameters stablished.

> 2 groups

When type = 'auto' the next assumptions will be checked for > 2 paired samples:

Assumption checked	How is tested	If met	If not
Normality	`stats::shapiro.test` for n < 50 or `nortest::lillie.test` for n >= 50	Sphericity check.	Friedman rank sum test
Sphericity	`sphericity_check(model)`	One-way repeated measures ANOVA (rmANOVA)	Greenhouse-Geisser (GG) or Huynh-Feldt (HF) correction is applied

n <- 40
set.seed(123)
cancer <- data.frame(
  id = rep(seq_len(n), 3)
  , cells = round(c(rnorm(n = n, mean = 100, sd = 15)   # Basal
           , rnorm(n = n, mean = 98, sd = 10)   # Time-1
           , rnorm(n = n, mean = 96, sd = 5) )) # Time-2
  , period = gl(n = 3, k = n, labels = c('Basal', 'Time-1', 'Time-2') ) )

result <- autest(
  data = cancer
  , x = "period"
  , y = "cells"
  , rowid = "id"
  , paired = TRUE
  , posthoc = TRUE # set to TRUE for pairwise comparisons
  )

# Access the whole results
print(result)
#> $test
#>        y      x statistic      df df.error   p.value
#> 1: cells period  2.231395 1.77965 69.40635 0.1206689
#>                                        method   estimate conf.level conf.low
#> 1: Repeated measures ANOVA with HF correction 0.01998957       0.95        0
#>    conf.high     effectsize n_obs
#> 1:         1 Omega2_partial    40
#> 
#> $pwc
#>    group1 group2 statistic         p            method p.adjust.method
#> 1: Time-1  Basal -1.466883 0.5560343 Games-Howell Test            none
#> 2: Time-2  Basal -2.933847 0.1061919 Games-Howell Test            none
#> 3: Time-2 Time-1 -1.616262 0.4922794 Games-Howell Test            none

# For inline resutls or statistical reports
lablr(result$test)
#>                  stats         p            es                ci
#> 1: F(1.8, 69.4) = 2.23 p = 0.121 omega2 = 0.02 CI95 [0.00, 1.00]
#>                                                                full
#> 1: F(1.8, 69.4) = 2.23, p = 0.121, omega2 = 0.02, CI95 [0.00, 1.00]

However, you can specify your own parameters for the selection of the test:

Test	Parameters
One-way repeated measures ANOVA (rmANOVA)	`paired = TRUE` + `type = 'p'` + `sphericity = 'none'`
rmANOVA with Greenhouse-Geisser correction	`paired = TRUE` + `type = 'p'` + `sphericity = 'GG'`
rmANOVA with Huynh-Feldt correction	`paired = TRUE` + `type = 'p'` + `sphericity = 'HF'`
Heteroscedastic rmANOVA for trimmed means	`paired = TRUE` + `type = 'r'`
Friedman rank sum test	`paired = TRUE` + `type = 'np'`

2 groups

Similar as before, if type = 'auto' assumptions will be checked for 2 paired samples:

Assumption checked	How is tested	If met	If not
Normality	`stats::shapiro.test` for n < 50 or `nortest::lillie.test` for n >= 50	Student’s t-test	Wilcoxon signed-rank test

cancer_two <- cancer[cancer$period %in% c('Time-1','Time-2'),]
  
result <- autest(
  data = cancer_two
  , x = "period"
  , y = "cells"
  , paired = TRUE
)

# Access the whole results
print(result)
#>        y      x statistic df   p.value        method alternative  estimate
#> 1: cells period  1.093978 39 0.2806758 Paired t-test   two.sided 0.1696215
#>    conf.level   conf.low conf.high effectsize n_obs
#> 1:       0.95 -0.1394006  0.480802   Hedges_g    40

# For inline results
lablr(result)
#>           stats         p       es                 ci
#> 1: t(39) = 1.09 p = 0.281 g = 0.17 CI95 [-0.14, 0.48]
#>                                                     full
#> 1: t(39) = 1.09, p = 0.281, g = 0.17, CI95 [-0.14, 0.48]

Same as above, you can specify your own parameters for the selection of the test:

Test	Parameters
Student’s t-test for paired samples	`paired = TRUE` + `type = 'p'`
Wilcoxon signed-rank test	`paired = TRUE` + `type = 'np'`
Yuen’s test on trimmed means for dependent samples	`paired = TRUE` + `type = 'r'`

Independent samples design

For independent samples you need to set paired = FALSE, and then, based on the numbers of groups detected, the test will run depending on the parameters stablished.

> 2 groups

When type = 'auto' the next assumptions will be checked for > 2 independent samples:

Assumption checked	How is tested	If met	If not
Normality	`stats::shapiro.test` for n < 50 or `nortest::lillie.test` for n >= 50	Homogeneity of variances check.	Kruskal-Wallis ANOVA
Homogeneity of variances	Levene’s test on medians with `is_var.equal()`	Fisher’s ANOVA	Welch’s ANOVA

set.seed(123)
cancer_unpaired <- data.frame(
    cells = round(c(rnorm(n = n, mean = 100, sd = 20)   # Control
           , rnorm(n = n, mean = 95, sd = 12)   # Drug A
           , rnorm(n = n, mean = 90, sd = 15) )) # Drug B
  , group = gl(n = 3, k = n, labels = c('Control', 'Drug A', 'Drug B') ) )

result <- autest(
  data = cancer_unpaired
  , x = "group"
  , y = "cells"
  , paired = FALSE
  , posthoc = TRUE
  )

# Check results
print(result)
#> $test
#>        y     x statistic df df.error    p.value        method   estimate
#> 1: cells group  4.861757  2 75.91708 0.01030964 Welch's ANOVA 0.08914428
#>    conf.level    conf.low conf.high effectsize n_obs
#> 1:       0.95 0.005281224         1     Omega2   120
#> 
#> $pwc
#>    group1  group2 statistic           p            method p.adjust.method
#> 1: Drug A Control -2.516185 0.184417805 Games-Howell Test            none
#> 2: Drug B Control -4.376442 0.007873682 Games-Howell Test            none
#> 3: Drug B  Drug A -2.482696 0.191506063 Games-Howell Test            none

# For inline results
lablr(result$test)
#>                  stats        p            es                ci
#> 1: F(2.0, 75.9) = 4.86 p = 0.01 omega2 = 0.09 CI95 [0.01, 1.00]
#>                                                               full
#> 1: F(2.0, 75.9) = 4.86, p = 0.01, omega2 = 0.09, CI95 [0.01, 1.00]

However, you can specify your own parameters for the selection of the test:

Test	Parameters
Fisher’s One-way ANOVA	`paired = FALSE` + `type = 'p'` + `var.equal = TRUE`
Welch’s One-way ANOVA	`paired = FALSE` + `type = 'p'` + `var.equal = FALSE`
Kruskal–Wallis one-way ANOVA	`paired = FALSE` + `type = 'np'`
Heteroscedastic one-way ANOVA for trimmed means	`paired = FALSE` + `type = 'r'`

2 groups

Just like above, if type = 'auto' assumptions will be checked for 2 independent samples:

Assumption checked	How is tested	If met	If not
Normality	`stats::shapiro.test` for n < 50 or `nortest::lillie.test` for n >= 50	Homogeneity of variances check.	Mann-Whitney U test
Homogeneity of variances	Levene’s test on medians with `is_var.equal()`	Student’s t-test	Welch’s t-test

result <- autest(
  data = cancer_unpaired[cancer_unpaired$group %in% c('Drug A','Drug B'),]
  , x = "group"
  , y = "cells"
  , var.equal = FALSE
  )

# For tabular results
print(result)
#>        y     x statistic df    p.value             method alternative  estimate
#> 1: cells group  1.755531 78 0.08309445  Two Sample t-test   two.sided 0.3887601
#>    conf.level    conf.low conf.high effectsize n_obs
#> 1:       0.95 -0.05074507 0.8258231   Hedges_g    80

# For inline results (e.g. manuscript)
lablr(result)
#>           stats         p       es                 ci
#> 1: t(78) = 1.76 p = 0.083 g = 0.39 CI95 [-0.05, 0.83]
#>                                                     full
#> 1: t(78) = 1.76, p = 0.083, g = 0.39, CI95 [-0.05, 0.83]

You can specify your own parameters for the selection of the test as well:

Test	Parameters
Student’s t-test for independent samples	`paired = FALSE` + `type = 'p'` + `var.equal = TRUE`
Welch’s t-test for independent samples	`paired = FALSE` + `type = 'p'` + `var.equal = FALSE`
Mann–Whitney U test	`paired = FALSE` + `type = 'np'`
Yuen’s test on trimmed means	`paired = FALSE` + `type = 'r'`

Mixed effects ANOVA

By using aov_r function is possible to get the statistical report of between/within-subject(s) factor(s) for factorial designs using afex package under the hood for statistical reporting. Let’s see an example

# set parameters to simulate data with a between and within subject factor
within <- 3
between <- 2
n <- 70

set.seed(123)
stroop <- data.frame(
  subject = rep(1:n, within),
  gender = gl(between, n/between, length = n*within, labels = c('Male','Female')),
  time = gl(within, n, length = n*within),
  score = rnorm(n*within, mean = 150, sd = 30))

# Manipulate data to generate interaction between Gender and Time
stroop <- within(stroop, {
  score[gender == 'Male' & time == 1] <- score[gender == 'Male' & time == 1]*1
  score[gender == 'Male' & time == 2] <- score[gender == 'Male' & time == 2]*1.15
  score[gender == 'Male' & time == 3] <- score[gender == 'Male' & time == 3]*1.3
  score[gender == 'Female' & time == 1] <- score[gender == 'Female' & time == 1]*1
  score[gender == 'Female' & time == 2] <- score[gender == 'Female' & time == 2]*0.85
  score[gender == 'Female' & time == 3] <- score[gender == 'Female' & time == 3]*0.7
})


result <- aov_r(
  data = stroop
, response = "score"
, between = "gender"
, within = "time"
, rowid = "subject"
, effsize.type = 'omega' # omega squared as our measure of effect size
, sphericity = 'auto' # check if sphericity is not being violated
)

# Check results
print(result)
#>        y           x   statistic df df.error      p.value
#> 1: score      gender 130.7357382  1       68 1.720992e-17
#> 2: score        time   0.2367333  2      136 7.895263e-01
#> 3: score gender:time  42.8799011  2      136 3.635914e-15
#>                              method    estimate conf.level  conf.low conf.high
#> 1:                   Fisher's ANOVA  0.64953693       0.95 0.5389314         1
#> 2: Fisher's repeated measures ANOVA -0.00747718       0.95 0.0000000         1
#> 3: Fisher's repeated measures ANOVA  0.28938040       0.95 0.1844234         1
#>        effectsize n_obs
#> 1: Omega2_partial    70
#> 2: Omega2_partial    70
#> 3: Omega2_partial    70

# And inline results for reporting purposes
inline <- result[j = lablr(.SD), keyby = x]

print(inline)
#>              x             stats         p             es                ci
#> 1:      gender F(1, 68) = 130.74 p < 0.001  omega2 = 0.65 CI95 [0.54, 1.00]
#> 2: gender:time F(2, 136) = 42.88 p < 0.001  omega2 = 0.29 CI95 [0.18, 1.00]
#> 3:        time  F(2, 136) = 0.24  p = 0.79 omega2 = -0.01 CI95 [0.00, 1.00]
#>                                                              full
#> 1: F(1, 68) = 130.74, p < 0.001, omega2 = 0.65, CI95 [0.54, 1.00]
#> 2: F(2, 136) = 42.88, p < 0.001, omega2 = 0.29, CI95 [0.18, 1.00]
#> 3:  F(2, 136) = 0.24, p = 0.79, omega2 = -0.01, CI95 [0.00, 1.00]

For inline results with previous data we would do something like this:

In order to analyze the effect of gender on subjects’ scores in each of the evaluation periods, we performed an analysis of variance (ANOVA) with between- and within-subjects factors. From the analyses, we find that gender has a large effect ( inline["gender", paste(es, ci, sep = ", ")] ) on scores when adjusting for each of the time periods, inline["gender", paste(stats, p, sep = ", ")]. In a similar way we find a significant interaction between evaluative time and gender ( inline["gender:time", paste(stats, p, sep = ", ")] ), indicating unequal responses between males and females over time, inline["gender:time", paste(es, ci, sep = ", ")], however, time alone is not able to explain statistically significantly the variance in scores, inline["time"]$full.

Which will translate into this after evaluation in R Markdown:

In order to analyze the effect of gender on subjects’ scores in each of the evaluation periods, we performed an analysis of variance (ANOVA) with between- and within-subjects factors. From the analyses, we find that gender has a large effect ( omega2 = 0.65, CI95
0.51, 0.74
) on scores when adjusting for each of the time periods, F(1, 68) = 130.74, p < 0.001. In a similar way we find a significant interaction between evaluative time and gender ( F(2, 136) = 42.88, p < 0.001 ), indicating unequal responses between males and females over time, omega2 = 0.29, CI95
0.17, 0.40
, however, time alone is not able to explain statistically significantly the variance in scores, F(2, 136) = 0.24, p = 0.79, omega2 = -0.01, CI95
0.00, 0.00
.

When you have more than 1 factor (between or within subjects) you have to specify them as a character vector: between = c('factor1', 'factor2' ...), and the same for within = c('factor1', 'factor2' ...).

Testing categorical data

To test purely categorical data, contingency() function is your guy.

Goodness-of-fit Chi-squared

By only filling the data, and x argument, the Goodness-of-fit chi-squared test (χ²_gof)

result <- contingency(
  data = cancer_unpaired[-(1:10),], # 3 groups: Control, Drug A, Drug B
  x = "group"
)

# Tabular format dropping empty columns
print(result)
#>        x statistic df   p.value                                   method
#> 1: group  1.818182  2 0.4028903 Chi-squared test for given probabilities
#>     estimate conf.level conf.low conf.high effectsize n_obs
#> 1: 0.1275153       0.95        0         1 pearsons_c   110

# For inline results
inline <- lablr(result, markdown = T)

And the inline result would look like this:

In preliminary analyses, we’ve seen that the proportion of pacients the same across groups, inline$full.

translates into:

In preliminary analyses, we’ve seen that the proportion of pacients the same across groups, X2(2) = 0.00, p = 1, V = 0.00, CI95
0.00, 0.00
.

Pearson’s Chi-squared

By providing x and y arguments on contingency() you get Pearson’s Chi-squared test.

result <- contingency(
  data = mtcars, # Using mtcars data
  x = "cyl",
  y = "gear"
)

# Statistics in tabular format
print(result)
#>       y   x statistic df     p.value                     method  estimate
#> 1: gear cyl  18.03636  4 0.001214066 Pearson's Chi-squared test 0.5308655
#>    conf.level  conf.low conf.high effectsize n_obs
#> 1:       0.95 0.2629954         1  Cramers_v    32

# Inline results format
lablr(result)
#>            stats         p       es                ci
#> 1: X2(4) = 18.04 p = 0.001 V = 0.53 CI95 [0.26, 1.00]
#>                                                     full
#> 1: X2(4) = 18.04, p = 0.001, V = 0.53, CI95 [0.26, 1.00]

Fisher’s exact test

Otherwise, you could use Fisher’s exact test for count data if you specify exact = TRUE.

result <- contingency(
  data = mtcars, 
  x = "cyl",
  y = "gear",
  exact = TRUE 
)

# Statistics in tabular format
print(result)
#>       y   x      p.value                                        method n_obs
#> 1: gear cyl 8.259716e-05 Fisher's Exact Test for Count Data without OR    32

# Inline results format
lablr(result)
#>            p      full
#> 1: p < 0.001 p < 0.001

McNemar’s Chi-squared Test

If you have a paired design and are using only categorical variables, then McNemar’s Chi-squared Test for Count data is your test to go.

## Presidential Approval Ratings.
## Approval of the President's performance in office in two surveys,
## one month apart, for a random sample of 1600 voting-age Americans.

performance <- data.frame(
  id = rep(1:1600, 2),
  `1st survey` = c(rep("Approve", 944), rep("Disapprove", 656)),
  `2nd survey` = c(rep("Approve", 794), rep("Disapprove", 150),
                   rep("Approve", 86), rep("Disapprove", 570)), check.names = F)

result <- contingency(
  data = performance,
  x = "1st survey",
  y = "2nd survey",
  paired = TRUE # Set TRUE for McNemar test
)

# Statistics in tabular format
print(result)
#>             y          x statistic df      p.value
#> 1: 2nd survey 1st survey  34.17161  1 5.045976e-09
#>                                                   method  estimate conf.level
#> 1: McNemar's Chi-squared test with continuity correction 0.1355932       0.95
#>      conf.low conf.high effectsize n_obs
#> 1: 0.09124332 0.1777538   Cohens_g  3200

# Inline results
lablr(result)
#>            stats         p       es                ci
#> 1: X2(1) = 34.17 p < 0.001 g = 0.14 CI95 [0.09, 0.18]
#>                                                     full
#> 1: X2(1) = 34.17, p < 0.001, g = 0.14, CI95 [0.09, 0.18]

Dependencies

The package writR is standing on the shoulders of giants. writR depends on the following packages:

deepdep::plot_dependencies('writR', local = TRUE, depth = 3)

Acknowledgments

I would like to thank to developers of statsExpressions and ggstatsplot for being an inspiration for this package. Naturally this package is in its first steps, but I hope that future collaborative work can expand the potential of this package.

Citation

To cite package ‘writR’ in publications run the following code in your R console:

citation('writR')
#> 
#> To cite package 'writR' in publications use:
#> 
#>   Matías Castillo Aguilar (2021). writR: Inferential statistics and
#>   reporting in APA style. https://doi.org/10.5281/zenodo.4603838,
#>   https://matcasti.github.io/writR.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Manual{,
#>     title = {writR: Inferential statistics and reporting in APA style},
#>     author = {Matías {Castillo Aguilar}},
#>     year = {2021},
#>     note = {https://doi.org/10.5281/zenodo.4603838,
#> https://matcasti.github.io/writR},
#>   }

writR

Installation

Summary of available tests using `autest()` function

For paired samples designs

For independent samples design

Corresponding Post-Hoc tests for Nº groups > 2

Available effect sizes

Automated testing

Inline results in APA style

Paired samples design

> 2 groups

2 groups

Independent samples design

> 2 groups

2 groups

Mixed effects ANOVA

Testing categorical data

Goodness-of-fit Chi-squared

Pearson’s Chi-squared

Fisher’s exact test

McNemar’s Chi-squared Test

Dependencies

Acknowledgments

Citation

Links

License

Developers

Dev status

writR

Installation

Summary of available tests using autest() function

For paired samples designs

For independent samples design

Corresponding Post-Hoc tests for Nº groups > 2

Available effect sizes

Automated testing

Inline results in APA style

Paired samples design

> 2 groups

2 groups

Independent samples design

> 2 groups

2 groups

Mixed effects ANOVA

Testing categorical data

Goodness-of-fit Chi-squared

Pearson’s Chi-squared

Fisher’s exact test

McNemar’s Chi-squared Test

Dependencies

Acknowledgments

Citation

Links

License

Developers

Dev status

Summary of available tests using `autest()` function