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

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writR

DOI Lifecycle: experimental CRAN status R-CMD-check

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, k_sample(), 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 <- k_sample( 
  data = diets, 
  x = "diet", # independent variable
  y = "weight", # dependent variable
  type = NULL, # default, checks assumptions then choose appropiate test
)

print(result) # Detailed statistical results
#>         y      x statistic    df df.error    p.value         method   estimate
#>    <char> <char>     <num> <num>    <num>      <num>         <char>      <num>
#> 1: weight   diet  6.292829     1       98 0.01376398 Fisher's ANOVA 0.05026771
#>    conf.level    conf.low conf.high              effectsize n_obs
#>         <num>       <num>     <num>                  <char> <int>
#> 1:       0.95 0.003432758         1 Omega-squared (partial)   100

Inline results in APA style

The core function: k_sample() by default return a list of length 13 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, F(1, 98) = 6.29, p 0.014, omega2 = 0.05, CI95 [0.00, 1.00].

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(
  len,              ## Variable to describe
  list(supp, dose), ## Variables to aggregate on
  cent_disp         ## cent_disp() function
  ))

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 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 <- k_sample(
  data = cancer
  , x = "period"
  , y = "cells"
  , rowid = "id"
  , paired = TRUE
  )

# Access the whole results
print(result)
#>         y      x statistic      df df.error   p.value                method
#>    <char> <char>     <num>   <num>    <num>     <num>                <char>
#> 1:  cells period  2.231395 1.77965 69.40635 0.1206689 Huynh-Feldt's rmANOVA
#>      estimate conf.level conf.low conf.high              effectsize n_obs
#>         <num>      <num>    <num>     <num>                  <char> <num>
#> 1: 0.01998957       0.95        0         1 Omega-squared (partial)    40

# For inline resutls or statistical reports
lablr(result)
#>                  stats       p            es                ci
#>                 <char>  <char>        <char>            <char>
#> 1: F(1.8, 69.4) = 2.23 p 0.121 omega2 = 0.02 CI95 [0.00, 1.00]
#>                                                              full
#>                                                            <char>
#> 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 Student’s t-test Wilcoxon signed-rank test 
cancer_two <- cancer[cancer$period %in% c('Time-1','Time-2'),]
  
result <- k_sample(
  data = cancer_two
  , x = "period"
  , y = "cells"
  , paired = TRUE
)

# Access the whole results
print(result)
#>         y      x statistic    df df.error   p.value           method   estimate
#>    <char> <char>     <num> <num>    <num>     <num>           <char>      <num>
#> 1:  cells period  1.196787     1       39 0.2806758 Fisher's rmANOVA 0.00267743
#>    conf.level conf.low conf.high              effectsize n_obs
#>         <num>    <num>     <num>                  <char> <num>
#> 1:       0.95        0         1 Omega-squared (partial)    40

# For inline results
lablr(result)
#>              stats       p            es                ci
#>             <char>  <char>        <char>            <char>
#> 1: F(1, 39) = 1.20 p 0.281 omega2 = 0.00 CI95 [0.00, 1.00]
#>                                                          full
#>                                                        <char>
#> 1: F(1, 39) = 1.20, p 0.281, omega2 = 0.00, CI95 [0.00, 1.00]

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

# Check results
print(result)
#>         y      x statistic    df df.error    p.value        method   estimate
#>    <char> <char>     <num> <num>    <num>      <num>        <char>      <num>
#> 1:  cells  group  4.861757     2 75.91708 0.01030964 Welch's ANOVA 0.08914428
#>    conf.level    conf.low conf.high              effectsize n_obs
#>         <num>       <num>     <num>                  <char> <int>
#> 1:       0.95 0.005281224         1 Omega-squared (partial)   120

# For inline results
lablr(result)
#>                  stats       p            es                ci
#>                 <char>  <char>        <char>            <char>
#> 1: F(2.0, 75.9) = 4.86 p 0.010 omega2 = 0.09 CI95 [0.01, 1.00]
#>                                                              full
#>                                                            <char>
#> 1: F(2.0, 75.9) = 4.86, p 0.010, 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 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 <- k_sample(
  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 df.error    p.value         method   estimate
#>    <char> <char>     <num> <num>    <num>      <num>         <char>      <num>
#> 1:  cells  group   3.08189     1       78 0.08309445 Fisher's ANOVA 0.02536358
#>    conf.level conf.low conf.high              effectsize n_obs
#>         <num>    <num>     <num>                  <char> <int>
#> 1:       0.95        0         1 Omega-squared (partial)    80

# For inline results (e.g. manuscript)
lablr(result)
#>              stats       p            es                ci
#>             <char>  <char>        <char>            <char>
#> 1: F(1, 78) = 3.08 p 0.083 omega2 = 0.03 CI95 [0.00, 1.00]
#>                                                          full
#>                                                        <char>
#> 1: F(1, 78) = 3.08, p 0.083, omega2 = 0.03, CI95 [0.00, 1.00]

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           method
#>    <char>      <char>       <num> <num>    <num>        <num>           <char>
#> 1:  score      gender 130.7357382     1       68 1.720992e-17   Fisher's ANOVA
#> 2:  score        time   0.2367333     2      136 7.895263e-01 Fisher's rmANOVA
#> 3:  score gender:time  42.8799011     2      136 3.635914e-15 Fisher's rmANOVA
#>     estimate conf.level  conf.low conf.high     effectsize n_obs
#>        <num>      <num>     <num>     <num>         <char> <int>
#> 1: 0.6495369       0.95 0.5389314         1 Omega2_partial    70
#> 2: 0.0000000       0.95 0.0000000         1 Omega2_partial    70
#> 3: 0.2893804       0.95 0.1844234         1 Omega2_partial    70

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

print(inline[,c("x", "full")])
#> Key: <x>
#>              x                                                         full
#>         <char>                                                       <char>
#> 1:      gender F(1, 68) = 130.74, p 2e-17, omega2 = 0.65, CI95 [0.54, 1.00]
#> 2: gender:time F(2, 136) = 42.88, p 4e-15, omega2 = 0.29, CI95 [0.18, 1.00]
#> 3:        time  F(2, 136) = 0.24, p 0.790, omega2 = 0.00, 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 [0.54, 1.00]) 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 (χ2gof)

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
#>    <char>     <num> <num>     <num>                                   <char>
#> 1:  group  1.818182     2 0.4028903 Chi-squared test for given probabilities
#>     estimate conf.level conf.low conf.high  effectsize
#>        <num>      <num>    <num>     <num>      <char>
#> 1: 0.1275153       0.95        0         1 Pearson's C

# 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
#>    <char> <char>     <num> <int>       <num>                     <char>
#> 1:   gear    cyl  18.03636     4 0.001214066 Pearson's Chi-squared test
#>     estimate conf.level   conf.low conf.high effectsize
#>        <num>      <num>      <num>     <num>     <char>
#> 1: 0.4819631       0.95 0.07050663         1 Cramer's V

# Inline results format
lablr(result)
#>            stats       p       es                ci
#>           <char>  <char>   <char>            <char>
#> 1: X2(4) = 18.04 p 0.001 V = 0.48 CI95 [0.07, 1.00]
#>                                                   full
#>                                                 <char>
#> 1: X2(4) = 18.04, p 0.001, V = 0.48, CI95 [0.07, 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 statistic    df     p.value                     method
#>    <char> <char>     <num> <int>       <num>                     <char>
#> 1:   gear    cyl  18.03636     4 0.001214066 Pearson's Chi-squared test
#>     estimate conf.level   conf.low conf.high effectsize
#>        <num>      <num>      <num>     <num>     <char>
#> 1: 0.4819631       0.95 0.07050663         1 Cramer's V

# Inline results format
lablr(result)
#>            stats       p       es                ci
#>           <char>  <char>   <char>            <char>
#> 1: X2(4) = 18.04 p 0.001 V = 0.48 CI95 [0.07, 1.00]
#>                                                   full
#>                                                 <char>
#> 1: X2(4) = 18.04, p 0.001, V = 0.48, CI95 [0.07, 1.00]

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
#>        <char>     <char>     <num> <num>        <num>
#> 1: 2nd survey 1st survey  34.71186     1 3.822946e-09
#>                        method  estimate conf.level   conf.low conf.high
#>                        <char>     <num>      <num>      <num>     <num>
#> 1: McNemar's Chi-squared test 0.1355932       0.95 0.09124332 0.1777538
#>    effectsize
#>        <char>
#> 1:  Cohen's g

# Inline results
lablr(result)
#>            stats       p       es                ci
#>           <char>  <char>   <char>            <char>
#> 1: X2(1) = 34.71 p 4e-09 g = 0.14 CI95 [0.09, 0.18]
#>                                                   full
#>                                                 <char>
#> 1: X2(1) = 34.71, p 4e-09, 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:
#> 
#>   Castillo Aguilar M (2021). _writR: Inferential statistics and
#>   reporting in APA style_. R package version 1.0.1,
#>   <https://github.com/matcasti/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 = {R package version 1.0.1},
#>     url = {https://github.com/matcasti/writR},
#>   }

About

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

matcasti.github.io/writR

Topics

statistics apa reporting fisher t-test robust inferential-statistics assumptions parametric anova kruskal-wallis wilcoxon friedman apa-style repeated-measures-anova paired-samples independent-samples rank-sums inferential-testing

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GPL-3.0 license
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writR Latest
Aug 6, 2021
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