R version 4.6.0 beta (2026-04-10 r89860 ucrt) -- "Because it was There"
Copyright (C) 2026 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(testthat)
> library(statAPA)
statAPA 0.1.0 - APA 7e tables, plots, and multilevel reports.
  Use set_apa_style() to configure global output options.
> 
> test_check("statAPA")
Descriptive Statistics

 Variable     M   SD  N
      mpg 20.09 6.03 32
       wt  3.22 0.98 32

Note. M = Mean; SD = Standard Deviation; N = number of observations. 
Descriptive Statistics by cyl

 Variable    6: M (SD)    4: M (SD)    8: M (SD) Total: M (SD)  N
      mpg 26.66 (4.51) 19.74 (1.45) 15.10 (2.56)  20.09 (6.03) 32

Note. M = Mean; SD = Standard Deviation; N = number of observations. 
ANOVA Results

    Source     SS df     MS     F      p  eta2
       cyl 824.78  2 412.39 39.70 < .001 0.732
 Residuals 301.26 29  10.39  <NA>   <NA>  <NA>

SS = Sum of Squares; MS = Mean Square; eta2 = eta squared. Type II sums of squares. 
ANOVA Results

    Source     SS df     MS     F      p partial_eta2
       cyl 824.78  2 412.39 39.70 < .001        0.732
 Residuals 301.26 29  10.39  <NA>   <NA>         <NA>

SS = Sum of Squares; MS = Mean Square; partial_eta2 = partial eta squared. Type II sums of squares. 
ANCOVA Results

    Source     SS df     MS     F      p partial_eta2
       cyl  95.26  2  47.63  7.29   .003        0.342
        wt 118.20  1 118.20 18.08 < .001        0.392
 Residuals 183.06 28   6.54  <NA>   <NA>         <NA>

Note. Analysis of Covariance (ANCOVA) with covariate(s):  wt .  SS = Sum of Squares; MS = Mean Square;  partial_eta2  =  partial eta squared .  Type  II  sums of squares.  Adjusted means estimated at covariate mean(s) via emmeans. 
Covariate-Adjusted Means for cyl

 cyl Adjusted M    SE         95% CI
   4     23.678 1.043 [21.54, 25.81]
   6     19.422 0.969 [17.44, 21.41]
   8     17.607 0.903 [15.76, 19.45]
Two-Way ANOVA Results

    Source     SS df     MS     F      p partial_eta2
       cyl 349.79  2 174.90 15.60 < .001        0.565
      gear   8.25  2   4.13  0.37   .696        0.030
  cyl:gear  23.89  3   7.96  0.71   .555        0.082
 Residuals 269.12 24  11.21  <NA>   <NA>         <NA>

Note. Two-Way Analysis of Variance (ANOVA).  SS = Sum of Squares; MS = Mean Square;  partial_eta2  =  partial eta squared .  Type  II  sums of squares.  Simple effects of cyl within levels of gear computed via emmeans::joint_tests(). 
Simple Effects of cyl Within gear

       Source df1 df2     F    p
 cyl | gear=3   2  24  3.08 .065
 cyl | gear=4   1  24 12.24 .002
 cyl | gear=5   2  24  7.46 .003
MANOVA Results

  Effect             Test    Stat approx F num df den df      p  eta2
 Species           Pillai  0.9885    71.83      4 294.00 < .001 0.494
 Species            Wilks  0.0399   292.56      4 292.00 < .001 0.800
 Species Hotelling-Lawley 23.3647   846.97      4 290.00 < .001 0.921
 Species              Roy 23.3342  1715.06      2 147.00 < .001 0.959

Note. Pillai = Pillai's trace; Wilks = Wilks' lambda;  Hotelling-Lawley = Hotelling-Lawley trace; Roy = Roy's largest root.  approx F = approximate F-statistic; num df = numerator degrees of freedom;  den df = denominator degrees of freedom.  eta2 = eta-squared, approximated from F-ratio and degrees of freedom.  Type II sums of squares. 
Post-hoc Pairwise Comparisons

    Contrast Estimate   SE    t      p        95% CI
 cyl4 - cyl6     6.92 1.56 4.44 < .001 [3.07, 10.77]
 cyl4 - cyl8    11.56 1.30 8.90 < .001 [8.36, 14.77]
 cyl6 - cyl8     4.64 1.49 3.11   .011  [0.96, 8.33]

Note. Estimates are pairwise differences of marginal means. Contrast method = pairwise; adjusted using tukey; confidence level = 0.95. 
Heteroscedasticity Diagnostics

                        Test Stat df    p
               Breusch-Pagan 0.88  2 .644
 Non-Constant Variance (NCV) 0.70  1 .402

Note. Breusch-Pagan uses studentized residuals.  
Homoskedasticity Check

          Test Stat df    p
 Breusch-Pagan 0.88  2 .644

Note. Breusch-Pagan and White tests are sensitive to functional form. Levene/Brown-Forsythe not run (no 'group' provided).  At alpha = 0.05, no test detected heteroskedasticity (results are consistent with homoskedastic errors). 
Linear Model (HC3 robust SEs)

   Predictor     b   SE     t      p         95% CI
 (Intercept) 37.23 2.23 16.70 < .001 [32.86, 41.60]
          wt -3.88 0.77 -5.05 < .001 [-5.38, -2.37]
          hp -0.03 0.01 -3.39   .002 [-0.05, -0.01]

Note. Unstandardized coefficients reported. Standard errors are HC3 robust via sandwich/lmtest; 95% CIs use normal approximation. 
Linear Model Predicting mpg

   Predictor     b   SE     t      p         95% CI
 (Intercept) 37.23 1.60 23.28 < .001 [33.96, 40.50]
          wt -3.88 0.63 -6.13 < .001 [-5.17, -2.58]
          hp -0.03 0.01 -3.52   .001 [-0.05, -0.01]

Note. Unstandardized coefficients reported. SE = Standard Error; CI = Confidence Interval. p-values based on t tests. N (Level 1) = 32. 
[ FAIL 0 | WARN 0 | SKIP 0 | PASS 52 ]
> 
> proc.time()
   user  system elapsed 
   2.84    0.54    3.35