R version 4.4.0 beta (2024-04-12 r86413 ucrt) -- "Puppy Cup"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64

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> library(testthat)
> library(betaNB)
> 
> test_check("betaNB")
Call:
BetaNB(object = nb)

Standardized regression slopes
type = "pc"
           est     se R  0.05%   0.5%   2.5%  97.5%  99.5% 99.95%
NARTIC  0.4951 0.0328 5 0.4386 0.4389 0.4403 0.5199 0.5221 0.5226
PCTGRT  0.3915 0.0869 5 0.2636 0.2662 0.2773 0.4971 0.5051 0.5069
PCTSUPP 0.2632 0.1156 5 0.1858 0.1866 0.1902 0.4549 0.4621 0.4638
Call:
BetaNB(object = nb)

Standardized regression slopes
type = "pc"
Call:
BetaNB(object = nb)

Standardized regression slopes
type = "bc"
Call:
BetaNB(object = nb)

Standardized regression slopes
type = "bc"
           est     se R  0.05%   0.5%   2.5%  97.5%  99.5% 99.95%
NARTIC  0.4951 0.0328 5 0.4424 0.4517 0.4677 0.5227 0.5227 0.5227
PCTGRT  0.3915 0.0869 5 0.2634 0.2634 0.2634 0.4064 0.4327 0.4855
PCTSUPP 0.2632 0.1156 5 0.1862 0.1892 0.1989 0.4615 0.4636 0.4639
Call:
BetaNB(object = nb)

Standardized regression slopes
type = "bca"
Call:
BetaNB(object = nb)

Standardized regression slopes
type = "bca"
           est     se R  0.05%   0.5%   2.5%  97.5%  99.5% 99.95%
NARTIC  0.4951 0.0328 5 0.4426 0.4520 0.4679 0.5227 0.5227 0.5227
PCTGRT  0.3915 0.0869 5 0.2634 0.2634 0.2635 0.4064 0.4391 0.4904
PCTSUPP 0.2632 0.1156 5 0.1859 0.1882 0.1972 0.4606 0.4633 0.4639
Call:
BetaNB(object = nb)

Standardized regression slopes
type = "pc"
          est     se R  0.05%   0.5%   2.5%  97.5%  99.5% 99.95%
NARTIC 0.7622 0.0642 5 0.6776 0.6783 0.6812 0.8255 0.8278 0.8283
Call:
BetaNB(object = nb)

Standardized regression slopes
type = "pc"
Call:
BetaNB(object = nb)

Standardized regression slopes
type = "bc"
Call:
BetaNB(object = nb)

Standardized regression slopes
type = "bc"
          est     se R  0.05%   0.5%   2.5% 97.5%  99.5% 99.95%
NARTIC 0.7622 0.0642 5 0.6775 0.6777 0.6785  0.82 0.8262 0.8281
Call:
BetaNB(object = nb)

Standardized regression slopes
type = "bca"
Call:
BetaNB(object = nb)

Standardized regression slopes
type = "bca"
          est     se R  0.05%   0.5%   2.5%  97.5%  99.5% 99.95%
NARTIC 0.7622 0.0642 5 0.6775 0.6776 0.6781 0.8184 0.8251 0.8277
Call:
DeltaRSqNB(object = nb)

Improvement in R-squared
type = "pc"
           est     se R  0.05%   0.5%   2.5%  97.5%  99.5% 99.95%
NARTIC  0.1859 0.0306 5 0.1773 0.1778 0.1796 0.2542 0.2559 0.2563
PCTGRT  0.1177 0.0446 5 0.0411 0.0423 0.0476 0.1576 0.1596 0.1601
PCTSUPP 0.0569 0.0109 5 0.0191 0.0192 0.0199 0.0450 0.0452 0.0453
Call:
DeltaRSqNB(object = nb)

Improvement in R-squared
type = "pc"
Call:
DiffBetaNB(object = nb)

Differences of standardized regression slopes
type = "pc"
                  est     se R   0.05%    0.5%    2.5%  97.5%  99.5% 99.95%
NARTIC-PCTGRT  0.1037 0.0994 5 -0.0449 -0.0449 -0.0446 0.1807 0.1948 0.1979
NARTIC-PCTSUPP 0.2319 0.1573 5  0.0440  0.0454  0.0517 0.4355 0.4517 0.4553
PCTGRT-PCTSUPP 0.1282 0.1994 5 -0.0751 -0.0722 -0.0590 0.4318 0.4439 0.4467
Call:
DiffBetaNB(object = nb)

Differences of standardized regression slopes
type = "pc"
Call:
NB(object = object, R = 6)

The first six bootstrap covariance matrices.

[[1]]
          [,1]       [,2]       [,3]
[1,] 1.0099124 0.53370896 0.47994262
[2,] 0.5337090 1.07034018 0.04362108
[3,] 0.4799426 0.04362108 0.99932510

[[2]]
          [,1]        [,2]        [,3]
[1,] 0.9528211  0.44685052  0.48008660
[2,] 0.4468505  0.98824535 -0.02845785
[3,] 0.4800866 -0.02845785  1.04559557

[[3]]
          [,1]        [,2]        [,3]
[1,] 1.0588165 0.540911948 0.480732629
[2,] 0.5409119 1.038247062 0.009921316
[3,] 0.4807326 0.009921316 1.002934084

[[4]]
          [,1]       [,2]       [,3]
[1,] 1.0611349 0.48205132 0.50436155
[2,] 0.4820513 0.99659705 0.00559072
[3,] 0.5043616 0.00559072 0.96031537

[[5]]
          [,1]       [,2]       [,3]
[1,] 1.1187257 0.55970856 0.56140845
[2,] 0.5597086 1.02203524 0.02477157
[3,] 0.5614085 0.02477157 1.12240179

[[6]]
          [,1]       [,2]       [,3]
[1,] 1.0318549 0.54098946 0.47090224
[2,] 0.5409895 1.13249519 0.03964982
[3,] 0.4709022 0.03964982 0.96126856

Call:
PCorNB(object = nb)

Squared partial correlations
type = "pc"
           est     se R  0.05%   0.5%   2.5%  97.5%  99.5% 99.95%
NARTIC  0.4874 0.0989 5 0.3606 0.3617 0.3666 0.6083 0.6149 0.6164
PCTGRT  0.3757 0.0819 5 0.2892 0.2901 0.2942 0.4883 0.4928 0.4938
PCTSUPP 0.2254 0.1261 5 0.0114 0.0155 0.0339 0.3196 0.3208 0.3211
Call:
PCorNB(object = nb)

Squared partial correlations
type = "pc"
Call:
RSqNB(object = nb)

R-squared and adjusted R-squared
type = "pc"
       est     se R  0.05%   0.5%   2.5%  97.5%  99.5% 99.95%
rsq 0.8045 0.0545 5 0.7330 0.7347 0.7423 0.8743 0.8764 0.8769
adj 0.7906 0.0584 5 0.7139 0.7158 0.7239 0.8653 0.8676 0.8681
Call:
RSqNB(object = nb)

R-squared and adjusted R-squared
type = "pc"
Call:
RSqNB(object = nb)

R-squared and adjusted R-squared
type = "pc"
       est     se R  0.05%   0.5%   2.5%  97.5%  99.5% 99.95%
rsq 0.5809 0.0750 5 0.5512 0.5526 0.5591 0.7435 0.7468 0.7476
adj 0.5714 0.0767 5 0.5410 0.5425 0.5490 0.7377 0.7411 0.7418
Call:
RSqNB(object = nb)

R-squared and adjusted R-squared
type = "pc"
Call:
SCorNB(object = nb)

Semipartial correlations
type = "pc"
           est     se R  0.05%   0.5%   2.5%  97.5%  99.5% 99.95%
NARTIC  0.4312 0.0741 5 0.3129 0.3147 0.3226 0.5038 0.5065 0.5071
PCTGRT  0.3430 0.1078 5 0.2347 0.2351 0.2368 0.4863 0.4959 0.4980
PCTSUPP 0.2385 0.0413 5 0.1942 0.1943 0.1951 0.2830 0.2835 0.2836
Call:
SCorNB(object = nb)

Semipartial correlations
type = "pc"
[ FAIL 0 | WARN 0 | SKIP 0 | PASS 12 ]
> 
> proc.time()
   user  system elapsed 
   0.96    0.15    1.09