R Under development (unstable) (2025-01-09 r87552 ucrt) -- "Unsuffered Consequences"
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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 0.0638 5 0.3444 0.3457 0.3512 0.5116 0.5161 0.5172
PCTGRT  0.3915 0.0230 5 0.3747 0.3750 0.3765 0.4323 0.4334 0.4336
PCTSUPP 0.2632 0.0551 5 0.2129 0.2135 0.2161 0.3371 0.3382 0.3385
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 0.0638 5 0.3591 0.3953 0.4320 0.5172 0.5173 0.5173
PCTGRT  0.3915 0.0230 5 0.3747 0.3747 0.3747 0.4093 0.4232 0.4306
PCTSUPP 0.2632 0.0551 5 0.2128 0.2130 0.2137 0.3345 0.3374 0.3384
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 0.0638 5 0.3600 0.3964 0.4323 0.5172 0.5173 0.5173
PCTGRT  0.3915 0.0230 5 0.3747 0.3747 0.3747 0.4100 0.4241 0.4313
PCTSUPP 0.2632 0.0551 5 0.2128 0.2129 0.2135 0.3339 0.3371 0.3383
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 0.0915 5 0.6715 0.6718 0.6732 0.8719 0.8759 0.8768
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 0.0915 5 0.6714 0.6715 0.6719 0.8622 0.873 0.8763
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 0.0915 5 0.6714 0.6715 0.6717 0.8594 0.8711 0.8756
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 0.0694 5 0.1247 0.1248 0.1254 0.2828 0.2869 0.2879
PCTGRT  0.1177 0.0414 5 0.0494 0.0495 0.0504 0.1394 0.1400 0.1402
PCTSUPP 0.0569 0.0154 5 0.0125 0.0127 0.0138 0.0520 0.0534 0.0537
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 0.2539 5 -0.3150 -0.3133 -0.3057 0.2767 0.2869 0.2892
NARTIC-PCTSUPP 0.2319 0.1022 5  0.1068  0.1097  0.1223 0.3631 0.3648 0.3652
PCTGRT-PCTSUPP 0.1282 0.1932 5  0.0758  0.0763  0.0784 0.5047 0.5121 0.5138
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 12 | PASS 0 ]

══ Skipped tests (12) ══════════════════════════════════════════════════════════
• On CRAN (12): 'test-betaNB-beta-nb-est.R:36:9',
  'test-betaNB-delta-r-sq-nb-est.R:31:9',
  'test-betaNB-delta-r-sq-nb-est.R:49:9',
  'test-betaNB-diff-beta-nb-est.R:30:9', 'test-betaNB-diff-beta-nb-est.R:49:9',
  'test-betaNB-nb.R:83:9', 'test-betaNB-p-cor-nb-est.R:30:9',
  'test-betaNB-p-cor-nb-est.R:49:9', 'test-betaNB-r-sq-nb-est.R:33:9',
  'test-betaNB-r-sq-nb-est.R:57:9', 'test-betaNB-s-cor-nb-est.R:30:9',
  'test-betaNB-s-cor-nb-est.R:49:9'

[ FAIL 0 | WARN 0 | SKIP 12 | PASS 0 ]
> 
> proc.time()
   user  system elapsed 
   0.92    0.07    1.00