R Under development (unstable) (2023-10-13 r85327 ucrt) -- "Unsuffered Consequences"
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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 0.0444 5 0.3998 0.4004 0.4029 0.5059 0.5069 0.5071
PCTGRT  0.3915 0.0564 5 0.4180 0.4182 0.4189 0.5450 0.5523 0.5539
PCTSUPP 0.2632 0.0672 5 0.1669 0.1672 0.1689 0.3238 0.3285 0.3295
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 0.0444 5 0.4065 0.4229 0.4458 0.5072 0.5072 0.5072
PCTGRT  0.3915 0.0564 5 0.4180 0.4180 0.4180 0.4180 0.4180 0.4180
PCTSUPP 0.2632 0.0672 5 0.1671 0.1684 0.1729 0.3280 0.3294 0.3296
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 0.0444 5 0.4069 0.4234 0.4460 0.5072 0.5072 0.5072
PCTGRT  0.3915 0.0564 5    NaN    NaN    NaN    NaN    NaN    NaN
PCTSUPP 0.2632 0.0672 5 0.1669 0.1680 0.1721 0.3275 0.3292 0.3296
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 0.0591 5 0.6824 0.6836 0.6891 0.8328 0.8348 0.8353
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 0.0591 5 0.6823 0.6825 0.6841 0.828 0.8334  0.835
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 0.0591 5 0.6822 0.6823 0.6833 0.8266 0.8324 0.8347
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 0.0373 5 0.1652 0.1658 0.1684 0.2622 0.2658 0.2667
PCTGRT  0.1177 0.0495 5 0.0219 0.0226 0.0257 0.1502 0.1550 0.1561
PCTSUPP 0.0569 0.0219 5 0.0221 0.0223 0.0231 0.0689 0.0690 0.0690
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 0.1303 5 -0.0553 -0.0539 -0.0476 0.2484 0.2509 0.2514
NARTIC-PCTSUPP 0.2319 0.0760 5  0.1545  0.1546  0.1553 0.3214 0.3254 0.3263
PCTGRT-PCTSUPP 0.1282 0.0700 5  0.0560  0.0564  0.0579 0.2129 0.2172 0.2182
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 
   0.84    0.15    0.98