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diff --git a/.setup/build/betaDelta.pdf b/.setup/build/betaDelta.pdf
diff --git a/.setup/build/betaDelta_1.0.4.9000.tar.gz b/.setup/build/betaDelta_1.0.4.9000.tar.gz
diff --git a/vignettes/example-beta-delta.Rmd b/vignettes/example-beta-delta.Rmd
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+---
+title: "betaDelta: Example Using the BetaDelta Function"
+author: "Ivan Jacob Agaloos Pesigan"
+output: rmarkdown::html_vignette
+bibliography: "vignettes.bib"
+csl: https://raw.githubusercontent.com/citation-style-language/styles/master/apa.csl
+nocite: |
+  @Pesigan-Sun-Cheung-2023
+  @Yuan-Chan-2011
+  @Jones-Waller-2015
+  @NationalResearchCouncil-1982
+vignette: >
+  %\VignetteIndexEntry{betaDelta: Example Using the BetaDelta Function}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+
+
+In this example, a multiple regression model is fitted
+using program quality ratings (`QUALITY`) as the regressand/outcome variable
+and number of published articles attributed to the program faculty members (`NARTIC`),
+percent of faculty members holding research grants (`PCTGRT`), and
+percentage of program graduates who received support (`PCTSUPP`) as regressor/predictor variables
+using a data set from 1982 ratings of 46 doctoral programs in psychology in the USA [@NationalResearchCouncil-1982].
+Confidence intervals for the standardized regression coefficients are generated
+using the `BetaDelta()` function from the `betaDelta` package following @Yuan-Chan-2011 and @Jones-Waller-2015.
+
+
+```r
+library(betaDelta)
+```
+
+
+
+
+```r
+df <- betaDelta::nas1982
+```
+
+## Fit the regression model using the `lm()` function.
+
+
+```r
+object <- lm(QUALITY ~ NARTIC + PCTGRT + PCTSUPP, data = df)
+```
+
+## Estimate the standardized regression slopes and the corresponding sampling covariance matrix.
+
+#### Multivariate Normal-Theory Approach
+
+
+```r
+BetaDelta(object, type = "mvn", alpha = 0.05)
+#> Call:
+#> BetaDelta(object = object, type = "mvn", alpha = 0.05)
+#> 
+#> Standardized regression slopes with MVN standard errors:
+#>            est     se      t df     p   2.5%  97.5%
+#> NARTIC  0.4951 0.0759 6.5272 42 0.000 0.3421 0.6482
+#> PCTGRT  0.3915 0.0770 5.0824 42 0.000 0.2360 0.5469
+#> PCTSUPP 0.2632 0.0747 3.5224 42 0.001 0.1124 0.4141
+```
+
+#### Asymptotic Distribution-Free Approach
+
+
+```r
+BetaDelta(object, type = "adf", alpha = 0.05)
+#> Call:
+#> BetaDelta(object = object, type = "adf", alpha = 0.05)
+#> 
+#> Standardized regression slopes with ADF standard errors:
+#>            est     se      t df      p   2.5%  97.5%
+#> NARTIC  0.4951 0.0674 7.3490 42 0.0000 0.3592 0.6311
+#> PCTGRT  0.3915 0.0710 5.5164 42 0.0000 0.2483 0.5347
+#> PCTSUPP 0.2632 0.0769 3.4231 42 0.0014 0.1081 0.4184
+```
+
+## Methods
+
+
+```r
+mvn <- BetaDelta(object, type = "mvn")
+adf <- BetaDelta(object, type = "adf")
+```
+
+### summary
+
+Summary of the results of `BetaDelta()`.
+
+
+```r
+summary(mvn)
+#> Call:
+#> BetaDelta(object = object, type = "mvn")
+#> 
+#> Standardized regression slopes with MVN standard errors:
+#>            est     se      t df     p   0.05%   0.5%   2.5%  97.5%  99.5%
+#> NARTIC  0.4951 0.0759 6.5272 42 0.000  0.2268 0.2905 0.3421 0.6482 0.6998
+#> PCTGRT  0.3915 0.0770 5.0824 42 0.000  0.1190 0.1837 0.2360 0.5469 0.5993
+#> PCTSUPP 0.2632 0.0747 3.5224 42 0.001 -0.0011 0.0616 0.1124 0.4141 0.4649
+#>         99.95%
+#> NARTIC  0.7635
+#> PCTGRT  0.6640
+#> PCTSUPP 0.5276
+summary(adf)
+#> Call:
+#> BetaDelta(object = object, type = "adf")
+#> 
+#> Standardized regression slopes with ADF standard errors:
+#>            est     se      t df      p   0.05%   0.5%   2.5%  97.5%  99.5%
+#> NARTIC  0.4951 0.0674 7.3490 42 0.0000  0.2568 0.3134 0.3592 0.6311 0.6769
+#> PCTGRT  0.3915 0.0710 5.5164 42 0.0000  0.1404 0.2000 0.2483 0.5347 0.5830
+#> PCTSUPP 0.2632 0.0769 3.4231 42 0.0014 -0.0088 0.0558 0.1081 0.4184 0.4707
+#>         99.95%
+#> NARTIC  0.7335
+#> PCTGRT  0.6426
+#> PCTSUPP 0.5353
+```
+
+### coef
+
+Calculate the standardized regression slopes.
+
+
+```r
+coef(mvn)
+#>    NARTIC    PCTGRT   PCTSUPP 
+#> 0.4951451 0.3914887 0.2632477
+coef(adf)
+#>    NARTIC    PCTGRT   PCTSUPP 
+#> 0.4951451 0.3914887 0.2632477
+```
+
+### vcov
+
+Calculate the sampling covariance matrix of the standardized regression slopes.
+
+
+```r
+vcov(mvn)
+#>               NARTIC       PCTGRT      PCTSUPP
+#> NARTIC   0.005754524 -0.003360334 -0.002166127
+#> PCTGRT  -0.003360334  0.005933462 -0.001769723
+#> PCTSUPP -0.002166127 -0.001769723  0.005585256
+vcov(adf)
+#>               NARTIC       PCTGRT      PCTSUPP
+#> NARTIC   0.004539472 -0.002552698 -0.001742698
+#> PCTGRT  -0.002552698  0.005036538 -0.001906216
+#> PCTSUPP -0.001742698 -0.001906216  0.005914088
+```
+
+### confint
+
+Generate confidence intervals for standardized regression slopes.
+
+
+```r
+confint(mvn, level = 0.95)
+#>             2.5 %    97.5 %
+#> NARTIC  0.3420563 0.6482339
+#> PCTGRT  0.2360380 0.5469395
+#> PCTSUPP 0.1124272 0.4140682
+confint(adf, level = 0.95)
+#>             2.5 %    97.5 %
+#> NARTIC  0.3591757 0.6311146
+#> PCTGRT  0.2482683 0.5347091
+#> PCTSUPP 0.1080509 0.4184444
+```
+
+## References
diff --git a/vignettes/example-delta-generic.Rmd b/vignettes/example-delta-generic.Rmd
@@ -0,0 +1,104 @@
+---
+title: "betaDelta: Example Using the DeltaGeneric Function"
+author: "Ivan Jacob Agaloos Pesigan"
+output: rmarkdown::html_vignette
+bibliography: "vignettes.bib"
+csl: https://raw.githubusercontent.com/citation-style-language/styles/master/apa.csl
+nocite: |
+  @Pesigan-Sun-Cheung-2023
+vignette: >
+  %\VignetteIndexEntry{betaDelta: Example Using the DeltaGeneric Function}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+
+
+In this example, we use the delta method to calculate the odds ratio, the associated standard errors, and confidence intervals within a logistic regression model.
+
+
+```r
+library(betaDelta)
+```
+
+
+```r
+object <- glm(
+  formula = vs ~ wt + disp,
+  family = "binomial",
+  data = mtcars
+)
+def <- list("exp(wt)", "exp(disp)")
+DeltaGeneric(
+  object = object,
+  def = def,
+  alpha = 0.05
+)
+#> Call:
+#> DeltaGeneric(object = object, def = def, alpha = 0.05)
+#>              est     se       z      p    2.5%   97.5%
+#> exp(wt)   5.0853 7.5805  0.6708 0.5023 -9.7723 19.9429
+#> exp(disp) 0.9662 0.0148 65.0838 0.0000  0.9371  0.9952
+```
+
+## Methods
+
+
+```r
+delta <- DeltaGeneric(
+  object = object,
+  def = def,
+  alpha = 0.05
+)
+```
+
+### summary
+
+Summary of the results of `DeltaGeneric()`.
+
+
+```r
+summary(delta)
+#> Call:
+#> DeltaGeneric(object = object, def = def, alpha = 0.05)
+#>              est     se       z      p    2.5%   97.5%
+#> exp(wt)   5.0853 7.5805  0.6708 0.5023 -9.7723 19.9429
+#> exp(disp) 0.9662 0.0148 65.0838 0.0000  0.9371  0.9952
+```
+
+### coef
+
+Calculate the estimates.
+
+
+```r
+coef(delta)
+#>   exp(wt) exp(disp) 
+#> 5.0852960 0.9661524
+```
+
+### vcov
+
+Calculate the sampling covariance matrix.
+
+
+```r
+vcov(delta)
+#>               exp(wt)     exp(disp)
+#> exp(wt)   57.46443026 -0.0977480169
+#> exp(disp) -0.09774802  0.0002203662
+```
+
+### confint
+
+Generate confidence intervals.
+
+
+```r
+confint(delta, level = 0.95)
+#>                2.5 %     97.5 %
+#> exp(wt)   -9.7722691 19.9428612
+#> exp(disp)  0.9370572  0.9952475
+```
+
+## References
diff --git a/vignettes/example-delta.Rmd b/vignettes/example-delta.Rmd
@@ -0,0 +1,112 @@
+---
+title: "betaDelta: Example Using the Delta Function"
+author: "Ivan Jacob Agaloos Pesigan"
+output: rmarkdown::html_vignette
+bibliography: "vignettes.bib"
+csl: https://raw.githubusercontent.com/citation-style-language/styles/master/apa.csl
+nocite: |
+  @Pesigan-Sun-Cheung-2023
+vignette: >
+  %\VignetteIndexEntry{betaDelta: Example Using the Delta Function}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+
+
+In this example, we use the delta method to calculate the odds ratio, the associated standard errors, and confidence intervals within a logistic regression model.
+
+
+```r
+library(betaDelta)
+```
+
+
+```r
+object <- glm(
+  formula = vs ~ wt + disp,
+  family = "binomial",
+  data = mtcars
+)
+func <- function(x) {
+  y <- exp(x)
+  names(y) <- paste0("exp", "(", names(x), ")")
+  return(y[-1])
+}
+Delta(
+  coef = coef(object),
+  vcov = vcov(object),
+  func = func,
+  alpha = 0.05
+)
+#> Call:
+#> Delta(coef = coef(object), vcov = vcov(object), func = func, 
+#>     alpha = 0.05)
+#>              est     se       z      p    2.5%   97.5%
+#> exp(wt)   5.0853 7.5805  0.6708 0.5023 -9.7723 19.9429
+#> exp(disp) 0.9662 0.0148 65.0838 0.0000  0.9371  0.9952
+```
+
+## Methods
+
+
+```r
+delta <- Delta(
+  coef = coef(object),
+  vcov = vcov(object),
+  func = func,
+  alpha = 0.05
+)
+```
+
+### summary
+
+Summary of the results of `Delta()`.
+
+
+```r
+summary(delta)
+#> Call:
+#> Delta(coef = coef(object), vcov = vcov(object), func = func, 
+#>     alpha = 0.05)
+#>              est     se       z      p    2.5%   97.5%
+#> exp(wt)   5.0853 7.5805  0.6708 0.5023 -9.7723 19.9429
+#> exp(disp) 0.9662 0.0148 65.0838 0.0000  0.9371  0.9952
+```
+
+### coef
+
+Calculate the estimates.
+
+
+```r
+coef(delta)
+#>   exp(wt) exp(disp) 
+#> 5.0852960 0.9661524
+```
+
+### vcov
+
+Calculate the sampling covariance matrix.
+
+
+```r
+vcov(delta)
+#>               exp(wt)     exp(disp)
+#> exp(wt)   57.46443026 -0.0977480169
+#> exp(disp) -0.09774802  0.0002203662
+```
+
+### confint
+
+Generate confidence intervals.
+
+
+```r
+confint(delta, level = 0.95)
+#>                2.5 %     97.5 %
+#> exp(wt)   -9.7722691 19.9428612
+#> exp(disp)  0.9370572  0.9952475
+```
+
+## References