Update paper.md

#227

Author: mattansb

paper/paper.md

@@ -35,14 +35,15 @@ affiliations:
 
 # Aims of the Package
 
-In both theoretical and applied research, it is often of interest to assess the strength of an observed association. This is typically done to allow the judgment of the magnitude of an effect (especially when units of measurement are not meaningful), to facilitate comparing between predictors' importance within a given model, or both. Though some indices of effect size, such as the correlation coefficient (itself a standardized covariance coefficient) are readily available, other measures are often harder to obtain. **effectsize** is an R package [@rcore] that fills this important gap, providing utilities for easily estimating a wide variety of standardized effect sizes (i.e., effect sizes that are not tied to the units of measurement of the variables of interest) and their confidence intervals (CIs), from a variety of statistical models. **effectsize** provides easy-to-use functions, with full documentation and explanation of the various effect sizes offered, and is also used by developers of other R packages as the back-end for effect size computation, such as **parameters** [@ludecke2020extracting], **ggstatsplot** [@patil2020ggstatsplot], **gtsummary** [@sjoberg2020gtsummary] and more.
+In both theoretical and applied research, it is often of interest to assess the strength of an observed association. This is typically done to allow the judgment of the magnitude of an effect (especially when units of measurement are not meaningful, e.g. in the use of estimated latent variable), to facilitate comparing between predictors' importance within a given model, or both. Though some indices of effect size, such as the correlation coefficient (itself a standardized covariance coefficient) are readily available, other measures are often harder to obtain. **effectsize** is an R package [@rcore] that fills this important gap, providing utilities for easily estimating a wide variety of standardized effect sizes (i.e., effect sizes that are not tied to the units of measurement of the variables of interest) and their confidence intervals (CIs), from a variety of statistical models. **effectsize** provides easy-to-use functions, with full documentation and explanation of the various effect sizes offered, and is also used by developers of other R packages as the back-end for effect size computation, such as **parameters** [@ludecke2020extracting], **ggstatsplot** [@patil2020ggstatsplot], **gtsummary** [@sjoberg2020gtsummary] and more.
 
 # Comparison to Other Packages
 
 **effectsize**'s functionality is in part comparable to packages like **lm.beta** [@behrendt2014lmbeta], **MOTE** [@buchanan2019MOTE] or **MBESS** [@kelley2020MBESS]. Yet, there are some notable differences, e.g.:
 
 - **lm.beta** provides standardized regression coefficients for linear models, based on post-hoc model matrix standardization. However, the functionality is available only for a limited number of models (models inheriting from the `lm` class), whereas **effectsize** provides support for many types of models, including (generalized) linear mixed models, Bayesian models, and more. Additionally, in additional to post-hoc model matrix standardization, **effectsize** offers other methods of standardization (see below).  
-- Both **MOTE** and **MBESS** provide functions for computing effect sizes such as Cohen's *d* and effect sizes for ANOVAs, and their confidence intervals. However, both require manual input of *F*- or *t*-statistics, *degrees of freedom*, and *Sums of Squares* for the computation the effect sizes, whereas **effectsize** can automatically extract this information from the provided models, thus allowing for better ease-of-use as well as reducing any potential for error.
+- Both **MOTE** and **MBESS** provide functions for computing effect sizes such as Cohen's *d* and effect sizes for ANOVAs, and their confidence intervals. However, both require manual input of *F*- or *t*-statistics, *degrees of freedom*, and *Sums of Squares* for the computation the effect sizes, whereas **effectsize** can automatically extract this information from the provided models, thus allowing for better ease-of-use as well as reducing any potential for error.  
+- Finally, in **base R**, the function `scale()` can be used to standardize vectors, matrices and data frame, which can be used to standardize data prior to model fitting. The coefficients of a linear model fit on such data are in effect standardized regression coefficients. **effectsize** expands an this, allowing for robust standardization (using the median and the MAD, instead of the mean and SD), post-hoc parameter standardization, and more.
 
 # Examples of Features
 
@@ -59,6 +60,8 @@ cohens_d(mpg ~ am, data = mtcars)
 #> Cohen's d |         95% CI
 #> --------------------------
 #>     -1.48 | [-2.27, -0.67]
+#> - Estimate using pooled SD
+
 ```
 
 ### Contingency Tables
@@ -109,7 +112,7 @@ Standardized parameters can also be produced for generalized linear models (GLMs
 ``` r
 model <- glm(am ~ cyl + hp,
              family = "binomial",
-             data = mtcars, )
+             data = mtcars)
 
 standardize_parameters(model, exponentiate = TRUE)
 #> Parameter   | Odds Ratio (std.) |        95% CI