This R package, which has no dependencies, contains a variety of functions for a variety of applications. Download it by running the line of code devtools::install_github("davidblakneymoore/DBM.functions"). For a much more detailed description of some of these functions, please read the vignette.
The function Aligning_Values_Across_Multiple_Vertical_Axes generates axis limits for aligning values across multiple vertical axes on plots. Here and here are example figures that used this function to align values across multiple vertical axes.
The function Arranging_Plots_Nicely generates a plot layout matrix that is as square as possible - in other words, it generates a plot layout matrix whose number of rows and number of columns differ by either 0 (if possible) or 1 (as a last resort). Here is an example figure that was made using this function.
The function Comparing_Multiple_Independent_Correlation_Coefficients compares multiple correlation coefficients from independent correlations (Levy, 1977). This function generates p values for pairwise correlation coefficient comparisons as well as means separation lettering. It has been cited in several publications (Beghin, 2023; Chue and Yeo, 2022; Findor et al., 2021; Matko and Sedlmeier, 2023).
The function Finding_the_Optimal_Sigmoid_Function_Model determines which sigmoid function best fits a binary response data set (a data set where the response variable contains only 1s and 0s) from ten different, fully differentiable sigmoid functions. Here is a plot that shows what this function can do.
The function Optimally_Assigning_Experimental_Units_to_Treatment_Groups_With_a_Blocking_Variable assigns experimental units to treatment groups (for cases when there is a blocking variable) in a way that ensures that treatment groups are as balanced as possible for a particular set of experimental units' variables. This function works by calculating means and possibly other higher-order mathematical moments (such as variances, skewnesses, and kurtoses) for a particular set of experimental units' variables that have already been measured and, out of every single possible grouping arrangement, choosing the one that holds these moments as similar as possible across treatment groups.
The function Optimally_Assigning_Experimental_Units_to_Treatment_Groups_Without_a_Blocking_Variable assigns experimental units to treatment groups (for cases when there is no blocking variable) in a way that ensures that treatment groups are as balanced as possible for a particular set of experimental units' variables. This function works by calculating means and possibly other higher-order mathematical moments (such as variances, skewnesses, and kurtoses) for a particular set of experimental units' variables that have already been measured and, out of every single possible grouping arrangement, choosing the one that holds these moments as similar as possible across treatment groups.
The data frame Sugar_Maple_Data may be used with the Aligning_Values_Across_Multiple_Vertical_Axes function - the Sap_Flow and Wood_Temperature columns in this data frame can be aligned across primary and secondary vertical axes at the values of 0 as shown here.
Beghin, G. 2023. Does the Lay Concept of Mental Disorder Necessitate a Dysfunction? Advances in Experimental Philosophy of Medicine, edited by Kristien Hens and Andreas De Block. Bloomsbury Publishing. Pp. 71-96.
Chue, K.L., and A. Yeo. 2022. Exploring associations of positive relationships and adolescent well-being across cultures. Youth Soc. 00:1-12.
Findor, A., M. Hruska, P. Jankovská, and M. Pobudová. 2021. Re-examining public opinion preferences for migrant categorizations: “Refugees” are evaluated more negatively than “migrants” and “foreigners” related to participants’ direct, extended, and mass-mediated intergroup contact experiences. Int. J. Intercult. Relat. 80:262-273.
Levy, K.J. 1977. Pairwise comparisons involving unequal sample sizes associated with correlations, proportions or variances. Br. J. Math. Stat. Psychol. 30:137-139.
Matko, K., and P. Sedlmeier. 2023. Which meditation technique for whom? An experimental single-case study comparing concentrative, humming, observing-thoughts, and walking meditation.
Moore, D.B. 2024. DBM.functions: A Variety of Functions for a Variety of Applications. R package version 0.0.0.9000. https://github.com/davidblakneymoore/DBM.functions.