# Daniel Lüdecke

#### 8 packages on CRAN

Collection of functions dealing with labelled data, like reading and writing data between R and other statistical software packages like 'SPSS', 'SAS' or 'Stata', and working with labelled data. This includes easy ways to get, set or change value and variable label attributes, to convert labelled vectors into factors or numeric (and vice versa), or to deal with multiple declared missing values.

Collection of miscellaneous utility functions, supporting data transformation tasks like recoding, dichotomizing or grouping variables, setting and replacing missing values. The data transformation functions also support labelled data, and all integrate seamlessly into a 'tidyverse'-workflow.

Collection of convenient functions for common statistical computations, which are not directly provided by R's base or stats packages. This package aims at providing, first, shortcuts for statistical measures, which otherwise could only be calculated with additional effort (like standard errors or root mean squared errors). Second, these shortcut functions are generic (if appropriate), and can be applied not only to vectors, but also to other objects as well (e.g., the Coefficient of Variation can be computed for vectors, linear models, or linear mixed models; the r2()-function returns the r-squared value for 'lm', 'glm', 'merMod' and other model objects). The focus of most functions lies on summary statistics or fit measures for regression models, including generalized linear models, mixed effects models and Bayesian models. However, some of the functions also deal with other statistical measures, like Cronbach's Alpha, Cramer's V, Phi etc.

Collection of plotting and table output functions for data visualization. Results of various statistical analyses (that are commonly used in social sciences) can be visualized using this package, including simple and cross tabulated frequencies, histograms, box plots, (generalized) linear models, mixed effects models, principal component analysis and correlation matrices, cluster analyses, scatter plots, stacked scales, effects plots of regression models (including interaction terms) and much more. This package supports labelled data.

Compute marginal effects at the mean or average marginal effects from statistical models and returns the result as tidy data frames. These data frames are ready to use with the 'ggplot2'-package. Marginal effects can be calculated for many different models. Interaction terms, splines and polynomial terms are also supported. The two main functions are ggpredict() and ggaverage(), however, there are some convenient wrapper-functions especially for polynomials or interactions. There is a generic plot()-method to plot the results using 'ggplot2'.

Implementation of the web-based 'Practical Meta-Analysis Effect Size Calculator' from David B. Wilson (<http://www.campbellcollaboration.org/escalc/html/EffectSizeCalculator-Home.php>) in R. Based on the input, the effect size can be returned as standardized mean difference, Cohen's f, Hedges' g, Pearson's r or Fisher's transformation z, odds ratio or log odds, or eta squared effect size.

Contains functions for the easy display of statistical tests as well as some convenience functions for data cleanup. It is meant to ease existing workflows with packages like 'sjPlot', 'dplyr', and 'ggplot2'. The primary components are the functions prefixed with 'tadaa_', which are built to work in an interactive environment, but also print tidy markdown tables powered by 'pixiedust' for the creation of 'RMarkdown' reports.