* Methods for Measuring Functional Diversity Based on Trait Probability Density* (

**TPD**)

Tools to calculate trait probability density functions (TPD) at any scale (e.g. populations, species, communities). TPD functions are used to compute several indices of functional diversity, as well as its partition across scales. These indices constitute a unified framework that incorporates the underlying probabilistic nature of trait distributions into uni- or multidimensional functional trait-based studies. See Carmona et al. (2016) <doi:10.1016/j.tree.2016.02.003> for further information.

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**Estimating and Testing Direct Effects in Directed Acyclic Graphs using Estimating Equations****CIEE**)

In many studies across different disciplines, detailed measures of the variables of interest are available. If assumptions can be made regarding the direction of effects between the assessed variables, this has to be considered in the analysis. The functions in this package implement the novel approach CIEE (causal inference using estimating equations; Konigorski et al., 2017, Genetic Epidemiology, in press) for estimating and testing the direct effect of an exposure variable on a primary outcome, while adjusting for indirect effects of the exposure on the primary outcome through a secondary intermediate outcome and potential factors influencing the secondary outcome. The underlying directed acyclic graph (DAG) of this considered model is described in the vignette. CIEE can be applied to studies in many different fields, and it is implemented here for the analysis of a continuous primary outcome and a time-to-event primary outcome subject to censoring. CIEE uses estimating equations to obtain estimates of the direct effect and robust sandwich standard error estimates. Then, a large-sample Wald-type test statistic is computed for testing the absence of the direct effect. Additionally, standard multiple regression, regression of residuals, and the structural equation modeling approach are implemented for comparison.

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**Accumulated Local Effects (ALE) Plots and Partial Dependence (PD) Plots****ALEPlot**)

Visualizes the main effects of individual predictor variables and their second-order interaction effects in black-box supervised learning models. The package creates either Accumulated Local Effects (ALE) plots and/or Partial Dependence (PD) plots, given a fitted supervised learning model.

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**RStudio’ Addin for Teaching and Learning ‘ggplot2’****ggplotAssist**)

An ‘RStudio’ addin for teaching and learning making plot using the ‘ggplot2’ package. You can learn each steps of making plot by clicking your mouse without coding. You can get resultant code for the plot.

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**Observational Database Study Planning using Exact Sequential Analysis for Poisson and Binomial Data****SequentialDesign**)

Functions to be used in conjunction with the ‘Sequential’ package that allows for planning of observational database studies that will be analyzed with exact sequential analysis. This package supports Poisson- and binomial-based data. The primary function, seq_wrapper(…), accepts parameters for simulation of a simple exposure pattern and for the ‘Sequential’ package setup and analysis functions. The exposure matrix is used to simulate the true and false positive and negative populations (Green (1983) <doi:10.1093/oxfordjournals.aje.a113521>, Brenner (1993) <doi:10.1093/oxfordjournals.aje.a116805>). Functions are then run from the ‘Sequential’ package on these populations, which allows for the exploration of outcome misclassification in data.

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**Helpers for Developing Command Line Interfaces****cli**)

A suite of tools designed to build attractive command line interfaces (‘CLIs’). Includes tools for drawing rules, boxes, trees, and ‘Unicode’ symbols with ‘ASCII’ alternatives.