Generate Isolines and Isobands from Regularly Spaced Elevation Grids

A fast C++ implementation to generate contour lines (isolines) and contour polygons (isobands) from regularly spaced grids containing elevation data.

A Toolbox for Clinical Significance Analyses in Intervention Studies

A clinical significance analysis can be used to determine if an intervention has a meaningful or practical effect for patients. You provide a tidy data set plus a few more metrics and this package will take care of it to make your results publication ready. Accompanying package to Claus et al. .

Improved Text Rendering Support for 'Grid' Graphics

Provides support for rendering of formatted text using 'grid' graphics. Text can be formatted via a minimal subset of 'Markdown', 'HTML', and inline 'CSS' directives, and it can be rendered both with and without word wrap.

'ggplot' Visualizations for the 'partykit' Package

Extends 'ggplot2' functionality to the 'partykit' package. 'ggparty' provides the necessary tools to create clearly structured and highly customizable visualizations for tree-objects of the class 'party'.

Tools for Descriptive Statistics

A collection of miscellaneous basic statistic functions and convenience wrappers for efficiently describing data. The author's intention was to create a toolbox, which facilitates the (notoriously time consuming) first descriptive tasks in data analysis, consisting of calculating descriptive statistics, drawing graphical summaries and reporting the results. The package contains furthermore functions to produce documents using MS Word (or PowerPoint) and functions to import data from Excel. Many of the included functions can be found scattered in other packages and other sources written partly by Titans of R. The reason for collecting them here, was primarily to have them consolidated in ONE instead of dozens of packages (which themselves might depend on other packages which are not needed at all), and to provide a common and consistent interface as far as function and arguments naming, NA handling, recycling rules etc. are concerned. Google style guides were used as naming rules (in absence of convincing alternatives). The 'BigCamelCase' style was consequently applied to functions borrowed from contributed R packages as well.

Joyplots in 'ggplot2'

Joyplots provide a convenient way of visualizing changes in distributions over time or space. This package enables the creation of such plots in 'ggplot2'.

Solve Generalized Estimating Equations for Clustered Data

Estimation of generalized linear models with correlated/clustered observations by use of generalized estimating equations (GEE). See e.g. Halekoh and Højsgaard, (2005, ), for details. Several types of clustering are supported, including exchangeable variance structures, AR1 structures, M-dependent, user-specified variance structures and more. The model fitting computations are performed using modified code from the 'geeM' package, while the interface and output objects have been written to resemble the 'geepack' package. The package also contains additional tools for working with and inspecting results from the 'geepack' package, e.g. a 'confint' method for 'geeglm' objects from 'geepack'.

Prediction and Interpretation in Decision Trees for Classification and Regression

Optimization of conditional inference trees from the package 'party' for classification and regression. For optimization, the model space is searched for the best tree on the full sample by means of repeated subsampling. Restrictions are allowed so that only trees are accepted which do not include pre-specified uninterpretable split results (cf. Weihs & Buschfeld, 2021a). The function PrInDT() represents the basic resampling loop for 2-class classification (cf. Weihs & Buschfeld, 2021a). The function RePrInDT() (repeated PrInDT()) allows for repeated applications of PrInDT() for different percentages of the observations of the large and the small classes (cf. Weihs & Buschfeld, 2021c). The function NesPrInDT() (nested PrInDT()) allows for an extra layer of subsampling for a specific factor variable (cf. Weihs & Buschfeld, 2021b). The functions PrInDTMulev() and PrInDTMulab() deal with multilevel and multilabel classification. In addition to these PrInDT() variants for classification, the function PrInDTreg() has been developed for regression problems. Finally, the function PostPrInDT() allows for a posterior analysis of the distribution of a specified variable in the terminal nodes of a given tree. References are: -- Weihs, C., Buschfeld, S. (2021a) "Combining Prediction and Interpretation in Decision Trees (PrInDT) - a Linguistic Example" ; -- Weihs, C., Buschfeld, S. (2021b) "NesPrInDT: Nested undersampling in PrInDT" ; -- Weihs, C., Buschfeld, S. (2021c) "Repeated undersampling in PrInDT (RePrInDT): Variation in undersampling and prediction, and ranking of predictors in ensembles" .

Analysis of Single-Cell Viral Growth Curves

Aims to quantify time intensity data by using sigmoidal and double sigmoidal curves. It fits straight lines, sigmoidal, and double sigmoidal curves on to time vs intensity data. Then all the fits are used to make decision on which model best describes the data. This method was first developed in the context of single-cell viral growth analysis (for details, see Caglar et al. (2018) ), and the package name stands for "SIngle CEll Growth Analysis in R".

Presentation Ninja

Create HTML5 slides with R Markdown and the JavaScript library 'remark.js' (< https://remarkjs.com>).