Double/Debiased Machine Learning

Estimate common causal parameters using double/debiased machine learning as proposed by Chernozhukov et al. (2018) . 'ddml' simplifies estimation based on (short-)stacking as discussed in Ahrens et al. (2024) , which leverages multiple base learners to increase robustness to the underlying data generating process.

A Tool for Processing and Analyzing Dendrometer Data

There are various functions for managing and cleaning data before the application of different approaches. This includes identifying and erasing sudden jumps in dendrometer data not related to environmental change, identifying the time gaps of recordings, and changing the temporal resolution of data to different frequencies. Furthermore, the package calculates daily statistics of dendrometer data, including the daily amplitude of tree growth. Various approaches can be applied to separate radial growth from daily cyclic shrinkage and expansion due to uptake and loss of stem water. In addition, it identifies periods of consecutive days with user-defined climatic conditions in daily meteorological data, then check what trees are doing during that period.

Automatic Generation of Exams in R for 'Sakai'

Automatic Generation of Exams in R for 'Sakai'. Question templates in the form of the 'exams' package (see < https://www.r-exams.org/>) are transformed into XML format required by 'Sakai'.

HMM-Based Model for Genotyping and Cross-Over Identification

Our method integrates information from all sequenced samples, thus avoiding loss of alleles due to low coverage. Moreover, it increases the statistical power to uncover sequencing or alignment errors .

Ensemble Conditional Trees for Missing Data Imputation

Single imputation based on the Ensemble Conditional Trees (i.e. Cforest algorithm Strobl, C., Boulesteix, A. L., Zeileis, A., & Hothorn, T. (2007) ).

Dataset for Climate Analysis with Data from the Nordic Region

The Nordklim dataset 1.0 is a unique and useful achievement for climate analysis. It includes observations of twelve different climate elements from more than 100 stations in the Nordic region, in time span over 100 years. The project contractors were NORDKLIM/NORDMET on behalf of the National meteorological services in Denmark (DMI), Finland (FMI), Iceland (VI), Norway (DNMI) and Sweden (SMHI).

Generalized Multivariate Functional Additive Models

Supply implementation to model generalized multivariate functional data using Bayesian additive mixed models of R package 'bamlss' via a latent Gaussian process (see Umlauf, Klein, Zeileis (2018) ).

Lots of Superior Depictions

Create lots of colorful plots in a plethora of variations. Try the LSD demotour().

Binning Variables to Use in Logistic Regression

Fast binning of multiple variables using parallel processing. A summary of all the variables binned is generated which provides the information value, entropy, an indicator of whether the variable follows a monotonic trend or not, etc. It supports rebinning of variables to force a monotonic trend as well as manual binning based on pre specified cuts. The cut points of the bins are based on conditional inference trees as implemented in the partykit package. The conditional inference framework is described by Hothorn T, Hornik K, Zeileis A (2006) .

Support for Compiling Examination Tasks using the 'exams' Package

The main aim is to further facilitate the creation of exercises based on the package 'exams' by Grün, B., and Zeileis, A. (2009) . Creating effective student exercises involves challenges such as creating appropriate data sets and ensuring access to intermediate values for accurate explanation of solutions. The functionality includes the generation of univariate and bivariate data including simple time series, functions for theoretical distributions and their approximation, statistical and mathematical calculations for tasks in basic statistics courses as well as general tasks such as string manipulation, LaTeX/HTML formatting and the editing of XML task files for 'Moodle'.