Building Regression and Classification Models

Consistent user interface to the most common regression and classification algorithms, such as random forest, neural networks, C5 trees and support vector machines, complemented with a handful of auxiliary functions, such as variable importance and a tuning function for the parameters.

Exact Variable-Subset Selection in Linear Regression

Exact and approximation algorithms for variable-subset selection in ordinary linear regression models. Either compute all submodels with the lowest residual sum of squares, or determine the single-best submodel according to a pre-determined statistical criterion. Hofmann et al. (2020) .

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 .

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).

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) ).

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().