Model Wrappers for Poisson Regression

Bindings for Poisson regression models for use with the 'parsnip' package. Models include simple generalized linear models, Bayesian models, and zero-inflated Poisson models (Zeileis, Kleiber, and Jackman (2008) ).

Classification and Regression with Structured and Mixed-Type Data

Implementation of Energy Trees, a statistical model to perform classification and regression with structured and mixed-type data. The model has a similar structure to Conditional Trees, but brings in Energy Statistics to test independence between variables that are possibly structured and of different nature. Currently, the package covers functions and graphs as structured covariates. It builds upon 'partykit' to provide functionalities for fitting, printing, plotting, and predicting with Energy Trees. Energy Trees are described in Giubilei et al. (2022) .

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.

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

Tools for importing, cleaning, analyzing, and visualizing high-resolution dendrometer data and for linking them with climate data. Dendrometer and climate records can be imported with automatic date-time parsing (read.dendrometer(), read.climate()) and checked for a regular temporal resolution (reso_dm()). Preprocessing functions detect and correct artificial jumps with a threshold-based or an automatic changepoint method (jump.locator()), detect and fill gaps with spline, seasonal, or network interpolation (dm.na.interpolation(), network.interpolation()), and truncate or resample the series (dendro.truncate(), dendro.resample()). Daily statistics (daily.data()), the stem-cycle approach (phase.sc()), and the zero-growth approach (phase.zg()) separate radial growth from reversible stem shrinkage and swelling. The function phase.zg() also returns metrics of tree water deficit (TWD) phases, including the event-based ABr index, and the daily drought indices of Peters et al. (2025) . Climate data can be summarized at daily and sub-daily scales and attached to daily, phase-level, and point-level outputs (dm_add_climate()). Event-based climate analyses, superposed epoch analyses, and adverse-period analyses (dm_event_climate(), dm_epoch_test(), clim.twd()) relate tree responses to climate conditions. Seasonal growth can be fitted with Gompertz, logistic, Richards, generalized additive model, LOESS, and spline functions, detrended, and compared among methods (dm.growth.fit(), dm.detrend.fit(), dm.growth.evaluate()). Running correlations with climate (mov.cor.dm()) and wavelet power and coherence analyses based on 'WaveletComp' (dm_wavelet(), dm_wavelet_coherence()) are also provided. Most outputs have dedicated plot methods, and an optional 'shiny' application (dendroanalyst()) allows the complete workflow to be run without programming. The zero-growth approach follows Zweifel et al. (2016) , and the first version of the package is described in Aryal et al. (2020) .

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) ).