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Tools for Building Regression and Classification Models
Collection of tools for regression and classification tasks. The package implements a consistent user interface to the most popular regression and classification algorithms, such as random forest, neural networks, C5 trees and support vector machines, and complements it 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)
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)
Double/Debiased Machine Learning
Estimate common causal parameters using double/debiased machine
learning as proposed by Chernozhukov et al. (2018)
Model Wrappers for Tree-Based Models
Bindings for additional tree-based model engines for use with
the 'parsnip' package. Models include gradient boosted decision trees
with 'LightGBM' (Ke et al, 2017.) and
conditional inference trees and conditional random forests with
'partykit' (Hothorn and Zeileis, 2015. and
Hothorn et al, 2006.
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 < http://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).