Community Ecology Package

Ordination methods, diversity analysis and other functions for community and vegetation ecologists.

Signal Processing

R implementation of the 'Octave' package 'signal', containing a variety of signal processing tools, such as signal generation and measurement, correlation and convolution, filtering, filter design, filter analysis and conversion, power spectrum analysis, system identification, decimation and sample rate change, and windowing.

Fast Cross-Validation for Multi-Penalty Ridge Regression

Multi-penalty linear, logistic and cox ridge regression, including estimation of the penalty parameters by efficient (repeated) cross-validation and marginal likelihood maximization. Multiple high-dimensional data types that require penalization are allowed, as well as unpenalized variables. Paired and preferential data types can be specified. See Van de Wiel et al. (2021), .

Analyzing Dendrometer Data

Various functions to import, verify, process and plot high-resolution dendrometer data using daily and stem-cycle approaches as described in Deslauriers et al, 2007 . For more details about the package please see: Van der Maaten et al. 2016 .

Various Functions to Facilitate Visualization of Data and Analysis

When analyzing data, plots are a helpful tool for visualizing data and interpreting statistical models. This package provides a set of simple tools for building plots incrementally, starting with an empty plot region, and adding bars, data points, regression lines, error bars, gradient legends, density distributions in the margins, and even pictures. The package builds further on R graphics by simply combining functions and settings in order to reduce the amount of code to produce for the user. As a result, the package does not use formula input or special syntax, but can be used in combination with default R plot functions. Note: Most of the functions were part of the package 'itsadug', which is now split in two packages: 1. the package 'itsadug', which contains the core functions for visualizing and evaluating nonlinear regression models, and 2. the package 'plotfunctions', which contains more general plot functions.

Data Table Back-End for 'dplyr'

Provides a data.table backend for 'dplyr'. The goal of 'dtplyr' is to allow you to write 'dplyr' code that is automatically translated to the equivalent, but usually much faster, data.table code.

"Hit and Run" and "Shake and Bake" for Sampling Uniformly from Convex Shapes

The "Hit and Run" Markov Chain Monte Carlo method for sampling uniformly from convex shapes defined by linear constraints, and the "Shake and Bake" method for sampling from the boundary of such shapes. Includes specialized functions for sampling normalized weights with arbitrary linear constraints. Tervonen, T., van Valkenhoef, G., Basturk, N., and Postmus, D. (2012) . van Valkenhoef, G., Tervonen, T., and Postmus, D. (2014) .

Classification and Regression Tests

Provides wrapper functions for running classification and regression tests using different machine learning techniques, such as Random Forests and decision trees. The package provides standardized methods for preparing data to suit the algorithm's needs, training a model, making predictions, and evaluating results. Also, some functions are provided to run multiple instances of a test.

Tidy Interface to 'data.table'

A tidy interface to 'data.table', giving users the speed of 'data.table' while using tidyverse-like syntax.

Minimization Methods for Ill-Conditioned Problems

Implementation of methods for minimizing ill-conditioned problems. Currently only includes regularized (quasi-)newton optimization (Kanzow and Steck et al. (2023), ).