Client Interface for 'openEO' Servers

Access data and processing functionalities of 'openEO' compliant back-ends in R.

TraMineR Extension

Collection of ancillary functions and utilities to be used in conjunction with the 'TraMineR' package for sequence data exploration. Includes, among others, specific functions such as state survival plots, position-wise group-typical states, dynamic sequence indicators, and dissimilarities between event sequences. Also includes contributions by non-members of the TraMineR team such as methods for polyadic data and for the comparison of groups of sequences.

Estimating the Parameters of a Continuous-Time Markov Chain from Discrete-Time Data

Estimation of Markov generator matrices from discrete-time observations. The implemented approaches comprise diagonal and weighted adjustment of matrix logarithm based candidate solutions as in Israel (2001) as well as a quasi-optimization approach. Moreover, the expectation-maximization algorithm and the Gibbs sampling approach of Bladt and Sorensen (2005) are included.

Predict Regional Community Composition

Predict regional community composition at a fine spatial resolution using only sparse biological and environmental data. The package is based on the DynamicFOAM algorithm described in Mokany et al. (2011) .

Robust Time Series Filters

Implementations for several robust procedures that allow for (online) extraction of the signal of univariate or multivariate time series by applying robust regression techniques to a moving time window are provided. Included are univariate filtering procedures based on repeated-median regression as well as hybrid and trimmed filters derived from it; see Schettlinger et al. (2006) . The adaptive online repeated median by Schettlinger et al. (2010) and the slope comparing adaptive repeated median by Borowski and Fried (2013) choose the width of the moving time window adaptively. Multivariate versions are also provided; see Borowski et al. (2009) for a multivariate online adaptive repeated median and Borowski (2012) for a multivariate slope comparing adaptive repeated median. Furthermore, a repeated-median based filter with automatic outlier replacement and shift detection is provided; see Fried (2004) .

An Interface Between the 'FSKX' Standard and 'R'

Functions for importing, creating, editing and exporting 'FSK' files < https://foodrisklabs.bfr.bund.de/fskx-food-safety-knowledge-exchange-format/> using the 'R' programming environment. Furthermore, it enables users to run simulations contained in the 'FSK' files and visualize the results.

Tropical Fisheries Analysis

A compilation of fish stock assessment methods for the analysis of length-frequency data in the context of data-poor fisheries. Includes methods and examples included in the FAO Manual by P. Sparre and S.C. Venema (1998), "Introduction to tropical fish stock assessment" (< https://openknowledge.fao.org/server/api/core/bitstreams/bc7c37b6-30df-49c0-b5b4-8367a872c97e/content>), as well as other more recent methods.

Structural Exploration of the Gene Ontology (GO) Knowledge Base

It provides an effective, efficient, and fast way to explore the Gene Ontology (GO). Given a set of genes, the package contains functions to assess the GO and obtain the terms associated with the genes and the levels of the GO terms. The package provides functions for the three different GO ontology. We discussed the methods explicitly in the following article .

Survey Standard Error Estimation for Cumulated Estimates and their Differences in Complex Panel Designs

Calculate point estimates and their standard errors in complex household surveys using bootstrap replicates. Bootstrapping considers survey design with a rotating panel. A comprehensive description of the methodology can be found under < https://statistikat.github.io/surveysd/articles/methodology.html>.

Coalescent-Based Simulation of Ecological Communities

Coalescent-Based Simulation of Ecological Communities as proposed by Munoz et al. (2018) . The package includes a tool for estimating parameters of community assembly by using Approximate Bayesian Computation.