Providing new german-wide 'TapeR' Models and functions for their 
  evaluation. Included are the most common tree species in Germany (Norway 
  spruce, Scots pine, European larch, Douglas fir, Silver fir as well as 
  European beech, Common/Sessile oak and Red oak). Many other species are mapped
  to them so that 36 tree species / groups can be processed. Single trees are 
  defined by species code, one or multiple diameters in arbitrary measuring 
  height and tree height. The functions then provide information on diameters 
  along the stem, bark thickness, height of diameters, volume of the total or 
  parts of the trunk and total and component above-ground biomass. It is also 
  possible to calculate assortments from the taper curves. Uncertainty 
  information is provided for diameter, volume and component biomass estimation.

Wrapper for using 'tapkee' command line utility,
 it allows to run it from inside R and catch the results for further analysis and plotting.
 'Tapkee' is a program for fast dimension reduction, see 'package?tapkee' and <http://tapkee.lisitsyn.me/>
 for installation and other details.

Swift and seamless Single Sign-On (SSO) integration. Designed
    for effortless compatibility with popular Single Sign-On providers
    like Google and Microsoft, it streamlines authentication, enhancing
    both user experience and application security. Elevate your 'shiny'
    applications for a simplified, unified, and secure authentication process.

Functions to produce, fit and predict from bipartite networks with abundance, trait and phylogenetic information. Its methods are described in detail in Benadi, G., Dormann, C.F., Fruend, J., Stephan, R. & Vazquez, D.P. (2021) Quantitative prediction of interactions in bipartite networks based on traits, abundances, and phylogeny. The American Naturalist, in press.

Identification and estimation of the autoregressive threshold models with Gaussian noise, as well as positive-valued time series. The package provides the identification of the number of regimes, the thresholds and the autoregressive orders, as well as the estimation of remain parameters. The package implements the methodology from the 2005 paper: Modeling Bivariate Threshold Autoregressive Processes in the Presence of Missing Data <DOI:10.1081/STA-200054435>.