Provides tools that can be used to calculate, evaluate, plot and use for inference the profiles of *arbitrary* inference functions for *arbitrary* 'glm'-like fitted models with linear predictors. More information on the methods that are implemented can be found in Kosmidis (2008) <https://www.r-project.org/doc/Rnews/Rnews_2008-2.pdf>.

A suite of multivariate methods and data visualization 
      tools to implement profile analysis and cross-validation techniques described in 
      Davison & Davenport (2002) <DOI: 10.1037/1082-989X.7.4.468>, Bulut (2013), and other published and unpublished resources.
      The package includes routines to perform criterion-related profile analysis, profile analysis 
      via multidimensional scaling, moderated profile analysis, profile analysis by group, and a 
      within-person factor model to derive score profiles.

Help unify visual output of R analyses in the Profinit EU company. 
  So far, there are color and fill scales for 'ggplot2', plotting theme for 
  'ggplot2', color palettes and utils to make the tools default choices.

A simple and light-weight API for memory profiling of R expressions.  The profiling is built on top of R's built-in memory profiler ('utils::Rprofmem()'), which records every memory allocation done by R (also native code).

Combine probabilistic forecasts using CRPS learning algorithms proposed in Berrisch, Ziel (2021) <arXiv:2102.00968> <doi:10.1016/j.jeconom.2021.11.008>. The package implements multiple online learning algorithms like Bernstein online aggregation; see Wintenberger (2014) <arXiv:1404.1356>. Quantile regression is also implemented for comparison purposes. Model parameters can be tuned automatically with respect to the loss of the forecast combination. Methods like predict(), update(), plot() and print() are available for convenience. This package utilizes the optim C++ library for numeric optimization <https://github.com/kthohr/optim>.

Provides an R interface for the PROFOUND database <doi:10.5880/PIK.2019.008>. The 
    PROFOUND database contains a wide range of data  to evaluate vegetation models and simulate climate 
    impacts at the forest stand scale. It includes 9 forest sites across Europe, and provides for them a site 
    description as well as soil, climate, CO2, Nitrogen deposition, tree-level, forest stand-level 
    and remote sensing data. Moreover, for a subset of 5 sites, also time series of carbon fluxes, 
    energy balances and soil water are available.