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, Bulut (2013), and other published and unpublished resources.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.

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) <http://conservancy.umn.edu/handle/11299/155592>, and other published and unpublished resources.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.

Fit profile analytic models. This includes multivariate methods and data visualization tools
to implement profile analysis and cross-validation techniques described
in Davison & Davenport (2002) and Bulut (2013).
Okan Bulut <okanbulut84@gmail.com>, Christopher David Desjardins <cddesjardins@gmail.com>,

Allows users to quickly and reliably profile data in R
using convenience functions. The profiled data returns as a data.frame
and provides a wealth of common and uncommon summary statistics.

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).

Probabilistic Forecast Combination Using CRPS Learning
c(
person(given = "Jonathan",
family = "Berrisch",
role = "cre",
email = "Jonathan@Berrisch.biz",
comment = c(ORCID = "0000-0002-4944-9074")),
person(given = "Florian",
family = "Ziel",
role = "aut",
comment = c(ORCID = "0000-0002-2974-2660"))
)
Combine probabilistic forecasts using CRPS learning algorithms proposed in Berrisch, Ziel (2021) <arXiv:2102.00968>. 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>.
Rcpp, RcppArmadillo, RcppProgress, splines2 (>= 0.3.0)
testthat (>= 3.0.0), gamlss.dist, ggplot2