An evolution of 'reshape2'. It's designed specifically for data
tidying (not general reshaping or aggregating) and works well with
'dplyr' data pipelines.
A backend for the selecting functions of the 'tidyverse'. It makes it
easy to implement select-like functions in your own packages in a way
that is consistent with other 'tidyverse' interfaces for selection.
Extends the functionality of 'ggplot2', providing the capability to
plot ternary diagrams for (subset of) the 'ggplot2' geometries.
Additionally, 'ggtern' has implemented several NEW geometries which
are unavailable to the standard 'ggplot2' release. For further
examples and documentation, please proceed to the 'ggtern' website.
Provides functions for the consistent analysis of compositional data
(e.g. portions of substances) and positive numbers (e.g.
concentrations) in the way proposed by J. Aitchison and V.
Pawlowsky-Glahn.
"Essential" Robust Statistics. Tools allowing to analyze data with
robust methods. This includes regression methodology including model
selections and multivariate statistics where we strive to cover the
book "Robust Statistics, Theory and Methods" by 'Maronna, Martin and
Yohai'; Wiley 2006.
Provides convenience functions for advanced linear algebra with
tensors and computation with datasets of tensors on a higher level
abstraction. It includes Einstein and Riemann summing conventions,
dragging, co- and contravariate indices, parallel computations on
sequences of tensors.
Differential Evolution (DE) stochastic algorithms for global
optimization of problems with and without constraints. The aim is to
curate a collection of its state-of-the-art variants that (1) do not
sacrifice simplicity of design, (2) are essentially tuning-free, and
(3) can be efficiently implemented directly in the R language.
Currently, it only provides an implementation of the 'jDE' algorithm
by Brest et al. (2006) <doi:10.1109/TEVC.2006.872133>.
E-statistics (energy) tests and statistics for multivariate and
univariate inference, including distance correlation, one-sample,
two-sample, and multi-sample tests for comparing multivariate
distributions, are implemented. Measuring and testing multivariate
independence based on distance correlation, partial distance
correlation, multivariate goodness-of-fit tests, k-groups and
hierarchical clustering based on energy distance, testing for
multivariate normality, distance components (disco) for non-parametric
analysis of structured data, and other energy statistics/methods are
implemented.
Covers many important models used in marketing and micro-econometrics
applications. The package includes: Bayes Regression (univariate or
multivariate dep var), Bayes Seemingly Unrelated Regression (SUR),
Binary and Ordinal Probit, Multinomial Logit (MNL) and Multinomial
Probit (MNP), Multivariate Probit, Negative Binomial (Poisson)
Regression, Multivariate Mixtures of Normals (including clustering),
Dirichlet Process Prior Density Estimation with normal base,
Hierarchical Linear Models with normal prior and covariates,
Hierarchical Linear Models with a mixture of normals prior and
covariates, Hierarchical Multinomial Logits with a mixture of normals
prior and covariates, Hierarchical Multinomial Logits with a Dirichlet
Process prior and covariates, Hierarchical Negative Binomial
Regression Models, Bayesian analysis of choice-based conjoint data,
Bayesian treatment of linear instrumental variables models, Analysis
of Multivariate Ordinal survey data with scale usage heterogeneity (as
in Rossi et al, JASA (01)), Bayesian Analysis of Aggregate Random
Coefficient Logit Models as in BLP (see Jiang, Manchanda, Rossi 2009)
For further reference, consult our book, Bayesian Statistics and
Marketing by Rossi, Allenby and McCulloch (Wiley 2005) and Bayesian
Non- and Semi-Parametric Methods and Applications (Princeton U Press
2014).
Parses and converts LaTeX math formulas to R's plotmath expressions,
used to enter mathematical formulas and symbols to be rendered as
text, axis labels, etc. throughout R's plotting system.
