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Suite of Deterministic and Robust Algorithms for Linear Regression
DetLTS, DetMM (and DetS) Algorithms for Deterministic, Robust Linear Regression.
Animated Interactive Grammar of Graphics
Functions are provided for defining animated,
interactive data visualizations in R code, and rendering
on a web page. The 2018 Journal of Computational and
Graphical Statistics paper,
R Package Installation from Remote Repositories, Including 'GitHub'
Download and install R packages stored in 'GitHub', 'GitLab', 'Bitbucket', 'Bioconductor', or plain 'subversion' or 'git' repositories. This package provides the 'install_*' functions in 'devtools'. Indeed most of the code was copied over from 'devtools'.
Tools for Developing R Packages Interfacing with 'Stan'
Provides various tools for developers of R packages interfacing with 'Stan' < https://mc-stan.org>, including functions to set up the required package structure, S3 generics and default methods to unify function naming across 'Stan'-based R packages, and vignettes with recommendations for developers.
Access the Bioconductor Project Package Repository
A convenient tool to install and update Bioconductor packages.
Bayesian Additive Regression Trees with Stan-Sampled Parametric Extensions
Fits semiparametric linear and multilevel models with non-parametric additive Bayesian additive regression tree (BART; Chipman, George, and McCulloch (2010)
Define and Work with Parameter Spaces for Complex Algorithms
Define parameter spaces, constraints and dependencies for arbitrary algorithms, to program on such spaces. Also includes statistical designs and random samplers. Objects are implemented as 'R6' classes.
R Interface to Stan
User-facing R functions are provided to parse, compile, test, estimate, and analyze Stan models by accessing the header-only Stan library provided by the 'StanHeaders' package. The Stan project develops a probabilistic programming language that implements full Bayesian statistical inference via Markov Chain Monte Carlo, rough Bayesian inference via 'variational' approximation, and (optionally penalized) maximum likelihood estimation via optimization. In all three cases, automatic differentiation is used to quickly and accurately evaluate gradients without burdening the user with the need to derive the partial derivatives.
Generalized Linear Mixed Models using Template Model Builder
Fit linear and generalized linear mixed models with various extensions, including zero-inflation. The models are fitted using maximum likelihood estimation via 'TMB' (Template Model Builder). Random effects are assumed to be Gaussian on the scale of the linear predictor and are integrated out using the Laplace approximation. Gradients are calculated using automatic differentiation.
Extension of `data.frame`
Fast aggregation of large data (e.g. 100GB in RAM), fast ordered joins, fast add/modify/delete of columns by group using no copies at all, list columns, friendly and fast character-separated-value read/write. Offers a natural and flexible syntax, for faster development.