**Dynamic generation of scientific reports** (**R.rsp**)

R.rsp is an R package that implements a compiler for the RSP markup language. RSP can be used to embed dynamic R code in any text-based source document to be compiled into a final document, e.g. RSP-embedded LaTeX into PDF, RSP-embedded Markdown into HTML, RSP-embedded HTML into HTML and so on. The package provides a set of vignette engines making it straightforward to use RSP in vignettes and there are also other vignette engines to, for instance, include static PDF vignettes. Starting with R.rsp v0.20.0 (on CRAN), a vignette engine for including plain LaTeX-based vignettes is also available. The R.rsp package installs out-of-the-box on all common operating systems, including Linux, OS X and Windows.

**Partitioning Using Deletion, Substitution, and Addition Moves** (**partDSA**)

A novel tool for generating a piecewise constant estimation list of increasingly complex predictors based on an intensive and comprehensive search over the entire covariate space.

**Rank Correlations** (**pvrank**)

Computes rank correlations and their p-values with various options for tied ranks.

**Multinomial Logit Models with Random Parameters** (**gmnl**)

An implementation of maximum simulated likelihood method for the estimation of multinomial logit models with random coefficients. Specifically, it allows estimating models with continuous heterogeneity such as the mixed multinomial logit and the generalized multinomial logit. It also allows estimating models with discrete heterogeneity such as the latent class and the mixed-mixed multinomial logit model.

**Diff, Patch and Merge for Data.frames** (**daff**)

Diff, patch and merge for data frames. Document changes in data sets and use them to apply patches. Changes to data can be made visible by using render_diff. Daff uses the V8 package to wrap the ‘daff.js’ javascript library which is included in the package. Daff exposes a subset of ‘daff.js’ functionality, tailored for usage within R.

**Implementation of Colored Independent Component Analysis and Spatial Colored Independent Component Analysis** (**coloredICA**)

It implements colored Independent Component Analysis (Lee et al., 2011) and spatial colored Independent Component Analysis (Shen et al., 2014). They are two algorithms to perform ICA when sources are assumed to be temporal or spatial stochastic processes, respectively.

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