A socket server allows to connect clients to R.

Functions to enumerate and reference figures, tables and equations
  in R Markdown documents that do not support these features (thus not
  'bookdown' or 'quarto'. Supporting functions for using 'Sweave' and 'Knitr'
  with 'LyX'.

Set of tools aimed at wrapping some of the functionalities of the
  packages tools, utils and codetools into a nicer format so that an IDE can use
  them.

High level management of widgets, windows and other graphical resources.

Estimates the population average controlled difference for a given outcome between levels of a binary treatment, exposure, or other group membership variable of interest for clustered, stratified survey samples where sample selection depends on the comparison group. Provides three methods for estimation, namely outcome modeling and two factorizations of inverse probability weighting. Under stronger assumptions, these methods estimate the causal population average treatment effect. Salerno et al., (2024) <doi:10.48550/arXiv.2406.19597>.

Mixed-effect proportional hazards models for multistage stratified, 
   cluster-sampled, unequally weighted survey samples. Provides variance 
   estimation by Taylor series linearisation or replicate weights.

Diagnostics for fixed effects linear and general linear regression models fitted with survey data. Extensions of standard diagnostics to complex survey data are included: standardized residuals, leverages, Cook's D, dfbetas, dffits, condition indexes, and variance inflation factors as found in Li and Valliant (Surv. Meth., 2009, 35(1), pp. 15-24; Jnl. of Off. Stat., 2011, 27(1), pp. 99-119; Jnl. of Off. Stat., 2015, 31(1), pp. 61-75); Liao and Valliant (Surv. Meth., 2012, 38(1), pp. 53-62; Surv. Meth., 2012, 38(2), pp. 189-202).  Variance inflation factors and condition indexes are also computed for some general linear models as described in Liao (U. Maryland thesis, 2010).

Linear mixed models for complex survey data, by pairwise composite likelihood, as described in Lumley & Huang (2023) <arXiv:2311.13048>. Supports nested and crossed random effects, and correlated random effects as in genetic models.  Allows for multistage sampling and for other designs where pairwise sampling probabilities are specified or can be calculated.