Object Pooling (pool)
Enables the creation of object pools, which make it less computationally expensive to fetch a new object. Currently the only supported pooled objects are ‘DBI’ connections.

Implementing GSCAD Method for Image Denoising and Inpainting (GSCAD)
Method proposed in ‘Simultaneous Sparse Dictionary Learning and Pruning’ ( Qu and Wang (2016) <arXiv:1605.07870>) is implemented. The idea is to conduct a linear decomposition of a signal using a few atoms of a learned and usually over-completed dictionary instead of a pre-defined basis. A proper size of the to-be-learned dictionary is determining at the same time during the procedure. Application includes image denoising and image inpainting.

The Spike-and-Slab LASSO (SSLASSO)
Efficient algorithms for fitting regularization paths for linear models penalized by Spike-and-Slab LASSO.

Dyadic Network Analysis (dyads)
Includes a function for estimation of the p2 model (van Duijn, Snijders and Zijlstra (2004) <doi:10.1046/j.0039-0402.2003.00258.x>), more specifically, the adaptive random walk algorithm (Zijlstra, van Duijn and Snijders (2009) <doi:10.1348/000711007X255336>).

RStudio’ Addin for Editing a ‘data.frame’ (editData)
An ‘RStudio’ addin for editing a ‘data.frame’ or a ‘tibble’. You can delete, add or update a ‘data.frame’ without coding. You can get resultant data as a ‘tibble’ or ‘data.frame’.

Exact Confidence Intervals for Random Effects Meta-Analyses (rma.exact)
Compute an exact CI for the population mean under a random effects model. The routines implement the algorithm described in Michael, Thronton, Xie, and Tian (2017) <https://…/Exact_Inference_Meta.pdf>.