**Microsoft Academic’ API Client** (**microdemic**)

The ‘Microsoft Academic Knowledge’ API provides programmatic access to scholarly articles in the ‘Microsoft Academic Graph’ (<https://…/> ). Includes methods matching all ‘Microsoft Academic’ API routes, including search, graph search, text similarity, and interpret natural language query string.

**Fit Repeated Linear Regressions** (**fRLR**)

When fitting a set of linear regressions which have some same variables, we can separate the matrix and reduce the computation cost. This package aims to fit a set of repeated linear regressions faster. More details can be found in this blog Lijun Wang (2017) <https://…/>.

**String Distance Calculation with Tidy Data Principles** (**tidystringdist**)

Calculation of string distance following the tidy data principles. Built on top of the ‘stringdist’ package.

**Draws Overview of Outliers (O3) Plot** (**OutliersO3**)

Potential outliers are identified for all combinations of a dataset’s variables. The available methods are HDoutliers() from the package ‘HDoutliers’, FastPCS() from the package ‘FastPCS’, mvBACON() from ‘robustX’, adjOutlyingness() from ‘robustbase’, DectectDeviatingCells() from ‘cellWise’.

**Construct Rich Tables for Output to ‘HTML’/’Excel’** (**basictabler**)

Easily create tables from data frames/matrices. Create/manipulate tables row-by-row, column-by-column or cell-by-cell. Use common formatting/styling to output rich tables as ‘HTML’, ‘HTML widgets’ or to ‘Excel’.

**Randomer Forest** (**rerf**)

Random Forester (RerF) is an algorithm developed by Tomita (2016) <arXiv:1506.03410v2> which is similar to Random Forest – Random Combination (Forest-RC) developed by Breiman (2001) <doi:10.1023/A:1010933404324>. Random Forests create axis-parallel, or orthogonal trees. That is, the feature space is recursively split along directions parallel to the axes of the feature space. Thus, in cases in which the classes seem inseparable along any single dimension, Random Forests may be suboptimal. To address this, Breiman also proposed and characterized Forest-RC, which uses linear combinations of coordinates rather than individual coordinates, to split along. This package, ‘rerf’, implements RerF which is similar to Forest-RC. The difference between the two algorithms is where the random linear combinations occur: Forest-RC combines features at the per tree level whereas RerF takes linear combinations of coordinates at every node in the tree.

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