Delete or Merge Regressors Algorithms for Linear and Logistic Model Selection and High-Dimensional Data (DMRnet)
Model selection algorithms for regression and classification, where the predictors can be numerical and categorical and the number of regressors exceeds the number of observations. The selected model consists of a subset of numerical regressors and partitions of levels of factors. Aleksandra Maj-KaÅ„ska, Piotr Pokarowski and Agnieszka Prochenka (2015) <doi:10.1214/15-EJS1050>. Piotr Pokarowski and Jan Mielniczuk (2015) <http://…/pokarowski15a.pdf>.

RStudio Addin for Teaching and Learning Data Manipulation Using ‘dplyr’ (dplyrAssist)
An RStudio addin for teaching and learning data manipulation using the ‘dplyr’ package. You can learn each steps of data manipulation by clicking your mouse without coding. You can get resultant data (as a ‘tibble’) and the code for data manipulation.

Tokenization, Parts of Speech Tagging, Lemmatization and Dependency Parsing with the ‘UDPipe’ ‘NLP’ Toolkit (udpipe)
This natural language processing toolkit provides language-agnostic ‘tokenization’, ‘parts of speech tagging’, ‘lemmatization’ and ‘dependency parsing’ of raw text. Next to text parsing, the package also allows you to train annotation models based on data of ‘treebanks’ in ‘CoNLL-U’ format as provided at <http://…/format.html>. The techniques are explained in detail in the paper: ‘Tokenizing, POS Tagging, Lemmatizing and Parsing UD 2.0 with UDPipe’, available at <doi:10.18653/v1/K17-3009>.

Statistical Tools for Filebacked Big Matrices (bigstatsr)
Easy-to-use, efficient, flexible and scalable statistical tools. Package bigstatsr provides and uses Filebacked Big Matrices via memory-mapping. It provides for instance matrix operations, Principal Component Analysis, sparse linear supervised models, utility functions and more. A scientific paper associated with this package is in preparation.

Extreme Learning Machine for Survival Analysis (ELMSurv)
We use the Buckley-James method to impute the data and extend the emerging Extreme Learning Machine approach to survival analysis. Currently, only right censored data are supported. For a detailed information, see the paper by Hong Wang, Jianxin Wang and Lifeng Zhou (2017) <https://…/elmsurv-revised.pdf>, which will appear in Applied Intelligence <https://…/10489> soon.