variables, basefun, MASS, TH.data, knitr, prodlim, gridExtra,
nnet, HSAUR3, sandwich, grid, latticeExtra, colorspace, mgcv,
ICsurv, polspline, logspline, bshazard, gss, TransModel,
dynsurv, timereg, survivalMPL, gamlss, Matrix, muhaz, ordinal,
VGAM, AER, coin, gamlss.data, mlbench, tram (>= 0.6-0), rms

numDeriv, stats, eha, multcomp (>= 1.4-4), lattice, survival,
flexsurv, truncreg

person("G.", "Dudnik", email = "gl.dudnik@gmail.com", role = c("aut", "cre", "cph"))
A fast, robust and easy-to-use calculation of multi-class classification evaluation metrics based on confusion matrix.

A collection of machine learning helper functions, particularly assisting in the Exploratory Data Analysis phase.
Makes heavy use of the 'data.table' package for optimal speed and memory efficiency. Highlights include a versatile bin_data()
function, sparsify() for converting a data.table to sparse matrix format with one-hot encoding, fast evaluation metrics, and
empirical_cdf() for calculating empirical Multivariate Cumulative Distribution Functions.
data.table(>= 1.9.7), Matrix, methods, stats

A collection of machine learning helper functions, particularly assisting in the Exploratory Data Analysis phase. Makes heavy use of the 'data.table' package for optimal speed and memory efficiency. Highlights include a versatile bin_data() function, sparsify() for converting a data.table to sparse matrix format with one-hot encoding, fast evaluation metrics, and empirical_cdf() for calculating empirical Multivariate Cumulative Distribution Functions.

Vocabulary and Corpus Preprocessing for Natural Language
Pipelines
person("Vitalie", "Spinu", email = "spinuvit@gmail.com", role = c("aut", "cre"))
Utilities for preprocessing of text corpora into data structures
suitable for natural language models: integer sequences or matrices,
vocabulary embedding matrices, term-doc, doc-term, term co-occurrence matrices
etc. All functions allow for full or partial hashing of the terms in the
vocabulary.

C++11 with suport for regex (such as GCC 4.9 or
later, > 5 prefered)