Provides a friendly interface for modifying data frames with a sequence of piped commands built upon the 'tidyverse' Wickham et al., (2019) <doi:10.21105/joss.01686> . The majority of commands wrap 'dplyr' mutate statements in a convenient way to concisely solve common issues that arise when tidying small to medium data sets. Includes smart defaults and allows flexible selection of columns via 'tidyselect'.
dplyr, stringr, stringi, tidyselect, purrr, janitor, rlang,
lubridate, magrittr, tibble, rstudioapi, tidyr, forcats, bit64,
rio, readr, vroom, readxl, fs, rlist, printr

dplyr, stringr, tidyselect, purrr, janitor, rlang, lubridate,
magrittr, tibble, rstudioapi, forcats, bit64, rio, readr,
vroom, fs, rlist, fastDummies

Gabor Csardi, Titus Wormer, Maciej Ceglowski, Jacob R. Rideout,
and Kent S. Johnson
With no external dependencies and
support for 335 languages; all languages spoken by
more than one million speakers. 'Franc' is a port
of the 'JavaScript' project of the same name,
see <https://github.com/wooorm/franc>.
'distances.R' 'expressions.R' 'franc.R' 'ngrams.R'
'normalize.R' 'script.R' 'speakers.R' 'trigrams.R'

Automatic Data Processing and Visualization for FRAP
Automatically process Fluorescence Recovery After Photobleaching (FRAP) data and generate consistent, publishable figures. Note: this package does not replace 'ImageJ' (or its equivalence) in raw image quantification. Some references about the methods: Sprague, Brian L. (2004) <doi:10.1529/biophysj.103.026765>; Day, Charles A. (2012) <doi:10.1002/0471142956.cy0219s62>.

Implementation of the "Flexible kernels for RBF network"
algorithm
Flexible kernerl for radial basis function network
implementation.

Lala Septem Riza, Christoph Bergmeir, Francisco Herrera
Triguero, and Jose Manuel Benitez
This package implements functionality and various
algorithms to build and use fuzzy rule-based systems (FRBS).
FRBSs are based on the fuzzy concept, proposed by Zadeh in
1965, which aims at representing the reasoning of human experts
in a set of IF-THEN rules, to handle real-life problems in,
e.g., control, prediction and inference, data mining,
bioinformatics data processing, robotics, and speech
recognition. FRBSs are also known as fuzzy inference systems
and fuzzy models. During the modeling of an FRBS, there are two
important steps that need to be conducted: structure
identification and parameter estimation. Nowadays, there exists
a wide variety of algorithms to generate fuzzy IF-THEN rules
automatically from numerical data, covering both steps.
Approaches that have been used in the past are, e.g., heuristic
procedures, neuro-fuzzy techniques, clustering methods, genetic
algorithms, least squares methods, gradient descent, etc. This
package aims to implement the most widely used standard
procedures, thus offering a standard package for FRBS modeling
to the R community.
'ANFIS.R' 'ANFIS.update.R' 'convert.MF.R' 'convert.rule.R'
'data.gen3d.R' 'defuzzifier.R' 'DENFIS.eng.R' 'DENFIS.R'
'denorm.data.R' 'DM.R' 'DM.update.R' 'docData.R' 'ECM.R'
'frbs-package.R' 'frbs.eng.R' 'frbs.gen.R' 'frbs.learn.R'
'frbs.plot.R' 'predict.frbs.R' 'frcs.eng.R' 'frcs.R'
'fuzzifier.R' 'GFS.R' 'HGD.R' 'HGD.update.R' 'HyFIS.R'
'HyFIS.update.R' 'inference.R' 'norm.data.R' 'popubaru.R'
'rulebase.R' 'SBC.R' 'SBC.test.R' 'summary.R' 'WM.R' 'MSGFS.R'
'MSGFS.test.R'