Showing 7 of total 7 results (show query)
tidyverse
dplyr:A Grammar of Data Manipulation
A fast, consistent tool for working with data frame like objects, both in memory and out of memory.
Maintained by Hadley Wickham. Last updated 26 days ago.
4.8k stars 24.68 score 659k scripts 7.8k dependentstidyverse
dbplyr:A 'dplyr' Back End for Databases
A 'dplyr' back end for databases that allows you to work with remote database tables as if they are in-memory data frames. Basic features works with any database that has a 'DBI' back end; more advanced features require 'SQL' translation to be provided by the package author.
Maintained by Hadley Wickham. Last updated 4 months ago.
481 stars 19.72 score 5.2k scripts 736 dependents
heardacat
Ramble:Parser Combinator for R
Parser generator for R using combinatory parsers. It is inspired by combinatory parsers developed in Haskell.
Maintained by Chapman Siu. Last updated 8 years ago.
combinatory-parsersparser-combinatorsparsing
22 stars 5.93 score 39 scripts
swihart
event:Event History Procedures and Models
Functions for setting up and analyzing event history data.
Maintained by Bruce Swihart. Last updated 8 years ago.
1 stars 4.74 score 548 scripts
djpedregal
UComp:Automatic Univariate Time Series Modelling of many Kinds
Comprehensive analysis and forecasting of univariate time series using automatic time series models of many kinds. Harvey AC (1989) <doi:10.1017/CBO9781107049994>. Pedregal DJ and Young PC (2002) <doi:10.1002/9780470996430>. Durbin J and Koopman SJ (2012) <doi:10.1093/acprof:oso/9780199641178.001.0001>. Hyndman RJ, Koehler AB, Ord JK, and Snyder RD (2008) <doi:10.1007/978-3-540-71918-2>. Gómez V, Maravall A (2000) <doi:10.1002/9781118032978>. Pedregal DJ, Trapero JR and Holgado E (2024) <doi:10.1016/j.ijforecast.2023.09.004>.
Maintained by Diego J. Pedregal. Last updated 29 days ago.
1 stars 1.70 score 1 scripts
bentaylor1
deepNN:Deep Learning
Implementation of some Deep Learning methods. Includes multilayer perceptron, different activation functions, regularisation strategies, stochastic gradient descent and dropout. Thanks go to the following references for helping to inspire and develop the package: Ian Goodfellow, Yoshua Bengio, Aaron Courville, Francis Bach (2016, ISBN:978-0262035613) Deep Learning. Terrence J. Sejnowski (2018, ISBN:978-0262038034) The Deep Learning Revolution. Grant Sanderson (3brown1blue) <https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi> Neural Networks YouTube playlist. Michael A. Nielsen <http://neuralnetworksanddeeplearning.com/> Neural Networks and Deep Learning.
Maintained by Benjamin Taylor. Last updated 2 years ago.
1.34 score 22 scripts
cran
assist:A Suite of R Functions Implementing Spline Smoothing Techniques
Fit various smoothing spline models. Includes an ssr() function for smoothing spline regression, an nnr() function for nonparametric nonlinear regression, an snr() function for semiparametric nonlinear regression, an slm() function for semiparametric linear mixed-effects models, and an snm() function for semiparametric nonlinear mixed-effects models. See Wang (2011) <doi:10.1201/b10954> for an overview.
Maintained by Yuedong Wang. Last updated 2 years ago.
1.00 score