|
r-foghorn
|
public |
The CRAN check results and where your package stands in the CRAN submission queue in your R terminal.
|
2025-04-22 |
|
r-fmultivar
|
public |
A collection of functions inspired by Venables and Ripley (2002) <doi:10.1007/978-0-387-21706-2> and Azzalini and Capitanio (1999) <arXiv:0911.2093> to manage, investigate and analyze bivariate and multivariate data sets of financial returns.
|
2025-04-22 |
|
r-fmeffects
|
public |
Create local, regional, and global explanations for any machine learning model with forward marginal effects. You provide a model and data, and 'fmeffects' computes feature effects. The package is based on the theory in: C. A. Scholbeck, G. Casalicchio, C. Molnar, B. Bischl, and C. Heumann (2022) <arXiv:2201.08837>.
|
2025-04-22 |
|
r-fma
|
public |
All data sets from "Forecasting: methods and applications" by Makridakis, Wheelwright & Hyndman (Wiley, 3rd ed., 1998) <https://robjhyndman.com/forecasting/>.
|
2025-04-22 |
|
r-flexsurvcure
|
public |
Flexible parametric mixture and non-mixture cure models for time-to-event data.
|
2025-04-22 |
|
r-fitdistrplus
|
public |
Extends the fitdistr() function (of the MASS package) with several functions to help the fit of a parametric distribution to non-censored or censored data. Censored data may contain left censored, right censored and interval censored values, with several lower and upper bounds. In addition to maximum likelihood estimation (MLE), the package provides moment matching (MME), quantile matching (QME), maximum goodness-of-fit estimation (MGE) and maximum spacing estimation (MSE) methods (available only for non-censored data). Weighted versions of MLE, MME, QME and MSE are available. See e.g. Casella & Berger (2002), Statistical inference, Pacific Grove, for a general introduction to parametric estimation.
|
2025-04-22 |
|
r-finetune
|
public |
The ability to tune models is important. 'finetune' enhances the 'tune' package by providing more specialized methods for finding reasonable values of model tuning parameters. Two racing methods described by Kuhn (2014) <arXiv:1405.6974> are included. An iterative search method using generalized simulated annealing (Bohachevsky, Johnson and Stein, 1986) <doi:10.1080/00401706.1986.10488128> is also included.
|
2025-04-22 |
|
r-fincal
|
public |
Package for time value of money calculation, time series analysis and computational finance.
|
2025-04-22 |
|
r-filesstrings
|
public |
This started out as a package for file and string manipulation. Since then, the 'fs' and 'strex' packages emerged, offering functionality previously given by this package (but it's done better in these new ones). Those packages have hence almost pushed 'filesstrings' into extinction. However, it still has a small number of unique, handy file manipulation functions which can be seen in the vignette. One example is a function to remove spaces from all file names in a directory.
|
2025-04-22 |
|
r-finalfit
|
public |
Generate regression results tables and plots in final format for publication. Explore models and export directly to PDF and 'Word' using 'RMarkdown'.
|
2025-04-22 |
|
r-fieldhub
|
public |
A shiny design of experiments (DOE) app that aids in the creation of traditional, un-replicated, augmented and partially-replicated designs applied to agriculture, plant breeding, forestry, animal and biological sciences.
|
2025-04-22 |
|
r-feisr
|
public |
Provides the function feis() to estimate fixed effects individual slope (FEIS) models. The FEIS model constitutes a more general version of the often-used fixed effects (FE) panel model, as implemented in the package 'plm' by Croissant and Millo (2008) <doi:10.18637/jss.v027.i02>. In FEIS models, data are not only person demeaned like in conventional FE models, but detrended by the predicted individual slope of each person or group. Estimation is performed by applying least squares lm() to the transformed data. For more details on FEIS models see Bruederl and Ludwig (2015, ISBN:1446252442); Frees (2001) <doi:10.2307/3316008>; Polachek and Kim (1994) <doi:10.1016/0304-4076(94)90075-2>; Ruettenauer and Ludwig (2020) <doi:10.1177/0049124120926211>; Wooldridge (2010, ISBN:0262294354). To test consistency of conventional FE and random effects estimators against heterogeneous slopes, the package also provides the functions feistest() for an artificial regression test and bsfeistest() for a bootstrapped version of the Hausman test.
|
2025-04-22 |
|
r-feddata
|
public |
Functions to automate downloading geospatial data available from several federated data sources (mainly sources maintained by the US Federal government). Currently, the package enables extraction from seven datasets: The National Elevation Dataset digital elevation models (1 and 1/3 arc-second; USGS); The National Hydrography Dataset (USGS); The Soil Survey Geographic (SSURGO) database from the National Cooperative Soil Survey (NCSS), which is led by the Natural Resources Conservation Service (NRCS) under the USDA; the Global Historical Climatology Network (GHCN), coordinated by National Climatic Data Center at NOAA; the Daymet gridded estimates of daily weather parameters for North America, version 3, available from the Oak Ridge National Laboratory's Distributed Active Archive Center (DAAC); the International Tree Ring Data Bank; and the National Land Cover Database (NLCD).
|
2025-04-22 |
|
r-feasts
|
public |
Provides a collection of features, decomposition methods, statistical summaries and graphics functions for the analysing tidy time series data. The package name 'feasts' is an acronym comprising of its key features: Feature Extraction And Statistics for Time Series.
|
2025-04-22 |
|
r-fcps
|
public |
Over sixty clustering algorithms are provided in this package with consistent input and output, which enables the user to try out algorithms swiftly. Additionally, 26 statistical approaches for the estimation of the number of clusters as well as the mirrored density plot (MD-plot) of clusterability are implemented. The packages is published in Thrun, M.C., Stier Q.: "Fundamental Clustering Algorithms Suite" (2021), SoftwareX, <DOI:10.1016/j.softx.2020.100642>. Moreover, the fundamental clustering problems suite (FCPS) offers a variety of clustering challenges any algorithm should handle when facing real world data, see Thrun, M.C., Ultsch A.: "Clustering Benchmark Datasets Exploiting the Fundamental Clustering Problems" (2020), Data in Brief, <DOI:10.1016/j.dib.2020.105501>.
|
2025-04-22 |
|
r-fdboost
|
public |
Regression models for functional data, i.e., scalar-on-function, function-on-scalar and function-on-function regression models, are fitted by a component-wise gradient boosting algorithm. For a manual on how to use 'FDboost', see Brockhaus, Ruegamer, Greven (2017) <doi:10.18637/jss.v094.i10>.
|
2025-04-22 |
|
r-fastrmodels
|
public |
A data package that hosts all models for the 'nflfastR' package.
|
2025-04-22 |
|
r-faux
|
public |
Create datasets with factorial structure through simulation by specifying variable parameters. Extended documentation at <https://debruine.github.io/faux/>. Described in DeBruine (2020) <doi:10.5281/zenodo.2669586>.
|
2025-04-22 |
|
r-fastai
|
public |
The 'fastai' <https://docs.fast.ai/index.html> library simplifies training fast and accurate neural networks using modern best practices. It is based on research in to deep learning best practices undertaken at 'fast.ai', including 'out of the box' support for vision, text, tabular, audio, time series, and collaborative filtering models.
|
2025-04-22 |
|
r-fastverse
|
public |
Easy installation, loading and management, of high-performance packages for statistical computing and data manipulation in R. The core 'fastverse' consists of 4 packages: 'data.table', 'collapse', 'kit' and 'magrittr', that jointly only depend on 'Rcpp'. The 'fastverse' can be freely and permanently extended with additional packages, both globally or for individual projects. Separate package verses can also be created. Fast packages for many common tasks such as time series, dates and times, strings, spatial data, statistics, data serialization, larger-than-memory processing, and compilation of R code are listed in the README file: <https://github.com/fastverse/fastverse#suggested-extensions>.
|
2025-04-22 |
|
r-fastdummies
|
public |
Creates dummy columns from columns that have categorical variables (character or factor types). You can also specify which columns to make dummies out of, or which columns to ignore. Also creates dummy rows from character, factor, and Date columns. This package provides a significant speed increase from creating dummy variables through model.matrix().
|
2025-04-22 |
|
r-fassets
|
public |
A collection of functions to manage, to investigate and to analyze data sets of financial assets from different points of view.
|
2025-04-22 |
|
r-factoshiny
|
public |
Perform factorial analysis with a menu and draw graphs interactively thanks to 'FactoMineR' and a Shiny application.
|
2025-04-22 |
|
r-factoextra
|
public |
Provides some easy-to-use functions to extract and visualize the output of multivariate data analyses, including 'PCA' (Principal Component Analysis), 'CA' (Correspondence Analysis), 'MCA' (Multiple Correspondence Analysis), 'FAMD' (Factor Analysis of Mixed Data), 'MFA' (Multiple Factor Analysis) and 'HMFA' (Hierarchical Multiple Factor Analysis) functions from different R packages. It contains also functions for simplifying some clustering analysis steps and provides 'ggplot2' - based elegant data visualization.
|
2025-04-22 |
|
r-factoinvestigate
|
public |
Brings a set of tools to help and automatically realise the description of principal component analyses (from 'FactoMineR' functions). Detection of existing outliers, identification of the informative components, graphical views and dimensions description are performed threw dedicated functions. The Investigate() function performs all these functions in one, and returns the result as a report document (Word, PDF or HTML).
|
2025-04-22 |
