|
r-ftsa
|
public |
Functions for visualizing, modeling, forecasting and hypothesis testing of functional time series.
|
2025-03-25 |
|
r-fselector
|
public |
Functions for selecting attributes from a given dataset. Attribute subset selection is the process of identifying and removing as much of the irrelevant and redundant information as possible.
|
2025-03-25 |
|
r-ftextra
|
public |
Build display tables easily by extending the functionality of the 'flextable' package. Features include spanning header, grouping rows, parsing markdown and so on.
|
2025-03-25 |
|
r-frf2.catlg128
|
public |
Catalogues of resolution IV regular fractional factorial designs in 128 runs are provided for up to 33 2-level factors. The catalogues are complete, excluding resolution IV designs without 5-letter words, because these do not add value for a search for unblocked clear designs. The previous package version 1.0 with complete catalogues up to 24 runs (24 runs and a namespace added later) can be downloaded from the authors website.
|
2025-03-25 |
|
r-fsa
|
public |
A variety of simple fish stock assessment methods.
|
2025-03-25 |
|
r-freqtables
|
public |
Quickly make tables of descriptive statistics (i.e., counts, percentages, confidence intervals) for categorical variables. This package is designed to work in a Tidyverse pipeline, and consideration has been given to get results from R to Microsoft Word ® with minimal pain.
|
2025-03-25 |
|
r-frf2
|
public |
Regular and non-regular Fractional Factorial 2-level designs can be created. Furthermore, analysis tools for Fractional Factorial designs with 2-level factors are offered (main effects and interaction plots for all factors simultaneously, cube plot for looking at the simultaneous effects of three factors, full or half normal plot, alias structure in a more readable format than with the built-in function alias).
|
2025-03-25 |
|
r-frequencyconnectedness
|
public |
Accompanies a paper (Barunik, Krehlik (2018) <doi:10.1093/jjfinec/nby001>) dedicated to spectral decomposition of connectedness measures and their interpretation. We implement all the developed estimators as well as the historical counterparts. For more information, see the help or GitHub page (<https://github.com/tomaskrehlik/frequencyConnectedness>) for relevant information.
|
2025-03-25 |
|
r-frequency
|
public |
Generate 'SPSS'/'SAS' styled frequency tables. Frequency tables are generated with variable and value label attributes where applicable with optional html output to quickly examine datasets.
|
2025-03-25 |
|
r-fredr
|
public |
An R client for the 'Federal Reserve Economic Data' ('FRED') API <https://research.stlouisfed.org/docs/api/>. Functions to retrieve economic time series and other data from 'FRED'.
|
2025-03-25 |
|
r-fpp3
|
public |
All data sets required for the examples and exercises in the book "Forecasting: principles and practice" by Rob J Hyndman and George Athanasopoulos <https://OTexts.com/fpp3/>. All packages required to run the examples are also loaded.
|
2025-03-25 |
|
r-fpp2
|
public |
All data sets required for the examples and exercises in the book "Forecasting: principles and practice" (2nd ed, 2018) by Rob J Hyndman and George Athanasopoulos <https://otexts.com/fpp2/>. All packages required to run the examples are also loaded.
|
2025-03-25 |
|
r-fportfolio
|
public |
A collection of functions to optimize portfolios and to analyze them from different points of view.
|
2025-03-25 |
|
r-fpp
|
public |
All data sets required for the examples and exercises in the book "Forecasting: principles and practice" by Rob J Hyndman and George Athanasopoulos. All packages required to run the examples are also loaded.
|
2025-03-25 |
|
r-fossil
|
public |
A set of analytical tools useful in analysing ecological and geographical data sets, both ancient and modern. The package includes functions for estimating species richness (Chao 1 and 2, ACE, ICE, Jacknife), shared species/beta diversity, species area curves and geographic distances and areas.
|
2025-03-25 |
|
r-formatters
|
public |
We provide a framework for rendering complex tables to ASCII, and a set of formatters for transforming values or sets of values into ASCII-ready display strings.
|
2025-03-25 |
|
r-forestmodel
|
public |
Produces forest plots using 'ggplot2' from models produced by functions such as stats::lm(), stats::glm() and survival::coxph().
|
2025-03-25 |
|
r-forestploter
|
public |
Create forest plot based on the layout of the data. Confidence interval in multiple columns by groups can be done easily. Editing plot, inserting/adding text, applying theme to the plot and much more.
|
2025-03-25 |
|
r-forestmangr
|
public |
Processing forest inventory data with methods such as simple random sampling, stratified random sampling and systematic sampling. There are also functions for yield and growth predictions and model fitting, linear and nonlinear grouped data fitting, and statistical tests. References: Kershaw Jr., Ducey, Beers and Husch (2016). <doi:10.1002/9781118902028>.
|
2025-03-25 |
|
r-forecasthybrid
|
public |
Convenient functions for ensemble forecasts in R combining approaches from the 'forecast' package. Forecasts generated from auto.arima(), ets(), thetaf(), nnetar(), stlm(), tbats(), and snaive() can be combined with equal weights, weights based on in-sample errors (introduced by Bates & Granger (1969) <doi:10.1057/jors.1969.103>), or cross-validated weights. Cross validation for time series data with user-supplied models and forecasting functions is also supported to evaluate model accuracy.
|
2025-03-25 |
|
r-forectheta
|
public |
Routines for forecasting univariate time series using Theta Models.
|
2025-03-25 |
|
r-foreca
|
public |
Implementation of Forecastable Component Analysis ('ForeCA'), including main algorithms and auxiliary function (summary, plotting, etc.) to apply 'ForeCA' to multivariate time series data. 'ForeCA' is a novel dimension reduction (DR) technique for temporally dependent signals. Contrary to other popular DR methods, such as 'PCA' or 'ICA', 'ForeCA' takes time dependency explicitly into account and searches for the most ''forecastable'' signal. The measure of forecastability is based on the Shannon entropy of the spectral density of the transformed signal.
|
2025-03-25 |
|
r-fontquiver
|
public |
Provides a set of fonts with permissive licences. This is useful when you want to avoid system fonts to make sure your outputs are reproducible.
|
2025-03-25 |
|
r-foghorn
|
public |
The CRAN check results and where your package stands in the CRAN submission queue in your R terminal.
|
2025-03-25 |
|
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.
|
2025-03-25 |
|
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>.
|
2025-03-25 |
|
r-fma
|
public |
All data sets from "Forecasting: methods and applications" by Makridakis, Wheelwright & Hyndman (Wiley, 3rd ed., 1998) <https://robjhyndman.com/forecasting/>.
|
2025-03-25 |
|
r-flexsurvcure
|
public |
Flexible parametric mixture and non-mixture cure models for time-to-event data.
|
2025-03-25 |
|
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.
|
2025-03-25 |
|
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.
|
2025-03-25 |
|
r-fincal
|
public |
Package for time value of money calculation, time series analysis and computational finance.
|
2025-03-25 |
|
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.
|
2025-03-25 |
|
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'.
|
2025-03-25 |
|
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.
|
2025-03-25 |
|
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.
|
2025-03-25 |
|
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).
|
2025-03-25 |
|
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.
|
2025-03-25 |
|
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>.
|
2025-03-25 |
|
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>.
|
2025-03-25 |
|
r-fastrmodels
|
public |
A data package that hosts all models for the 'nflfastR' package.
|
2025-03-25 |
|
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>.
|
2025-03-25 |
|
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.
|
2025-03-25 |
|
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>.
|
2025-03-25 |
|
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().
|
2025-03-25 |
|
r-fassets
|
public |
A collection of functions to manage, to investigate and to analyze data sets of financial assets from different points of view.
|
2025-03-25 |
|
r-factoshiny
|
public |
Perform factorial analysis with a menu and draw graphs interactively thanks to 'FactoMineR' and a Shiny application.
|
2025-03-25 |
|
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.
|
2025-03-25 |
|
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).
|
2025-03-25 |
|
r-ez
|
public |
Facilitates easy analysis of factorial experiments, including purely within-Ss designs (a.k.a. "repeated measures"), purely between-Ss designs, and mixed within-and-between-Ss designs. The functions in this package aim to provide simple, intuitive and consistent specification of data analysis and visualization. Visualization functions also include design visualization for pre-analysis data auditing, and correlation matrix visualization. Finally, this package includes functions for non-parametric analysis, including permutation tests and bootstrap resampling. The bootstrap function obtains predictions either by cell means or by more advanced/powerful mixed effects models, yielding predictions and confidence intervals that may be easily visualized at any level of the experiment's design.
|
2025-03-25 |
|
r-fabletools
|
public |
Provides tools, helpers and data structures for developing models and time series functions for 'fable' and extension packages. These tools support a consistent and tidy interface for time series modelling and analysis.
|
2025-03-25 |
