|
r-vim
|
public |
New tools for the visualization of missing and/or imputed values are introduced, which can be used for exploring the data and the structure of the missing and/or imputed values. Depending on this structure of the missing values, the corresponding methods may help to identify the mechanism generating the missing values and allows to explore the data including missing values. In addition, the quality of imputation can be visually explored using various univariate, bivariate, multiple and multivariate plot methods. A graphical user interface available in the separate package VIMGUI allows an easy handling of the implemented plot methods.
|
2025-03-25 |
|
r-smooth
|
public |
Functions implementing Single Source of Error state space models for purposes of time series analysis and forecasting. The package includes Exponential Smoothing, SARIMA, Complex Exponential Smoothing, Simple Moving Average, Vector Exponential Smoothing in state space forms, several simulation functions and intermittent demand state space models.
|
2025-03-25 |
|
r-miceadds
|
public |
Contains functions for multiple imputation which complements existing functionality in R. In particular, several imputation methods for the mice package (van Buuren & Groothuis-Oudshoorn, 2011, <doi:10.18637/jss.v045.i03>) are included. Main features of the miceadds package include plausible value imputation (Mislevy, 1991, <doi:10.1007/BF02294457>), multilevel imputation for variables at any level or with any number of hierarchical and non-hierarchical levels (Grund, Luedtke & Robitzsch, 2018, <doi:10.1177/1094428117703686>; van Buuren, 2018, Ch.7, <doi:10.1201/9780429492259>), imputation using partial least squares (PLS) for high dimensional predictors (Robitzsch, Pham & Yanagida, 2016), nested multiple imputation (Rubin, 2003, <doi:10.1111/1467-9574.00217>) and substantive model compatible imputation (Bartlett et al., 2015, <doi:10.1177/0962280214521348>).
|
2025-03-25 |
|
r-logistf
|
public |
Fit a logistic regression model using Firth's bias reduction method, equivalent to penalization of the log-likelihood by the Jeffreys prior. Confidence intervals for regression coefficients can be computed by penalized profile likelihood. Firth's method was proposed as ideal solution to the problem of separation in logistic regression. If needed, the bias reduction can be turned off such that ordinary maximum likelihood logistic regression is obtained.
|
2025-03-25 |
|
r-dendser
|
public |
Re-arranges a dendrogram to optimize visualisation-based cost functions
|
2025-03-25 |
|
r-widyr
|
public |
Encapsulates the pattern of untidying data into a wide matrix, performing some processing, then turning it back into a tidy form. This is useful for several operations such as co-occurrence counts, correlations, or clustering that are mathematically convenient on wide matrices.
|
2025-03-25 |
|
r-varimp
|
public |
Computes the random forest variable importance (VIMP) for the conditional inference random forest (cforest) of the 'party' package. Includes a function (varImp) that computes the VIMP for arbitrary measures from the 'measures' package. For calculating the VIMP regarding the measures accuracy and AUC two extra functions exist (varImpACC and varImpAUC).
|
2025-03-25 |
|
r-usingr
|
public |
A collection of data sets to accompany the textbook "Using R for Introductory Statistics," second edition.
|
2025-03-25 |
|
r-tsoutliers
|
public |
Detection of outliers in time series following the Chen and Liu (1993) <DOI:10.2307/2290724> procedure. Innovational outliers, additive outliers, level shifts, temporary changes and seasonal level shifts are considered.
|
2025-03-25 |
|
r-tables
|
public |
Computes and displays complex tables of summary statistics. Output may be in LaTeX, HTML, plain text, or an R matrix for further processing.
|
2025-03-25 |
|
r-statsr
|
public |
Provides functions and datasets to support inference with the open access book "An Introduction to Bayesian Thinking", available online <https://statswithr.github.io/book> and online videos for the "Statistics with R Specialization" <https://www.coursera.org/specializations/statistics>. which includes an introduction to Bayesian inference and decision making for one and two sample credible intervals and hypothesis testing for Gaussian and Binomial data, in addition to frequentist inference using model-based and randomization-based methods. To help with understanding concepts, 'shiny' applications are used to aide visualization of sampling distributions, credible intervals, hypothesis testing, Lindley's and Bartlett's paradoxes. For development versions or to report issues, please visit <https://github.com/StatsWithR/statsr>.
|
2025-03-25 |
|
r-sjmisc
|
public |
Collection of miscellaneous utility functions, supporting data transformation tasks like recoding, dichotomizing or grouping variables, setting and replacing missing values. The data transformation functions also support labelled data, and all integrate seamlessly into a 'tidyverse'-workflow.
|
2025-03-25 |
|
r-questionr
|
public |
Set of functions to make the processing and analysis of surveys easier : interactive shiny apps and addins for data recoding, contingency tables, dataset metadata handling, and several convenience functions.
|
2025-03-25 |
|
r-naniar
|
public |
Missing values are ubiquitous in data and need to be explored and handled in the initial stages of analysis. 'naniar' provides data structures and functions that facilitate the plotting of missing values and examination of imputations. This allows missing data dependencies to be explored with minimal deviation from the common work patterns of 'ggplot2' and tidy data.
|
2025-03-25 |
|
r-modelr
|
public |
Functions for modelling that help you seamlessly integrate modelling into a pipeline of data manipulation and visualisation.
|
2025-03-25 |
|
r-mediation
|
public |
We implement parametric and non parametric mediation analysis. This package performs the methods and suggestions in Imai, Keele and Yamamoto (2010) <DOI:10.1214/10-STS321>, Imai, Keele and Tingley (2010) <DOI:10.1037/a0020761>, Imai, Tingley and Yamamoto (2013) <DOI:10.1111/j.1467-985X.2012.01032.x>, Imai and Yamamoto (2013) <DOI:10.1093/pan/mps040> and Yamamoto (2013) <http://web.mit.edu/teppei/www/research/IVmediate.pdf>. In addition to the estimation of causal mediation effects, the software also allows researchers to conduct sensitivity analysis for certain parametric models.
|
2025-03-25 |
|
r-mcomp
|
public |
The 1001 time series from the M-competition (Makridakis et al. 1982) <DOI:10.1002/for.3980010202> and the 3003 time series from the IJF-M3 competition (Makridakis and Hibon, 2000) <DOI:10.1016/S0169-2070(00)00057-1>.
|
2025-03-25 |
|
r-lexicon
|
public |
A collection of lexical hash tables, dictionaries, and word lists.
|
2025-03-25 |
|
r-fma
|
public |
All data sets from "Forecasting: methods and applications" by Makridakis, Wheelwright & Hyndman (Wiley, 3rd ed., 1998).
|
2025-03-25 |
|
r-expsmooth
|
public |
Data sets from the book "Forecasting with exponential smoothing: the state space approach" by Hyndman, Koehler, Ord and Snyder (Springer, 2008).
|
2025-03-25 |
|
r-editdata
|
public |
An 'RStudio' addin for editing a 'data.frame' or a 'tibble'. You can delete, add or update a 'data.frame' without coding. You can get resultant data as a 'data.frame'. In the package, modularized 'shiny' app codes are provided. These modules are intended for reuse across applications.
|
2025-03-25 |
|
r-dotwhisker
|
public |
Quick and easy dot-and-whisker plots of regression results.
|
2025-03-25 |
|
r-data.tree
|
public |
Create tree structures from hierarchical data, and traverse the tree in various orders. Aggregate, cumulate, print, plot, convert to and from data.frame and more. Useful for decision trees, machine learning, finance, conversion from and to JSON, and many other applications.
|
2025-03-25 |
|
r-d3heatmap
|
public |
Create interactive heat maps that are usable from the R console, in the 'RStudio' viewer pane, in 'R Markdown' documents, and in 'Shiny' apps. Hover the mouse pointer over a cell to show details, drag a rectangle to zoom, and click row/column labels to highlight.
|
2025-03-25 |
|
r-car
|
public |
Functions to Accompany J. Fox and S. Weisberg, An R Companion to Applied Regression, Third Edition, Sage, in press.
|
2025-03-25 |
