|
r-setmethods
|
public |
Functions for performing set-theoretic multi-method research, QCA for clustered data, theory evaluation, Enhanced Standard Analysis, indirect calibration, radar visualisations. Additionally it includes data to replicate the examples in the books by Oana, I.E, C. Q. Schneider, and E. Thomann. Qualitative Comparative Analysis (QCA) using R: A Beginner's Guide. Cambridge University Press and C. Q. Schneider and C. Wagemann "Set Theoretic Methods for the Social Sciences", Cambridge University Press.
|
2025-04-22 |
|
r-set
|
public |
More easy to get intersection, union or complementary set and combinations.
|
2025-04-22 |
|
r-sentimentanalysis
|
public |
Performs a sentiment analysis of textual contents in R. This implementation utilizes various existing dictionaries, such as Harvard IV, or finance-specific dictionaries. Furthermore, it can also create customized dictionaries. The latter uses LASSO regularization as a statistical approach to select relevant terms based on an exogenous response variable.
|
2025-04-22 |
|
r-sentimentr
|
public |
Calculate text polarity sentiment at the sentence level and optionally aggregate by rows or grouping variable(s).
|
2025-04-22 |
|
r-sensominer
|
public |
Statistical Methods to Analyse Sensory Data. SensoMineR: A package for sensory data analysis. S. Le and F. Husson (2008) <DOI:10.1111/j.1745-459X.2007.00137.x>.
|
2025-04-22 |
|
r-sen2r
|
public |
Functions to download Sentinel-2 optical images and perform preliminary processing operations. 'sen2r' provides the instruments required to easily perform (and eventually automate) the steps necessary to build a complete Sentinel-2 processing chain. A Graphical User Interface to facilitate data processing is also provided. For additional documentation refer to the following article: Ranghetti et al. (2020) <doi:10.1016/j.cageo.2020.104473>.
|
2025-04-22 |
|
r-semplot
|
public |
Path diagrams and visual analysis of various SEM packages' output.
|
2025-04-22 |
|
r-semeff
|
public |
Automatically calculate direct, indirect, and total effects for piecewise structural equation models, comprising lists of fitted models representing structured equations (Lefcheck, 2016 <doi:10/f8s8rb>). Confidence intervals are provided via bootstrapping.
|
2025-04-22 |
|
r-seewave
|
public |
Functions for analysing, manipulating, displaying, editing and synthesizing time waves (particularly sound). This package processes time analysis (oscillograms and envelopes), spectral content, resonance quality factor, entropy, cross correlation and autocorrelation, zero-crossing, dominant frequency, analytic signal, frequency coherence, 2D and 3D spectrograms and many other analyses. See Sueur et al. (2008) <doi:10.1080/09524622.2008.9753600> and Sueur (2018) <doi:10.1007/978-3-319-77647-7>.
|
2025-04-22 |
|
r-seastests
|
public |
An overall test for seasonality of a given time series in addition to a set of individual seasonality tests as described by Ollech and Webel (forthcoming): An overall seasonality test. Bundesbank Discussion Paper.
|
2025-04-22 |
|
r-see
|
public |
Provides plotting utilities supporting packages in the 'easystats' ecosystem (<https://github.com/easystats/easystats>) and some extra themes, geoms, and scales for 'ggplot2'. Color scales are based on <https://materialui.co/colors>. References: Lüdecke et al. (2021) <doi:10.21105/joss.03393>.
|
2025-04-22 |
|
r-seasonalview
|
public |
A graphical user interface to the 'seasonal' package and 'X-13ARIMA-SEATS', the U.S. Census Bureau's seasonal adjustment software. Unifies the code base of <http://www.seasonal.website> and the GUI in the 'seasonal' package.
|
2025-04-22 |
|
r-sdprior
|
public |
Utility functions for scale-dependent and alternative hyperpriors. The distribution parameters may capture location, scale, shape, etc. and every parameter may depend on complex additive terms (fixed, random, smooth, spatial, etc.) similar to a generalized additive model. Hyperpriors for all effects can be elicitated within the package. Including complex tensor product interaction terms and variable selection priors. The basic model is explained in in Klein and Kneib (2016) <doi:10.1214/15-BA983>.
|
2025-04-22 |
|
r-sda
|
public |
Provides an efficient framework for high-dimensional linear and diagonal discriminant analysis with variable selection. The classifier is trained using James-Stein-type shrinkage estimators and predictor variables are ranked using correlation-adjusted t-scores (CAT scores). Variable selection error is controlled using false non-discovery rates or higher criticism.
|
2025-04-22 |
|
r-sdamr
|
public |
Data sets and functions to support the books "Statistics: Data analysis and modelling" by Speekenbrink, M. (2021) <https://mspeekenbrink.github.io/sdam-book/> and "An R companion to Statistics: data analysis and modelling" by Speekenbrink, M. (2021) <https://mspeekenbrink.github.io/sdam-r-companion/>. All datasets analysed in these books are provided in this package. In addition, the package provides functions to compute sample statistics (variance, standard deviation, mode), create raincloud and enhanced Q-Q plots, and expand Anova results into omnibus tests and tests of individual contrasts.
|
2025-04-22 |
|
r-sdmpredictors
|
public |
Terrestrial and marine predictors for species distribution modelling from multiple sources, including WorldClim <https://www.worldclim.org/>,, ENVIREM <https://envirem.github.io/>, Bio-ORACLE <https://bio-oracle.org/> and MARSPEC <http://www.marspec.org/>.
|
2025-04-22 |
|
r-scpubr
|
public |
A system that provides a streamlined way of generating publication ready plots for known Single-Cell transcriptomics data in a “publication ready” format. This is, the goal is to automatically generate plots with the highest quality possible, that can be used right away or with minimal modifications for a research article.
|
2025-04-22 |
|
r-scoringutils
|
public |
Provides a collection of metrics and proper scoring rules (Tilmann Gneiting & Adrian E Raftery (2007) <doi:10.1198/016214506000001437>, Jordan, A., Krüger, F., & Lerch, S. (2019) <doi:10.18637/jss.v090.i12>) within a consistent framework for evaluation, comparison and visualisation of forecasts. In addition to proper scoring rules, functions are provided to assess bias, sharpness and calibration (Sebastian Funk, Anton Camacho, Adam J. Kucharski, Rachel Lowe, Rosalind M. Eggo, W. John Edmunds (2019) <doi:10.1371/journal.pcbi.1006785>) of forecasts. Several types of predictions (e.g. binary, discrete, continuous) which may come in different formats (e.g. forecasts represented by predictive samples or by quantiles of the predictive distribution) can be evaluated. Scoring metrics can be used either through a convenient data.frame format, or can be applied as individual functions in a vector / matrix format. All functionality has been implemented with a focus on performance and is robustly tested.
|
2025-04-22 |
|
r-scorecard
|
public |
The `scorecard` package makes the development of credit risk scorecard easier and efficient by providing functions for some common tasks, such as data partition, variable selection, woe binning, scorecard scaling, performance evaluation and report generation. These functions can also used in the development of machine learning models. The references including: 1. Refaat, M. (2011, ISBN: 9781447511199). Credit Risk Scorecard: Development and Implementation Using SAS. 2. Siddiqi, N. (2006, ISBN: 9780471754510). Credit risk scorecards. Developing and Implementing Intelligent Credit Scoring.
|
2025-04-22 |
|
r-scholar
|
public |
Provides functions to extract citation data from Google Scholar. Convenience functions are also provided for comparing multiple scholars and predicting future h-index values.
|
2025-04-22 |
|
r-sccustomize
|
public |
Collection of functions created and/or curated to aid in the visualization and analysis of single-cell data using 'R'. 'scCustomize' aims to provide 1) Customized visualizations for aid in ease of use and to create more aesthetic and functional visuals. 2) Improve speed/reproducibility of common tasks/pieces of code in scRNA-seq analysis with a single or group of functions. For citation please use: Marsh SE (2021) "Custom Visualizations & Functions for Streamlined Analyses of Single Cell Sequencing" <doi:10.5281/zenodo.5706430>.
|
2025-04-22 |
|
r-scatterpie
|
public |
Creates scatterpie plots, especially useful for plotting pies on a map.
|
2025-04-22 |
|
r-saslm
|
public |
This is a core implementation of 'SAS' procedures for linear models - GLM, REG, ANOVA, TTEST, FREQ, and UNIVARIATE. Some R packages provide type II and type III SS. However, the results of nested and complex designs are often different from those of 'SAS.' Different results does not necessarily mean incorrectness. However, many wants the same results to SAS. This package aims to achieve that. Reference: Littell RC, Stroup WW, Freund RJ (2002, ISBN:0-471-22174-0).
|
2025-04-22 |
|
r-sbw
|
public |
Implements the Stable Balancing Weights by Zubizarreta (2015) <DOI:10.1080/01621459.2015.1023805>. These are the weights of minimum variance that approximately balance the empirical distribution of the observed covariates. For an overview, see Chattopadhyay, Hase and Zubizarreta (2020) <DOI:10.1002/(ISSN)1097-0258>. To solve the optimization problem in 'sbw', the default solver is 'quadprog', which is readily available through CRAN. The solver 'osqp' is also posted on CRAN. To enhance the performance of 'sbw', users are encouraged to install other solvers such as 'gurobi' and 'Rmosek', which require special installation. For the installation of gurobi and pogs, please follow the instructions at <https://www.gurobi.com/documentation/9.1/quickstart_mac/r_ins_the_r_package.html> and <http://foges.github.io/pogs/stp/r>.
|
2025-04-22 |
|
r-samplingbook
|
public |
Sampling procedures from the book 'Stichproben - Methoden und praktische Umsetzung mit R' by Goeran Kauermann and Helmut Kuechenhoff (2010).
|
2025-04-22 |
