The rankFD() function calculates the Wald-type statistic (WTS) and the ANOVA-type statistic (ATS) for nonparametric factorial designs, e.g., for count, ordinal or score data in a crossed design with an arbitrary number of factors. Brunner, E., Bathke, A. and Konietschke, F. (2018) <doi:10.1007/978-3-030-02914-2>.

As an advanced approach to computerized adaptive testing (CAT), shadow testing (van der Linden(2005) <doi:10.1007/0-387-29054-0>) dynamically assembles entire shadow tests as a part of selecting items throughout the testing process. Selecting items from shadow tests guarantees the compliance of all content constraints defined by the blueprint. RSCAT is an R package for the shadow-test approach to CAT. The objective of RSCAT is twofold: 1) Enhancing the effectiveness of shadow-test CAT simulation; 2) Contributing to the academic and scientific community for CAT research. RSCAT is currently designed for dichotomous items based on the three-parameter logistic (3PL) model.

Applies quality control to daily precipitation observations; reconstructs the original series by estimating precipitation in missing values; and creates gridded datasets of daily precipitation.

Insert/extract text "reminders" into/from function source code comments or as the "comment" attribute of any object. The former can be handy in development as reminders of e.g. argument requirements, expected objects in the calling environment, required options settings, etc. The latter can be used to provide information of the object and as simple manual "tooltips" for users, among other things.

We implement causal mediation analysis using the methods proposed by Hong (2010) and Hong, Deutsch & Hill (2015) <doi:10.3102/1076998615583902>. It allows the estimation and hypothesis testing of causal mediation effects through ratio of mediator probability weights (RMPW). This strategy conveniently relaxes the assumption of no treatment-by-mediator interaction while greatly simplifying the outcome model specification without invoking strong distributional assumptions. We also implement a sensitivity analysis by extending the RMPW method to assess potential bias in the presence of omitted pretreatment or posttreatment covariates. The sensitivity analysis strategy was proposed by Hong, Qin, and Yang (2018) <doi:10.3102/1076998617749561>.

Rogue ("wildcard") taxa are leaves with uncertain phylogenetic position. Their position may vary from tree to tree under inference methods that yield a tree set (e.g. bootstrapping, Bayesian tree searches, maximum parsimony). The presence of rogue taxa in a tree set can potentially remove all information from a consensus tree. The information content of a consensus tree - a function of its resolution and branch support values - can often be increased by removing rogue taxa. Rogue provides an explicitly information-theoretic approach to rogue detection (Smith 2022) <doi:10.1093/sysbio/syab099>, and an interface to RogueNaRok (Aberer et al. 2013) <doi:10.1093/sysbio/sys078>.

Reconstructs retinae by morphing a flat surface with cuts (a dissected flat-mount retina) onto a curvilinear surface (the standard retinal shape). It can estimate the position of a point on the intact adult retina to within 8 degrees of arc (3.6% of nasotemporal axis). The coordinates in reconstructed retinae can be transformed to visuotopic coordinates. For more details see Sterratt, D. C., Lyngholm, D., Willshaw, D. J. and Thompson, I. D. (2013) <doi:10.1371/journal.pcbi.1002921>.

We rewrite of RAMpath software developed by John McArdle and Steven Boker as an R package. In addition to performing regular SEM analysis through the R package lavaan, RAMpath has unique features. First, it can generate path diagrams according to a given model. Second, it can display path tracing rules through path diagrams and decompose total effects into their respective direct and indirect effects as well as decompose variance and covariance into individual bridges. Furthermore, RAMpath can fit dynamic system models automatically based on latent change scores and generate vector field plots based upon results obtained from a bivariate dynamic system. Starting version 0.4, RAMpath can conduct power analysis for both univariate and bivariate latent change score models.

Use this package to identify indicators of transparency within the published literature. It can identify and extract text related to indicators of transparency from specifically formatted TXT files and from PMC XML files (i.e. XML files downloaded from the PubMed Central). It builds on the original rtransparent tool of Serghiou et al. (2021) <doi:10.1371/journal.pbio.3001107>.

This package provides functionality to interact with the FieldClimate API <https://api.fieldclimate.com/v2/docs/>.

R Markdown output formats based on JavaScript libraries such as Scrollama (<https://github.com/russellsamora/scrollama>) for storytelling.

Fetches NCBI data (RefSeq <https://www.ncbi.nlm.nih.gov/refseq/> database) and provides an environment to extract information at the level of gene, mRNA or protein accessions.

Electrical properties of resistor networks using matrix methods.

This package provides robust parameter tuning and model training for predictive models applied across data sources where the data distribution varies slightly from source to source. This package implements three primary tuning methods: cross-validation-based internal tuning, external tuning, and the RobustTuneC method. External tuning includes a conservative option where parameters are tuned internally on the training data and validating on an external dataset, providing a slightly pessimistic estimate. It supports Lasso, Ridge, Random Forest, Boosting, and Support Vector Machine classifiers. Currently, only binary classification is supported. The response variable must be the first column of the dataset and a factor with exactly two levels. The tuning methods are based on the paper by Nicole Ellenbach, Anne-Laure Boulesteix, Bernd Bischl, Kristian Unger, and Roman Hornung (2021) "Improved Outcome Prediction Across Data Sources Through Robust Parameter Tuning" <doi:10.1007/s00357-020-09368-z>.

Optimize and compress local and online image files with the reSmush.it API <https://resmush.it/api/>. Process individual files or entire directories. The API is free for personal use, accepts files smaller than 5 MB and supports PNG', JPEG', GIF', BMP and TIFF files.

An R implementation of the Reinert text clustering method. For more details about the algorithm see the included vignettes or Reinert (1990) <doi:10.1177/075910639002600103>.

An EM algorithm to fit Mallows Models to full or partial rankings, with or without ties. Based on Adkins and Flinger (1998) <doi:10.1080/03610929808832223>.

Upload R data.frame to Arm Treasure Data, see <https://www.treasuredata.com/>. You can execute database or table handling for resources on Arm Treasure Data.

Downloads Southern Oscillation Index, Oceanic Nino Index, North Pacific Gyre Oscillation data, North Atlantic Oscillation and Arctic Oscillation. Data sources are described in the help files for each function.

An implementation of simulated maximum likelihood method for the estimation of Binary (Probit and Logit), Ordered (Probit and Logit) and Poisson models with random parameters for cross-sectional and longitudinal data as presented in Sarrias (2016) <doi:10.18637/jss.v074.i10>.

This package performs goodness-of-fit tests for capture-recapture models as described by Gimenez et al. (2018) <doi:10.1111/2041-210X.13014>. Also contains several functions to process capture-recapture data.

Set of tools to manipulate the JDemetra+ workspaces. Based on the RJDemetra package (which interfaces with version 2 of the JDemetra+ (<https://github.com/jdemetra/jdemetra-app>), the seasonal adjustment software officially recommended to the members of the European Statistical System (ESS) and the European System of Central Banks). This package provides access to additional workspace manipulation functions such as metadata manipulation, raw paths and wrangling of several workspaces simultaneously. These additional functionalities are useful as part of a CVS data production chain.

Fits models for Rasch Measurement Theory, whose defining measurement properties include sufficiency and invariance. Available models include the dichotomous Rasch, partial credit, rating scale, many-facet, extended frame of reference and explanatory models. Explanatory modelling supports predictors at the item and threshold levels. Comparative judgement models are available for dichotomous and ordered pairwise responses, with support for extended frames of reference and explanatory predictors. Functions support estimation and examination of model fit, targeting, reliability, dimensionality, local dependence, differential item functioning, equating and simulation. A graphical interface for fitting models and examining results is provided through an interactive shiny application.

This package provides functionality for carrying out estimation with data collected using Respondent-Driven Sampling. This includes Heckathorn's RDS-I and RDS-II estimators as well as Gile's Sequential Sampling estimator. The package is part of the "RDS Analyst" suite of packages for the analysis of respondent-driven sampling data. See Gile and Handcock (2010) <doi:10.1111/j.1467-9531.2010.01223.x>, Gile and Handcock (2015) <doi:10.1111/rssa.12091> and Gile, Beaudry, Handcock and Ott (2018) <doi:10.1146/annurev-statistics-031017-100704>.