We provide functions to perform taxometric analyses. This package contains 46 functions, but only 5 should be called directly by users. CheckData() should be run prior to any taxometric analysis to ensure that the data are appropriate for taxometric analysis. RunTaxometrics() performs taxometric analyses for a sample of data. RunCCFIProfile() performs a series of taxometric analyses to generate a CCFI profile. CreateData() generates a sample of categorical or dimensional data. ClassifyCases() assigns cases to groups using the base-rate classification method.

This package provides the Book-Crossing Dataset for the package recommenderlab.

Helps to fit thermal performance curves (TPCs). rTPC contains 49 model formulations previously used to fit TPCs and has helper functions to set sensible start parameters, upper and lower parameter limits and estimate parameters useful in downstream analyses, such as cardinal temperatures, maximum rate and optimum temperature. See Padfield et al. (2021) <doi:10.1111/2041-210X.13585>.

BaseX <https://basex.org> is a XML database engine and a compliant XQuery 3.1 processor with full support of W3C Update Facility'. This package is a full client-implementation of the client/server protocol for BaseX and provides functionalities to create, manipulate and query on XML-data.

Slow Feature Analysis (SFA), ported to R based on matlab implementations of SFA: SFA toolkit 1.0 by Pietro Berkes and SFA toolkit 2.8 by Wolfgang Konen.

Computes attributable effects based confidence interval, permutation test confidence interval, or asymptotic confidence interval for the average treatment effect on a binary outcome. Methods outlined in further detail in Rigdon and Hudgens (2015) <doi:10.1002/sim.6384>.

Makes easier the creation of R package or research compendium (i.e. a predefined files/folders structure) so that users can focus on the code/analysis instead of wasting time organizing files. A full ready-to-work structure is set up with some additional features: version control, remote repository creation, CI/CD configuration (check package integrity under several OS, test code with testthat', and build and deploy website using pkgdown'). This package heavily relies on the R packages devtools and usethis and follows recommendations made by Wickham H. (2015) <ISBN:9781491910597> and Marwick B. et al. (2018) <doi:10.7287/peerj.preprints.3192v2>.

This package provides functions to generate response-surface designs, fit first- and second-order response-surface models, make surface plots, obtain the path of steepest ascent, and do canonical analysis. A good reference on these methods is Chapter 10 of Wu, C-F J and Hamada, M (2009) "Experiments: Planning, Analysis, and Parameter Design Optimization" ISBN 978-0-471-69946-0. An early version of the package is documented in Journal of Statistical Software <doi:10.18637/jss.v032.i07>.

Estimate significance of importance metrics for a Random Forest model by permuting the response variable. Produces null distribution of importance metrics for each predictor variable and p-value of observed. Provides summary and visualization functions for randomForest results.

Three methods to calculate R2 for models with correlated errors, including Phylogenetic GLS, Phylogenetic Logistic Regression, Linear Mixed Models (LMMs), and Generalized Linear Mixed Models (GLMMs). See details in Ives 2018 <doi:10.1093/sysbio/syy060>.

Implementation of a model-based clustering algorithm for ranking data (C. Biernacki, J. Jacques (2013) <doi:10.1016/j.csda.2012.08.008>). Multivariate rankings as well as partial rankings are taken into account. This algorithm is based on an extension of the Insertion Sorting Rank (ISR) model for ranking data, which is a meaningful and effective model parametrized by a position parameter (the modal ranking, quoted by mu) and a dispersion parameter (quoted by pi). The heterogeneity of the rank population is modelled by a mixture of ISR, whereas conditional independence assumption is considered for multivariate rankings.

Implementation of robust sparse PCA using the ROSPCA algorithm of Hubert et al. (2016) <DOI:10.1080/00401706.2015.1093962>.

Random number generation library implemented in C with multiple engines and distribution functions, providing an R interface focused on correctness, speed, and reproducibility. Supports various PRNGs including xoshiro256++/**, PCG64, Philox, and ChaCha20, with methods for continuous, discrete, and multivariate distributions.

This package provides C++ header files to deal with color conversion from some color spaces to hexadecimal with Rcpp', and exports some color mapping functions for usage in R. Also exports functions to convert colors from the HSLuv color space for usage in R. HSLuv is a human-friendly alternative to HSL.

Download and handle spatial and temporal data from the CAMELS-CL dataset (Catchment Attributes and Meteorology for Large Sample Studies, Chile) <https://camels.cr2.cl/>, developed by Alvarez-Garreton et al. (2018) <doi:10.5194/hess-22-5817-2018>. The package does not generate new data, it only facilitates direct access to the original dataset for hydrological analyses.

This package contains tools for reading and writing data from or to files in the formats: akterm, dmna, Scintec Format-1, and Campbell Scientific TOA5.

Compute the values of various parameters evaluating how similar two multidimensional datasets structures are in multidimensional space, as described in: Jouan-Rimbaud, D., Massart, D. L., Saby, C. A., Puel, C. (1998), <doi:10.1016/S0169-7439(98)00005-7>. The computed parameters evaluate three properties, namely, the direction of the data sets, the variance-covariance of the data points, and the location of the data sets centroids. The package contains workhorse function jrparams(), as well as two helper functions Mboxtest() and JRsMahaldist(), and four example data sets.

Aims at loading Criteo online advertising campaign data into R. Criteo <http://www.criteo.com/> is an online advertising service that enables advertisers to display commercial ads to web users. The package provides an authentication process for R with the Criteo API <http://kb.criteo.com/ advertising/content/5/27/en/api.html>. Moreover, the package features an interface to query campaign data from the Criteo API. The data can be downloaded and will be transformed into a R data frame.

Creation, manipulation, simulation of linear Gaussian Bayesian networks from text files and more...

The Reproducible Open Coding Kit ('ROCK', and this package, rock') was developed to facilitate reproducible and open coding, specifically geared towards qualitative research methods. It was developed to be both human- and machine-readable, in the spirit of MarkDown and YAML'. The idea is that this makes it relatively easy to write other functions and packages to process ROCK files. The rock package contains functions for basic coding and analysis, such as collecting and showing coded fragments and prettifying sources, as well as a number of advanced analyses such as the Qualitative Network Approach and Qualitative/Unified Exploration of State Transitions. The ROCK and this rock package are described in the ROCK book (ZörgÅ & Peters, 2022; <https://rockbook.org>), in ZörgÅ & Peters (2024) <doi:10.1080/21642850.2022.2119144> and Peters, ZörgÅ and van der Maas (2022) <doi:10.31234/osf.io/cvf52>, and more information and tutorials are available at <https://rock.science>.

This package implements the regularized exponentially tilted empirical likelihood method. Details of the method are given in Kim, MacEachern, and Peruggia (2023) <doi:10.48550/arXiv.2312.17015>. This work was supported by the U.S. National Science Foundation under Grants No. SES-1921523 and DMS-2015552.

Root Expected Proportion Squared Difference (REPSD) is a nonparametric differential item functioning (DIF) method that (a) allows practitioners to explore for DIF related to small, fine-grained focal groups of examinees, and (b) compares the focal group directly to the composite group that will be used to develop the reported test score scale. Using your provided response matrix with a column that identifies focal group membership, this package provides the REPSD values, a simulated null distribution of possible REPSD values, and the simulated p-values identifying items possibly displaying DIF without requiring enormous sample sizes.

This package provides an easy way to report the results of ROC analysis, including: 1. an ROC curve. 2. the value of Cutoff, AUC (Area Under Curve), ACC (accuracy), SEN (sensitivity), SPE (specificity), PLR (positive likelihood ratio), NLR (negative likelihood ratio), PPV (positive predictive value), NPV (negative predictive value), PPA (percentage of positive accordance), NPA (percentage of negative accordance), TPA (percentage of total accordance), KAPPA (kappa value).

This package provides methods for downloading and processing data and metadata from Kolada', the official Swedish regions and municipalities database <https://www.kolada.se/>.