This package performs regularization of differential item functioning (DIF) parameters in item response theory (IRT) models (Belzak & Bauer, 2020) <https://pubmed.ncbi.nlm.nih.gov/31916799/> using a penalized expectation-maximization algorithm.

Perform wavelet analysis (orthogonal,translation invariant, tensorial, 1-2-3d transforms, thresholding, block thresholding, linear,...) with applications to data compression or denoising/regression. The core of the code is a port of MATLAB Wavelab toolbox written by D. Donoho, A. Maleki and M. Shahram (<https://statweb.stanford.edu/~wavelab/>).

The regression discontinuity (RD) design is a popular quasi-experimental design for causal inference and policy evaluation. Under the local randomization approach, RD designs can be interpreted as randomized experiments inside a window around the cutoff. This package provides tools to perform randomization inference for RD designs under local randomization: rdrandinf() to perform hypothesis testing using randomization inference, rdwinselect() to select a window around the cutoff in which randomization is likely to hold, rdsensitivity() to assess the sensitivity of the results to different window lengths and null hypotheses and rdrbounds() to construct Rosenbaum bounds for sensitivity to unobserved confounders. See Cattaneo, Titiunik and Vazquez-Bare (2016) <https://rdpackages.github.io/references/Cattaneo-Titiunik-VazquezBare_2016_Stata.pdf> for further methodological details.

Some heavily used base R functions are reconstructed to also be compliant to data.table objects. Also, some general helper functions that could be of interest for working with data.table objects are included.

This package provides functions to deal with matrix algebra for matrices with rational entries: determinant, rank, image and kernel, inverse, Cholesky decomposition. All computations are exact.

Load data by campaigns, ads, ad sets and insights, ad account and business manager from Facebook Marketing API into R. For more details see official documents by Facebook Marketing API <https://developers.facebook.com/docs/marketing-api>.

This package provides functions and examples for testing hypothesis about the population mean and variance on samples drawn by r-size biased sampling schemes.

Testing the equality of two means using Ranked Set Sampling and Median Ranked Set Sampling are provided under normal distribution. Data generation functions are also given RSS and MRSS. Also, data generation functions are given under imperfect ranking data for Ranked Set Sampling and Median Ranked Set Sampling. Ozdemir Y.A., Ebegil M., & Gokpinar F. (2019), <doi:10.1007/s40995-018-0558-0> Ozdemir Y.A., Ebegil M., & Gokpinar F. (2017), <doi:10.1080/03610918.2016.1263736>.

Generate random user data from the Random User Generator API. For more information, see <https://randomuser.me/>.

Rcmdr GUI extension plug-in for Receiver Operator Characteristic tools from pROC package. Also it ads a Rcmdr GUI extension for Hosmer and Lemeshow GOF test from the package ResourceSelection.

Exports an Rcpp interface for the Bessel functions in the Bessel package, which can then be called from the C++ code of other packages. For the original Fortran implementation of these functions see Amos (1995) <doi:10.1145/212066.212078>.

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 implements techniques for educational resource inspection, selection, and evaluation (RISE) described in Bodily, Nyland, and Wiley (2017) <doi:10.19173/irrodl.v18i2.2952>. Automates the process of identifying learning materials that are not effectively supporting student learning in technology-mediated courses by synthesizing information about access to course content and performance on assessments.

Assessing and comparing risk prediction rules for clustered data. The method is based on the paper: Rosner B, Qiu W, and Lee MLT.(2013) <doi: 10.1007/s10985-012-9240-6>.

Decimal rounding is non-trivial in binary arithmetic. ISO standard round to even is more rare than typically assumed as most decimal fractions are not exactly representable in binary. Our roundX() versions explore differences between current and potential future versions of round() in R. Further, provides (some partly related) C99 math lib functions not in base R.

R access to the Sequential Monte Carlo Template Classes by Johansen <doi:10.18637/jss.v030.i06> is provided. At present, four additional examples have been added, and the first example from the JSS paper has been extended. Further integration and extensions are planned.

This package provides tools to read various file types into one list of data structures, usually, but not limited to, data frames. Excel files are read sheet-wise, i.e., all or a selection of sheets can be read. Field delimiters and decimal separators are determined automatically.

Enables researchers to sample redistricting plans from a pre-specified target distribution using Sequential Monte Carlo and Markov Chain Monte Carlo algorithms. The package allows for the implementation of various constraints in the redistricting process such as geographic compactness and population parity requirements. Tools for analysis such as computation of various summary statistics and plotting functionality are also included. The package implements the SMC algorithm of McCartan and Imai (2023) <doi:10.1214/23-AOAS1763>, the enumeration algorithm of Fifield, Imai, Kawahara, and Kenny (2020) <doi:10.1080/2330443X.2020.1791773>, the Flip MCMC algorithm of Fifield, Higgins, Imai and Tarr (2020) <doi:10.1080/10618600.2020.1739532>, the Merge-split/Recombination algorithms of Carter et al. (2019) <doi:10.48550/arXiv.1911.01503> and DeFord et al. (2021) <doi:10.1162/99608f92.eb30390f>, and the Short-burst optimization algorithm of Cannon et al. (2020) <doi:10.48550/arXiv.2011.02288>.

This package provides a collection of tools for measuring the similarity of text messages and tracing the flow of messages over time and across media.

Estimates and plots as a heat map the rolling window wavelet correlation (RWWC) coefficients statistically significant (within the 95% CI) between two regular (evenly spaced) time series. RolWinWavCor also plots at the same graphic the time series under study. The RolWinWavCor was designed for financial time series, but this software can be used with other kinds of data (e.g., climatic, ecological, geological, etc). The functions contained in RolWinWavCor are highly flexible since these contains some parameters to personalize the time series under analysis and the heat maps of the rolling window wavelet correlation coefficients. Moreover, we have also included a data set (named EU_stock_markets) that contains nine European stock market indices to exemplify the use of the functions contained in RolWinWavCor'. Methods derived from Polanco-Martà nez et al (2018) <doi:10.1016/j.physa.2017.08.065>).

Robust Estimation of Variance Component Models by classic and composite robust procedures. The composite procedures are robust against outliers generated by the Independent Contamination Model.

Enhances the R Optimization Infrastructure ('ROI') package with the NLopt solver for solving nonlinear optimization problems.

This package provides an intuitive and user-friendly interface for working with emojis in R'. It allows users to search, insert, and manage emojis by keyword, category, or through an interactive shiny'-based drop-down. The package enables integration of emojis into R scripts, R Markdown', Quarto', shiny apps, and ggplot2 plots. Also includes built-in mappings for commit messages, useful for version control. It builds on established emoji libraries and Unicode standards, adding expressiveness and visual cues to documentation, user interfaces, and reports. For more details see Emojipedia (2024) <https://emojipedia.org> and GitHub Emoji Cheat Sheet <https://github.com/ikatyang/emoji-cheat-sheet/tree/master>.

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.