This package provides permutation methods for testing in high-dimensional linear models. The tests are often robust against heteroscedasticity and non-normality and usually perform well under anti-sparsity. See Hemerik, Thoresen and Finos (2021) <doi:10.1080/00949655.2020.1836183>.

Fitting and testing probabilistic knowledge structures, especially the basic local independence model (BLIM, Doignon & Flamagne, 1999) and the simple learning model (SLM), using the minimum discrepancy maximum likelihood (MDML) method (Heller & Wickelmaier, 2013 <doi:10.1016/j.endm.2013.05.145>).

Given k populations (can be in thousands), what is the probability that a given subset of size t contains the true top t populations? This package finds this probability and offers three tuning parameters (G, d, L) to relax the definition.

Interfaces with the Hugging Face tokenizers library to provide implementations of today's most used tokenizers such as the Byte-Pair Encoding algorithm <https://huggingface.co/docs/tokenizers/index>. It's extremely fast for both training new vocabularies and tokenizing texts.

Statistical estimation of revealed preference models from data collected on bipartite matchings. The models are for matchings within a bipartite population where individuals have utility for people based on known and unknown characteristics. People can form a partnership or remain unpartnered. The model represents both the availability of potential partners of different types and preferences of individuals for such people. The software estimates preference parameters based on sample survey data on partnerships and population composition. The simulation of matchings and goodness-of-fit are considered. See Goyal, Handcock, Jackson, Rendall and Yeung (2022) <doi:10.1093/jrsssa/qnad031>.

This package provides a modified implementation of stepwise regression that greedily searches the space of interactions among features in order to build polynomial regression models. Furthermore, the hypothesis tests conducted are valid-post model selection due to the use of a revisiting procedure that implements an alpha-investing rule. As a result, the set of rejected sequential hypotheses is proven to control the marginal false discover rate. When not searching for polynomials, the package provides a statistically valid algorithm to run and terminate stepwise regression. For more information, see Johnson, Stine, and Foster (2019) <arXiv:1510.06322>.

This package provides tools to render DOT diagram markup language in R and also provides the possibility to export the graphs in PostScript and SVG (Scalable Vector Graphics) formats. In addition, it supports literate programming packages such as knitr and rmarkdown.

This package provides a Scheme-inspired Lisp dialect embedded in R, with macros, tail-call optimization, and seamless interoperability with R functions and data structures. (The name arl is short for An R Lisp.') Implemented in pure R with no compiled code.

Causal inference for a binary treatment and continuous outcome using Bayesian Causal Forests. See Hahn, Murray and Carvalho (2020) <doi:10.1214/19-BA1195> for additional information. This implementation relies on code originally accompanying Pratola et. al. (2013) <arXiv:1309.1906>.

An algorithm of optimal subset selection, related to Covariance matrices, observation matrices and Response vectors (COR) to select the optimal subsets in distributed estimation. The philosophy of the package is described in Guo G. (2024) <doi:10.1007/s11222-024-10471-z>.

Builds both ROC (Receiver Operating Characteristic) and DET (Detection Error Tradeoff) curves from a set of predictors that are the results of a binary classification system. The curves give a general view of classifier performance and are useful for comparing different systems.

Facilitates the aggregation of species geographic ranges from vector or raster spatial data, and that enables the calculation of various morphological and phylogenetic community metrics across geography. Citation: Title, PO, DL Swiderski and ML Zelditch (2022) <doi:10.1111/2041-210X.13914>.

The philosophy in the package is described in Stasny (1988) <doi:10.2307/1391558> and Guti?rrez, A., Trujillo, L. & Silva, N. (2014), <ISSN:1492-0921> to estimate the gross flows under complex surveys using a Markov chain approach with non response.

This package provides tools to compute the Generalized Measure of Correlation (GMC), a dependence measure accounting for nonlinearity and asymmetry in the relationship between variables. Based on the method proposed by Zheng, Shi, and Zhang (2012) <doi:10.1080/01621459.2012.710509>.

This package provides functions for the estimation, plotting, predicting and cross-validation of hierarchical feature regression models as described in Pfitzinger (2024). Cluster Regularization via a Hierarchical Feature Regression. Econometrics and Statistics (in press). <doi:10.1016/j.ecosta.2024.01.003>.

We use the ISR to handle with PCA-based missing data with high correlation, and the DISR to handle with distributed PCA-based missing data. The philosophy of the package is described in Guo G. (2024) <doi:10.1080/03610918.2022.2091779>.

Drawing statistical inference on the coefficients of a short- or long-horizon predictive regression with persistent regressors by using the IVX method of Magdalinos and Phillips (2009) <doi:10.1017/S0266466608090154> and Kostakis, Magdalinos and Stamatogiannis (2015) <doi:10.1093/rfs/hhu139>.

It implements Expectation/Conditional Maximization Either (ECME) and rapidly converging algorithms as well as Bayesian inference for linear mixed models, which is described in Schafer, J.L. (1998) "Some improved procedures for linear mixed models". Dept. of Statistics, The Pennsylvania State University.

The LIC criterion is to determine the most informative subsets so that the subset can retain most of the information contained in the complete data. The philosophy of the package is described in Guo G. (2022) <doi:10.1080/02664763.2022.2053949>.

Generating and validating One-time Password based on Hash-based Message Authentication Code (HOTP) and Time Based One-time Password (TOTP) according to RFC 4226 <https://datatracker.ietf.org/doc/html/rfc4226> and RFC 6238 <https://datatracker.ietf.org/doc/html/rfc6238>.

This package provides a fully legal chess move generator and game engine implemented in C++17 via Rcpp'. Provides FEN (Forsyth-Edwards Notation) parsing, PGN (Portable Game Notation) replay, position feature enrichment, and a multi-game registry backed by a bitboard representation.

Fit a probabilistic index model as described in Thas et al, 2012: <doi:10.1111/j.1467-9868.2011.01020.x>. The interface to the modeling function has changed in this new version. The old version is still available at R-Forge.

Computes a simple blinding index for randomized controlled trials introduced in Petroff, Bacak, Dagres, Dilk, Wachter: A simple blinding index for randomized controlled trials. Contemp Clin Trials Commun. 2024 Nov 26;42:101393. <doi:10.1016/j.conctc.2024.101393>. PMID: 39686958.

Extrema-weighted feature extraction for varying length functional data. Functional data analysis method that performs dimensionality reduction based on predefined features and allows for quantile weighting. Method implemented as presented in van den Boom et al. (2018) <doi:10.1093/bioinformatics/bty120>.