This package provides a set of functions to create SQL tables of gene and SNP information and compose them into a SNP Set, for example to export to a PLINK set.

Character vector to numerical translation in Euros from Spanish spelled monetary quantities. Reverse translation from integer to Spanish. Upper limit is up to the millions range. Geocoding via Cadastral web site.

This package provides a spatial covariate-augmented overdispersed Poisson factor model is proposed to perform efficient latent representation learning method for high-dimensional large-scale spatial count data with additional covariates.

Fast calculation of the Subtree Prune and Regraft (SPR), Tree Bisection and Reconnection (TBR) and Replug distances between unrooted trees, using the algorithms of Whidden and Matsen (2017) <arxiv:1511.07529>.

Forecasting univariate time series with Variational Mode Decomposition (VMD) based time delay neural network models.For method details see Konstantin, D.and Dominique, Z. (2014). <doi:10.1109/TSP.2013.2288675>.

Implementation of the variable banding procedure for modeling local dependence and estimating precision matrices that is introduced in Yu & Bien (2016) and is available at <https://arxiv.org/abs/1604.07451>.

This package defines data structures for linkage disequilibrium (LD) measures in populations. Its purpose is to simplify handling of existing population-level data for the purpose of flexibly defining LD blocks.

This package provides basic utility functions for performing single-cell analyses, focusing on simple normalization, quality control and data transformations. It also provides some helper functions to assist development of other packages.

This package is a collection of functions and layers to enhance ggplot2. The flagship function is ggMarginal(), which can be used to add marginal histograms/boxplots/density plots to ggplot2 scatterplots.

The fstlib library provides multithreaded serialization of compressed data frames using the fst format. The fst format allows for random access of stored data and compression with the LZ4 and ZSTD compressors.

This package provides functions for reading and writing data stored by some versions of Epi Info, Minitab, S, SAS, SPSS, Stata, Systat and Weka and for reading and writing some dBase files.

This package provides a pure Rust implementation of SEC1: Elliptic Curve Cryptography encoding formats including ASN.1 DER-serialized private keys as well as the Elliptic-Curve-Point-to-Octet-String encoding.

simd offers limited cross-platform access to SIMD instructions on CPUs, as well as raw interfaces to platform-specific instructions. (To be obsoleted by the std::simd implementation RFC 2366.)

This package provides a set of functions to facilitate building formatted strings under various replacement rules: C-style formatting, variable-based formatting, and number-based formatting. C-style formatting is basically identical to built-in function sprintf'. Variable-based formatting allows users to put variable names in a formatted string which will be replaced by variable values. Number-based formatting allows users to use index numbers to represent the corresponding argument value to appear in the string.

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>.

To facilitate using cereal with R via cpp11 or Rcpp'. cereal is a header-only C++11 serialization library. cereal takes arbitrary data types and reversibly turns them into different representations, such as compact binary encodings, XML', or JSON'. cereal was designed to be fast, light-weight, and easy to extend - it has no external dependencies and can be easily bundled with other code or used standalone. Please see <https://uscilab.github.io/cereal/> for more information.

An implementation of robust boosting algorithms for regression in R. This includes the RRBoost method proposed in the paper "Robust Boosting for Regression Problems" (Ju X and Salibian-Barrera M. 2020) <doi:10.1016/j.csda.2020.107065>. It also implements previously proposed boosting algorithms in the simulation section of the paper: L2Boost, LADBoost, MBoost (Friedman, J. H. (2001) <doi:10.1214/aos/1013203451>) and Robloss (Lutz et al. (2008) <doi:10.1016/j.csda.2007.11.006>).

S3 and S4 functions are implemented for spatial multi-site stochastic generation of daily time series of temperature and precipitation. These tools make use of Vector AutoRegressive models (VARs). The weather generator model is then saved as an object and is calibrated by daily instrumental "Gaussianized" time series through the vars package tools. Once obtained this model, it can it can be used for weather generations and be adapted to work with several climatic monthly time series.

This package provides a tool that allows to download and save historical time series data for future use offline. The intelligent updating functionality will only download the new available information; thus, saving you time and Internet bandwidth. It will only re-download the full data-set if any inconsistencies are detected. This package supports following data provides: Yahoo (finance.yahoo.com), FRED (fred.stlouisfed.org), Quandl (data.nasdaq.com), AlphaVantage (www.alphavantage.co), Tiingo (www.tiingo.com).

This package provides functions to reconstruct, generate, and simulate synchronous, asynchronous, probabilistic, and temporal Boolean networks. Provides also functions to analyze and visualize attractors in Boolean networks <doi:10.1093/bioinformatics/btq124>.

Used for Bayesian mediation analysis based on Bayesian additive Regression Trees (BART). The analysis method is described in Yu and Li (2025) "Mediation Analysis with Bayesian Additive Regression Trees", submitted for publication.

This package provides functions for training extreme gradient boosting model using propensity score A-learning and weight-learning methods. For further details, see Liu et al. (2024) <doi:10.1093/bioinformatics/btae592>.

Utility functions for the statistical analysis of corpus frequency data. This package is a companion to the open-source course "Statistical Inference: A Gentle Introduction for Computational Linguists and Similar Creatures" ('SIGIL').

Can be useful for finding associations among different positions in a position-wise aligned sequence dataset. The approach adopted for finding associations among positions is based on the latent multivariate normal distribution.