Quickly computes polygenic scores from GWAS summary statistics of either case-control or quantitative traits without parameter tuning. Reales,G., Vigorito, E., Kelemen,M., Wallace,C. (2021) <doi:10.1101/2020.07.24.220392> "RápidoPGS: A rapid polygenic score calculator for summary GWAS data without a test dataset".

Robust inference methods for fixed-effect and random-effects models of meta-analysis are implementable. The robust methods are developed using the density power divergence that is a robust estimating criterion developed in machine learning theory, and can effectively circumvent biases and misleading results caused by influential outliers. The density power divergence is originally introduced by Basu et al. (1998) <doi:10.1093/biomet/85.3.549>, and the meta-analysis methods are developed by Noma et al. (2022) <forthcoming>.

R Interface to JDemetra+ 3.x'(<https://github.com/jdemetra>). It offers several functions to manipulate JDemetra+ workspaces, which can be read by the software and can store several seasonal adjusted series along with user-defined calendars or regression variables.

This package implements the Ridge Selection Operator (RSO) for variable selection in linear regression as proposed by Wu (2021) <doi:10.1080/00401706.2020.1791254>. The RSO method extends classical ridge regression by using individually penalized ridge parameters, inducing sparsity through reciprocal penalty parameters. This package provides a fast C++ implementation ('RSOFast') using Armadillo linear algebra routines. The fast implementation precomputes matrix products, uses Cholesky factorization with primal/dual switching, and performs golden-section search for coordinate optimization.

Understanding heterogeneous causal effects based on pretreatment covariates is a crucial step in modern empirical work in data science. Building on the recent developments in Calonico et al (2025) <doi:10.48550/arXiv.2503.13696>, this package provides tools for estimation and inference of heterogeneous treatment effects in Regression Discontinuity (RD) Designs. The package includes two main commands: rdhte to conduct estimation and robust bias-corrected inference for conditional RD treatment effects (given choice of bandwidth parameter); rdbwhte', which implements automatic bandwidth selection methods; and rdhte_lincom to test linear combinations of parameters.

Gather boxscore, play-by-play, and auxiliary data from Major League Volleyball (MLV) <https://provolleyball.com>, League One Volleyball Pro (LOVB) <https://www.lovb.com/pro-league>, and Athletes Unlimited Pro Volleyball (AU) <https://auprosports.com/volleyball/> to create a repository of basic and advanced statistics for teams and players.

Rcpp reimplementation of the the Bayesian non-parametric Dirichlet Process Regression model for penalized regression first published in Zeng and Zhou (2017) <doi:10.1038/s41467-017-00470-2>. A full Bayesian version is implemented with Gibbs sampling, as well as a faster but less accurate variational Bayes approximation.

Allows to get weather data from Automated Surface Observing System (ASOS) stations (airports) in the whole world thanks to the Iowa Environment Mesonet website.

Empirical orthogonal teleconnections in R. remote is short for R(-based) EMpirical Orthogonal TEleconnections'. It implements a collection of functions to facilitate empirical orthogonal teleconnection analysis. Empirical Orthogonal Teleconnections (EOTs) denote a regression based approach to decompose spatio-temporal fields into a set of independent orthogonal patterns. They are quite similar to Empirical Orthogonal Functions (EOFs) with EOTs producing less abstract results. In contrast to EOFs, which are orthogonal in both space and time, EOT analysis produces patterns that are orthogonal in either space or time.

Generate a Core Collection with Principal Component Scoring Strategy (PCSS) using qualitative and/or quantitative trait data according to Hamon and Noirot (1990) <https://www.documentation.ird.fr/hor/fdi:36506>, Noirot et al. (1996) <doi:10.2307/2527837> and Noirot et al. (2003) <https://www.documentation.ird.fr/hor/fdi:010031886>.

Rasterize images using a 3D software renderer. 3D scenes are created either by importing external files, building scenes out of the included objects, or by constructing meshes manually. Supports point and directional lights, anti-aliased lines, shadow mapping, transparent objects, translucent objects, multiple materials types, reflection, refraction, environment maps, multicore rendering, bloom, tone-mapping, and screen-space ambient occlusion.

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.

An R command interface to the MLwiN multilevel modelling software package.

Routines that allow the user to run a large number of goodness-of-fit tests. It allows for data to be continuous or discrete. It includes routines to estimate the power of the tests and display them as a power graph. The routine run.studies allows a user to quickly study the power of a new method and how it compares to some of the standard ones.

This package provides utility functions that extend the capabilities of the reference-based multiple imputation package rbmi'. It supports clinical trial analysis workflows with functions for managing imputed datasets, applying analysis methods across imputations, and tidying results for reporting.

Reads data files acquired by Bruker Daltonics matrix-assisted laser desorption/ionization-time-of-flight mass spectrometer of the *flex series.

Matrix reconstruction, also known as matrix completion, is the task of inferring missing entries of a partially observed matrix. This package provides a method called OptSpace, which was proposed by Keshavan, R.H., Oh, S., and Montanari, A. (2009) <doi:10.1109/ISIT.2009.5205567> for a case under low-rank assumption.

This package provides tools for simulating synthetic survival data using a variety of methods, including kernel density estimation, parametric distribution fitting, and bootstrap resampling techniques for a desired sample size.

JDemetra+ (<https://github.com/jdemetra/jdemetra-app>) is the seasonal adjustment software officially recommended to the members of the European Statistical System and the European System of Central Banks. Seasonal adjustment models performed with JDemetra+ can be stored into workspaces. JWSACruncher (<https://github.com/jdemetra/jwsacruncher/releases> for v2 and <https://github.com/jdemetra/jdplus-main/releases> for v3) is a console tool that re-estimates all the multi-processing defined in a workspace and to export the result. rjwsacruncher allows to launch easily the JWSACruncher'.

This package provides functions for detecting and analyzing rare events in data. Implements isolation forest (Liu et al., 2008, <doi:10.1109/ICDM.2008.17>) and clustering for anomaly detection in time series residuals. Decomposes time series using LOESS (Locally Estimated Scatterplot Smoothing) or STL (Seasonal-Trend decomposition using LOESS). Detects marine heatwaves and cold spells following Hobday et al. (2016) <doi:10.1016/j.pocean.2015.12.014>. Provides goodness-of-fit tests for quantile regression (Haupt et al., 2011, <doi:10.1080/02664763.2011.573542>), partial dependence with quantile random forests, MCC (Matthews Correlation Coefficient) computation and testing, knee-point detection via the Kneedle algorithm (Satopaa et al., 2011, <doi:10.1109/ICDCSW.2011.20>), and spatial point matching.

Download the lyrics of your favorite songs in text and table formats. Also search for related songs or song information. More information: <https://docs.genius.com/> .

An implementation of a number of Global Trend models for time series forecasting that are Bayesian generalizations and extensions of some Exponential Smoothing models. The main differences/additions include 1) nonlinear global trend, 2) Student-t error distribution, and 3) a function for the error size, so heteroscedasticity. The methods are particularly useful for short time series. When tested on the well-known M3 dataset, they are able to outperform all classical time series algorithms. The models are fitted with MCMC using the rstan package.

This package provides an easy way to compute the Theil Sehn Regression method and also the Siegel Regression Method which are both robust methods base on the median of slopes between all pairs of data. In contrast with the least squared linear regression, these methods are not sensitive to outliers. Theil, H. (1992) <doi:10.1007/978-94-011-2546-8_20>, Sen, P. K. (1968) <doi:10.1080/01621459.1968.10480934>.

Measuring information flow between time series with Shannon and Rényi transfer entropy. See also Dimpfl and Peter (2013) <doi:10.1515/snde-2012-0044> and Dimpfl and Peter (2014) <doi:10.1016/j.intfin.2014.03.004> for theory and applications to financial time series. Additional references can be found in the theory part of the vignette.