The twelvedata REST service offers access to current and historical data on stocks, standard as well as digital crypto currencies, and other financial assets covering a wide variety of course and time spans. See <https://twelvedata.com/> for details, to create an account, and to request an API key for free-but-capped access to the data.

RVVM is a RISC-V CPU and system software implementation written in C. It supports the entire RV64GC ISA, and it passes compliance tests for both RV64 and RV32. OpenSBI, U-Boot, and custom firmwares boot and execute properly. It is capable of running Linux, FreeBSD, OpenBSD, Haiku, and other OSes. Furthermore, it emulates a variety of hardware and peripherals.

This package provides a series of tools to compute and plot quantities related to classical and robust wavelet variance for time series and regular lattices. More details can be found, for example, in Serroukh, A., Walden, A.T., & Percival, D.B. (2000) <doi:10.2307/2669537> and Guerrier, S. & Molinari, R. (2016) <doi:10.48550/arXiv.1607.05858>.

This package provides tools for creating and visualizing statistical process control charts. Control charts are used for monitoring measurement processes, such as those occurring in manufacturing. The objective is to monitor the history of such processes and flag outlying measurements: out-of-control signals. Montgomery, D. (2009, ISBN:978-0-470-16992-6) contains an extensive discussion of the methodology.

This package implements various win ratio methodologies for composite endpoints of death and non-fatal events, including the (stratified) proportional win-fractions (PW) regression models (Mao and Wang, 2020 <doi:10.1111/biom.13382>), (stratified) two-sample tests with possibly recurrent nonfatal event, and sample size calculation for standard win ratio test (Mao et al., 2021 <doi:10.1111/biom.13501>).

This package provides a new object oriented programming system designed to be a successor to S3 and S4. It includes formal class, generic, and method specification, and a limited form of multiple dispatch. It has been designed and implemented collaboratively by the R Consortium Object-Oriented Programming Working Group, which includes representatives from R-Core, Bioconductor, Posit/tidyverse, and the wider R community.

Bindings to FFmpeg <http://www.ffmpeg.org/> AV library for working with audio and video in R. Generates high quality video from images or R graphics with custom audio. Also offers high performance tools for reading raw audio, creating spectrograms', and converting between countless audio / video formats. This package interfaces directly to the C API and does not require any command line utilities.

This package contains Lioness Algorithm (LA) for finding optimal designs over continuous design space, optimal Latin hypercube designs, and optimal order-of-addition designs. LA is a brand new nature-inspired meta-heuristic optimization algorithm. Detailed methodologies of LA and its implementation on numerical simulations can be found at Hongzhi Wang, Qian Xiao and Abhyuday Mandal (2021) <doi:10.48550/arXiv.2010.09154>.

This package implements the "shrinkage t" statistic introduced in Opgen-Rhein and Strimmer (2007) <DOI:10.2202/1544-6115.1252> and a shrinkage estimate of the "correlation-adjusted t-score" (CAT score) described in Zuber and Strimmer (2009) <DOI:10.1093/bioinformatics/btp460>. It also offers a convenient interface to a number of other regularized t-statistics commonly employed in high-dimensional case-control studies.

This package provides functions to estimate the insertion and deletion rates of transposable element (TE) families. The estimation of insertion rate consists of an improved estimate of the age distribution that takes into account random mutations, and an adjustment by the deletion rate. A hypothesis test for a uniform insertion rate is also implemented. This package implements the methods proposed in Dai et al (2018).

Infer causation from observational data through pattern causality analysis (PC), with original algorithm for time series data from Stavroglou et al. (2020) <doi:10.1073/pnas.1918269117>, as well as methodological extensions for spatial cross-sectional data introduced by Zhang & Wang (2025) <doi:10.1080/13658816.2025.2581207>, together with a systematic description proposed in Lyu et al. (2026) <doi:10.1016/j.compenvurbsys.2026.102435>.

An enhanced implementation of Whittaker-Henderson smoothing for the graduation of one-dimensional and two-dimensional actuarial tables used to quantify Life Insurance risks. WH is based on the methods described in Biessy (2025) <doi:10.48550/arXiv.2306.06932>. Among other features, it generalizes the original smoothing algorithm to maximum likelihood estimation, automatically selects the smoothing parameter(s) and extrapolates beyond the range of data.

The computation of quadratic form (QF) distributions is often not trivial and it requires numerical routines. The package contains functions aimed at evaluating the exact distribution of quadratic forms (QFs) and ratios of QFs. In particular, we propose to evaluate density, quantile and distribution functions of positive definite QFs and ratio of independent positive QFs by means of an algorithm based on the numerical inversion of Mellin transforms.

re2c generates minimalistic hard-coded state machine (as opposed to full-featured table-based lexers). A flexible API allows generated code to be wired into virtually any environment. Instead of exposing a traditional yylex() style API, re2c exposes its internals. Be sure to take a look at the examples, as they cover a lot of real-world cases and shed some light on dark corners of the re2c API.

Empirical likelihood (EL) inference for two-sample problems. The following statistics are included: the difference of two-sample means, smooth Huber estimators, quantile (qdiff) and cumulative distribution functions (ddiff), probability-probability (P-P) and quantile-quantile (Q-Q) plots as well as receiver operating characteristic (ROC) curves. EL calculations are based on J. Valeinis, E. Cers (2011) <http://home.lu.lv/~valeinis/lv/petnieciba/EL_TwoSample_2011.pdf>.

This package provides a minimal R and C++ API for parsing well-known binary and well-known text representation of geometries to and from R-native formats. Well-known binary is compact and fast to parse; well-known text is human-readable and is useful for writing tests. These formats are only useful in R if the information they contain can be accessed in R, for which high-performance functions are provided here.

Comprehensive data analysis software, and the name "cg" stands for "compare groups." Its genesis and evolution are driven by common needs to compare administrations, conditions, etc. in medicine research and development. The current version provides comparisons of unpaired samples, i.e. a linear model with one factor of at least two levels. It also provides comparisons of two paired samples. Good data graphs, modern statistical methods, and useful displays of results are emphasized.

RSEM is a software package for estimating gene and isoform expression levels from RNA-Seq data. The RSEM package provides a user-friendly interface, supports threads for parallel computation of the EM algorithm, single-end and paired-end read data, quality scores, variable-length reads and RSPD estimation. In addition, it provides posterior mean and 95% credibility interval estimates for expression levels. For visualization, it can generate BAM and Wiggle files in both transcript-coordinate and genomic-coordinate.

Build display tables from tabular data with an easy-to-use set of functions. With its progressive approach, we can construct display tables with a cohesive set of table parts. Table values can be formatted using any of the included formatting functions. Footnotes and cell styles can be precisely added through a location targeting system. The way in which gt handles things for you means that you don't often have to worry about the fine details.

This package implements the split-fit-evaluate-assess workflow from Hastie, Tibshirani, and Friedman (2009, ISBN:978-0-387-84857-0) "The Elements of Statistical Learning", Chapter 7. Provides three-way data splitting with automatic stratification, mandatory seeds for reproducibility, automatic data type handling, and 10 algorithms out of the box. Uses Rust backend for cross-language deterministic splitting. Designed for tabular supervised learning with minimal ceremony. Polyglot parity with the Python mlw package on PyPI'.

Estimates the attributable fraction in different sampling designs adjusted for measured confounders using logistic regression (cross-sectional and case-control designs), conditional logistic regression (matched case-control design), Cox proportional hazard regression (cohort design with time-to- event outcome), gamma-frailty model with a Weibull baseline hazard and instrumental variables analysis. An exploration of the AF with a genetic exposure can be found in the package AFheritability Dahlqwist E et al. (2019) <doi:10.1007/s00439-019-02006-8>.

This package provides S3 vector types for functional data represented on grids, in spline bases, or via functional principal components. Supports arithmetic and summary methods, plotting, derivation, integration, smoothing, registration, and data import/export for these functional vectors. Includes data-wrangling tools for re-evaluation, subsetting, sub-assignment, zooming into sub-domains, and extracting functional features such as minima, maxima, and their locations. Enables joint analysis of functional and scalar variables by integrating functional vectors into standard data frames.

This package provides functions to estimate the parameters of the generalized Poisson distribution with or without covariates using maximum likelihood. The references include Nikoloulopoulos A.K. & Karlis D. (2008). "On modeling count data: a comparison of some well-known discrete distributions". Journal of Statistical Computation and Simulation, 78(3): 437--457, <doi:10.1080/10629360601010760> and Consul P.C. & Famoye F. (1992). "Generalized Poisson regression model". Communications in Statistics - Theory and Methods, 21(1): 89--109, <doi:10.1080/03610929208830766>.

This package provides a "tabular-data-resource" (<https://specs.frictionlessdata.io/tabular-data-resource/>) is a simple format to describe a singular tabular data resource such as a CSV file. It includes support both for metadata such as author and title and a schema to describe the data, for example the types of the fields/columns in the data. Create a tabular-data-resource by providing a data.frame and specifying metadata. Write and read tabular-data-resources to and from disk.