The doubly regularized matrix-variate regression solves a low-rank-plus-sparse structure for matrix-variate generalized linear models through a weighted combination of nuclear-norm and L1-norm. The methodology implemented by this package is described in the paper "Doubly Regularized Matrix-Variate Regression". See Xu, Luo and Jiang <doi:10.1080/10618600.2026.2702534>.

This package provides a unified interface for connecting to databases ('SQLite', MySQL', PostgreSQL'). Just provide the database name and the package will ask you questions to help you configure the connection and setup your credentials. Once database configuration and connection has been set up once, you won't have to do it ever again.

Fit the penalized Cox models with both non-overlapping and overlapping grouped penalties including the group lasso, group smoothly clipped absolute deviation, and group minimax concave penalty. The algorithms combine the MM approach and group-wise descent with some computational tricks including the screening, active set, and warm-start. Different tuning regularization parameter methods are provided.

Fast, flexible, patient-level microsimulation. Time-stepped simulation with a C++ back-end from user-supplied initial population, trajectories, hazards, and corresponding event transitions. User-defined aggregate time series histories are returned together with the final population. Designed for simulation of chronic diseases with continuous and evolving risk factors, but could easily be applied more generally.

Independent vector analysis (IVA) is a blind source separation (BSS) model where several datasets are jointly unmixed. This package provides several methods for the unmixing together with some performance measures. For details, see Anderson et al. (2011) <doi:10.1109/TSP.2011.2181836> and Lee et al. (2007) <doi:10.1016/j.sigpro.2007.01.010>.

JSON-LD <https://www.w3.org/TR/json-ld/> is a light-weight syntax for expressing linked data. It is primarily intended for web-based programming environments, interoperable web services and for storing linked data in JSON-based databases. This package provides bindings to the JavaScript library for converting, expanding and compacting JSON-LD documents.

Evaluation of the Jacobi theta functions and related functions: Weierstrass elliptic function, Weierstrass sigma function, Weierstrass zeta function, Klein j-function, Dedekind eta function, lambda modular function, Jacobi elliptic functions, Neville theta functions, Eisenstein series, lemniscate elliptic functions, elliptic alpha function, Rogers-Ramanujan continued fractions, and Dixon elliptic functions. Complex values of the variable are supported.

This package provides a toolkit for absolute and relative dating and analysis of chronological patterns. This package includes functions for chronological modeling and dating of archaeological assemblages from count data. It provides methods for matrix seriation. It also allows to compute time point estimates and density estimates of the occupation and duration of an archaeological site.

This package provides a collection of large language model (LLM) text analysis methods designed with psychological data in mind. Currently, LLMing (aka "lemming") includes a text anomaly detection method based on the angle-based subspace approach described by Zhang, Lin, and Karim (2015) and a text generation method. <doi:10.1016/j.ress.2015.05.025>.

This package implements methods to normalize multiplexed imaging data, including statistical metrics and visualizations to quantify technical variation in this data type. Reference for methods listed here: Harris, C., Wrobel, J., & Vandekar, S. (2022). mxnorm: An R Package to Normalize Multiplexed Imaging Data. Journal of Open Source Software, 7(71), 4180, <doi:10.21105/joss.04180>.

This package provides a general framework for computing powers of matrices. A key feature is the capability for users to write callback functions, called after each iteration, thus enabling customization for specific applications. Diverse types of matrix classes/matrix multiplication are accommodated. If the multiplication type computes in parallel, then the package computation is also parallel.

This package provides methods to construct multivariate grids, which can be used for multivariate quadrature. This grids can be based on different quadrature rules like Newton-Cotes formulas (trapezoidal-, Simpson's- rule, ...) or Gauss quadrature (Gauss-Hermite, Gauss-Legendre, ...). For the construction of the multidimensional grid the product-rule or the combination- technique can be applied.

Parse messy geographic coordinates from various character formats to decimal degree numeric values. Parse coordinates into their parts (degree, minutes, seconds); calculate hemisphere from coordinates; pull out individually degrees, minutes, or seconds; add and subtract degrees, minutes, and seconds. C++ code herein originally inspired from code written by Jeffrey D. Bogan, but then completely re-written.

Data sets used by Krause et al. (2022) <doi:10.1101/2022.04.11.487885>. It comprises phenotypic records obtained from the USDA Northern Region Uniform Soybean Tests from 1989 to 2019 for maturity groups II and III. In addition, soil and weather variables are provided for the 591 observed environments (combination of locations and years).

Implementations for two different Bayesian models of differential co-expression. scdeco.cop() fits the bivariate Gaussian copula model from Zichen Ma, Shannon W. Davis, Yen-Yi Ho (2023) <doi:10.1111/biom.13701>, while scdeco.pg() fits the bivariate Poisson-Gamma model from Zhen Yang, Yen-Yi Ho (2022) <doi:10.1111/biom.13457>.

We propose an optimality criterion to determine the required training set, r-score, which is derived directly from Pearson's correlation between the genomic estimated breeding values and phenotypic values of the test set <doi:10.1007/s00122-019-03387-0>. This package provides two main functions to determine a good training set and its size.

This package provides functions to produce, fit and predict from bipartite networks with abundance, trait and phylogenetic information. Its methods are described in detail in Benadi, G., Dormann, C.F., Fruend, J., Stephan, R. & Vazquez, D.P. (2021) Quantitative prediction of interactions in bipartite networks based on traits, abundances, and phylogeny. The American Naturalist, in press.

Implementation of dependent discrete models (with reversible jump MCMC) derived from BayesTraits V5.0.3 <https://github.com/AndrewPMeade/BayesTraits-Release/tree/Release>. Original software copyright Andrew Meade and contributors, distributed under GPL-3. Modifications for this package by Vivian G. Li <liguo.vivian@gmail.com>. The following articles should be referenced when using this package: Pagel, M., A. Meade and D. Barker (2004) "Bayesian estimation of ancestral character states on phylogenies" <doi:10.1080/10635150490522232>; Pagel, M. (1994) "Detecting correlated evolution on phylogenies: a general method for the comparative analysis of discrete characters" <doi:10.1098/rspb.1994.0006>; Pagel, M. and A. Meade (2006) "Bayesian analysis of correlated evolution of discrete characters by reversible-jump Markov chain Monte Carlo" <doi:10.1086/503444>.

This package provides a tool for multiply imputing missing data using MIDAS', a deep learning method based on denoising autoencoder neural networks (see Lall and Robinson, 2022; <doi:10.1017/pan.2020.49>). This algorithm offers significant accuracy and efficiency advantages over other multiple imputation strategies, particularly when applied to large datasets with complex features. Alongside interfacing with Python to run the core algorithm, this package contains functions for processing data before and after model training, running imputation model diagnostics, generating multiple completed datasets, and estimating regression models on these datasets. For more information see Lall and Robinson (2023) <doi:10.18637/jss.v107.i09>. This package is deprecated in favor of rMIDAS2'; it remains available for existing workflows but will receive only compatibility and documentation updates.

r128gain is a multi platform command line tool to scan your audio files and tag them with loudness metadata (ReplayGain v2 or Opus R128 gain format), to allow playback of several tracks or albums at a similar loudness level. r128gain can also be used as a Python module from other Python projects to scan and/or tag audio files.

This R package lets you estimate signatures of mutational processes and their activities on mutation count data. Starting from a set of single-nucleotide variants (SNVs), it allows both estimation of the exposure of samples to predefined mutational signatures (including whether the signatures are present at all), and identification of signatures de novo from the mutation counts.

Nucleotide conversion sequencing experiments have been developed to add a temporal dimension to RNA-seq and single-cell RNA-seq. Such experiments require specialized tools for primary processing such as GRAND-SLAM, and specialized tools for downstream analyses. grandR provides a comprehensive toolbox for quality control, kinetic modeling, differential gene expression analysis and visualization of such data.

This package provides tools for data importation, recoding, and inspection. There are functions to create new project folders, R code templates, create uniquely named output directories, and to quickly obtain a visual summary for each variable in a data frame. The main feature here is the systematic implementation of the "variable key" framework for data importation and recoding.

Redshift adjusts the color temperature according to the position of the sun. A different color temperature is set during night and daytime. During twilight and early morning, the color temperature transitions smoothly from night to daytime temperature to allow your eyes to slowly adapt. At night the color temperature should be set to match the lamps in your room.