This package provides a set of methods to access and parse live filing information from the U.S. Securities and Exchange Commission (SEC - <https://www.sec.gov/>) including company and fund filings along with all associated metadata.

This package provides functions to calculate the best linear unbiased prediction of genotype-by-environment metrics: ecovalence, environmental variance, Finlay and Wilkinson regression and Lin and Binns superiority measure, based on a multi-environment genomic prediction model.

This package provides flexible tools for the visualization of genomic data. Supports interactive and static plots tailored for presentations and publications, with customizable features like colors, themes, and annotations to align with specific analytical and presentation goals.

This package provides a RStudio addin to send some JavaScript code to the V8 console. The user can send an entire JavaScript file or only some selected lines. This is useful to test the code.

Approximate joint-inclusion probabilities in Unequal Probability Sampling, or compute Monte Carlo approximations of the first and second-order inclusion probabilities of a general sampling design as in Fattorini (2006) <doi:10.1093/biomet/93.2.269>.

Generates quotes from Lero Lero', a database for meaningless sentences filled with corporate buzzwords, intended to be used as corporate lorem ipsum (see <http://www.lerolero.com/> for more information). Unfortunately, quotes are currently portuguese-only.

Recursive partition algorithms designed for fitting survival trees with left-truncated and right-censored (LTRC) data, as well as interval-censored data. The LTRC trees can also be used to fit survival trees with time-varying covariates.

Fit and simulate latent position and cluster models for statistical networks. See Krivitsky and Handcock (2008) <doi:10.18637/jss.v024.i05> and Krivitsky, Handcock, Raftery, and Hoff (2009) <doi:10.1016/j.socnet.2009.04.001>.

This package provides multiply robust estimators of principal survival causal effects among always-takers, compliers, and never-takers in studies with treatment noncompliance. The methods are based on Cheng et al. (2026) <doi:10.1214/25-AOAS2117>.

Defines colour palettes and themes for Michigan State University (MSU) publications and presentations. Palettes and themes are supported in both base R and ggplot2 graphics, and are intended to provide consistency between those creating documents and presentations.

MUI X Charts React chart components for data visualization for building shiny applications and quarto documents. Bundles the MIT-licensed community edition of the @mui/x-charts JavaScript library (the commercial Pro tier is not included).

Perform library searches against electron ionization mass spectral databases using either the API provided by MS Search software (<https://chemdata.nist.gov/dokuwiki/doku.php?id=chemdata:nistlibs>) or custom implementations of the Identity and Similarity algorithms.

Loads, validates, and replays portable mars ModelSpec artifacts from R. The package provides helpers for constructing design matrices, generating predictions, and, when the companion runtime helper is available, fitting portable model specifications for cross-language replay.

This package provides functions for the robust estimation of parametric families of copulas using minimization of the Maximum Mean Discrepancy, following the article Alquier, Chérief-Abdellatif, Derumigny and Fermanian (2022) <doi:10.1080/01621459.2021.2024836>.

Nonparametric methods for smoothing regression function data with change-points, utilizing range kernels for iterative and anisotropic smoothing methods. For further details, see the paper by John R.J. Thompson (2024) <doi:10.1080/02664763.2024.2352759>.

Systematically creates and modifies NONMEM(R) control streams. Harvests NONMEM output, builds run logs, creates derivative data, generates diagnostics. NONMEM (ICON Development Solutions <https://www.iconplc.com/>) is software for nonlinear mixed effects modeling. See package?nonmemica'.

This package provides a unified object-oriented framework for numerical optimizers in R. Supports minimization and maximization with any optimizer, optimization over more than one function argument, computation time measurement, and time limits for long optimization tasks.

Track and record the use of applications and the user's interactions with Shiny inputs. Allows to trace the inputs with which the user interacts, the outputs generated, as well as the errors displayed in the interface.

Create a side-by-side view of raster(image)s with an interactive slider to switch between regions of the images. This can be especially useful for image comparison of the same region at different time stamps.

High-performance parsing of Tableau workbook files into tidy data frames and dependency graphs for other visualization tools like R Shiny or Power BI replication, plus an interactive Shiny workbook inspector for uploaded .twb and .twbx files.

Automates translating the instructions of iatgen generated qsf (Qualtrics survey files) to other languages using either officially supported or user-supplied translations (for tutorial see Santos et al., 2023 <doi:10.17504/protocols.io.kxygx34jdg8j/v1>).

R implementation of the vol2bird software for generating vertical profiles of birds and other biological signals in weather radar data. See Dokter et al. (2011) <doi:10.1098/rsif.2010.0116> for a paper describing the methodology.

This package provides an easy way to report the results of regression analysis, including: 1. Proportional hazards regression from function coxph of package survival'; 2. Conditional logistic regression from function clogit of package survival'; 3. Ordered logistic regression from function polr of package MASS'; 4. Binary logistic regression from function glm of package stats'; 5. Linear regression from function lm of package stats'; 6. Risk regression model for survival analysis with competing risks from function FGR of package riskRegression'; 7. Multilevel model from function lme of package nlme'.

Implemented are various tests for semi-parametric repeated measures and general MANOVA designs that do neither assume multivariate normality nor covariance homogeneity, i.e., the procedures are applicable for a wide range of general multivariate factorial designs. In addition to asymptotic inference methods, novel bootstrap and permutation approaches are implemented as well. These provide more accurate results in case of small to moderate sample sizes. Furthermore, post-hoc comparisons are provided for the multivariate analyses. Friedrich, S., Konietschke, F. and Pauly, M. (2019) <doi:10.32614/RJ-2019-051>.