This package provides functions in this package will import filtered variant call format (VCF) files of SNPs data and generate data sets to detect copy number variants, visualize them and do downstream analyses with copy number variants(e.g. Environmental association analyses).

This package provides a framework that supports creating and extending enterprise Shiny applications using best practices.

This package provides a tool for building projects that are visually consistent, accessible, and easy to maintain. It provides functions for managing branding assets, applying organization-wide themes using brand.yml', and setting up new projects with accessibility features and correct branding. It supports quarto', shiny', and rmarkdown projects, and integrates with ggplot2'. The accessibility features are based on the Web Content Accessibility Guidelines <https://www.w3.org/WAI/WCAG22/quickref/?versions=2.1> and Accessible Rich Internet Applications (ARIA) specifications <https://www.w3.org/WAI/ARIA/apg/>. The branding framework implements the brand.yml specification <https://posit-dev.github.io/brand-yml/>.

This package provides functions to have nice rmarkdown outputs of the seasonal and trading day adjustment models made with RJDemetra'.

Convert a string of text characters to Elder Futhark Runes <https://en.wikipedia.org/wiki/Elder_Futhark>.

Export all data, including metadata, from a REDCap (Research Electronic Data Capture) Project via the REDCap API <https://projectredcap.org/wp-content/resources/REDCapTechnicalOverview.pdf>. The exported (meta)data will be processed and formatted into a stand alone R data package which can be installed and shared between researchers. Several default reports are generated as vignettes in the resulting package.

This package provides functions to facilitate inference on the relative importance of predictors in a linear or generalized linear model, and a couple of useful Tcl/Tk widgets.

R Commander plugin for teaching statistical methods. It adds a new menu for making easier the teaching of the main concepts about the main statistical methods.

This package provides tools for regression-based Boolean rule inference in artificial intelligence studies. The package fits ridge regression models on conjunction expansions and composes interpretable rule sets. Parallel execution is supported for multi-CPU environments.

Estimates the pooled (unadjusted) Receiver Operating Characteristic (ROC) curve, the covariate-adjusted ROC (AROC) curve, and the covariate-specific/conditional ROC (cROC) curve by different methods, both Bayesian and frequentist. Also, it provides functions to obtain ROC-based optimal cutpoints utilizing several criteria. Based on Erkanli, A. et al. (2006) <doi:10.1002/sim.2496>; Faraggi, D. (2003) <doi:10.1111/1467-9884.00350>; Gu, J. et al. (2008) <doi:10.1002/sim.3366>; Inacio de Carvalho, V. et al. (2013) <doi:10.1214/13-BA825>; Inacio de Carvalho, V., and Rodriguez-Alvarez, M.X. (2022) <doi:10.1214/21-STS839>; Janes, H., and Pepe, M.S. (2009) <doi:10.1093/biomet/asp002>; Pepe, M.S. (1998) <http://www.jstor.org/stable/2534001?seq=1>; Rodriguez-Alvarez, M.X. et al. (2011a) <doi:10.1016/j.csda.2010.07.018>; Rodriguez-Alvarez, M.X. et al. (2011a) <doi:10.1007/s11222-010-9184-1>. Please see Rodriguez-Alvarez, M.X. and Inacio, V. (2021) <doi:10.32614/RJ-2021-066> for more details.

This package performs goodness of fits tests for both high and low-dimensional linear models. It can test for a variety of model misspecifications including nonlinearity and heteroscedasticity. In addition one can test the significance of potentially large groups of variables, and also produce p-values for the significance of individual variables in high-dimensional linear regression.

These tools help you to assess if a corporate lending portfolio aligns with climate goals. They summarize key climate indicators attributed to the portfolio (e.g. production, emission factors), and calculate alignment targets based on climate scenarios. They implement in R the last step of the free software PACTA (Paris Agreement Capital Transition Assessment; <https://www.transitionmonitor.com/>). Financial institutions use PACTA to study how their capital allocation decisions align with climate change mitigation goals.

The RBP curve is a visual tool to assess the performance of prediction models.

This package provides a methodology to perform multivariate measurement error adjustment using external validation data. Allows users to remove the attenuating effect of measurement error by incorporating a distribution of external validation data, and allows for plotting of all resultant adjustments. Sensitivity analyses can also be run through this package to test how different ranges of validity coefficients can impact the effect of the measurement error adjustment. The methods implemented in this package are based on the work by Muoka, A., Agogo, G., Ngesa, O., Mwambi, H. (2020): <doi:10.12688/f1000research.27892.1>.

Allows caching of raw data directly in R code. This allows R scripts and R Notebooks to be shared and re-run on a machine without access to the original data. Cached data is encoded into an ASCII string that can be pasted into R code. When the code is run, the data is automatically loaded from the cached version if the original data file is unavailable. Works best for small datasets (a few hundred observations).

Create interactive radar charts using the Chart.js JavaScript library and the htmlwidgets package. Chart.js <http://www.chartjs.org/> is a lightweight library that supports several types of simple chart using the HTML5 canvas element. This package provides an R interface specifically to the radar chart, sometimes called a spider chart, for visualising multivariate data.

Companion package for the book: "Robust Statistics: Theory and Methods, second edition", <http://www.wiley.com/go/maronna/robust>. This package contains code that implements the robust estimators discussed in the recent second edition of the book above, as well as the scripts reproducing all the examples in the book.

Uses convolution-based techniques to generate simulated camera bokeh, depth of field, and other camera effects, using an image and an optional depth map. Accepts both filename inputs and in-memory array representations of images and matrices. Includes functions to perform 2D convolutions, reorient and resize images/matrices, add image and text overlays, generate camera vignette effects, and add titles to images.

Wraps some of the matrix exponentiation utilities from EXPOKIT (<http://www.maths.uq.edu.au/expokit/>), a FORTRAN library that is widely recommended for matrix exponentiation (Sidje RB, 1998. "Expokit: A Software Package for Computing Matrix Exponentials." ACM Trans. Math. Softw. 24(1): 130-156). EXPOKIT includes functions for exponentiating both small, dense matrices, and large, sparse matrices (in sparse matrices, most of the cells have value 0). Rapid matrix exponentiation is useful in phylogenetics when we have a large number of states (as we do when we are inferring the history of transitions between the possible geographic ranges of a species), but is probably useful in other ways as well. NOTE: In case FORTRAN checks temporarily get rexpokit archived on CRAN, see archived binaries at GitHub in: nmatzke/Matzke_R_binaries (binaries install without compilation of source code).

This package performs one-sample t-test based on robustified statistics using median/MAD (TA) and Hodges-Lehmann/Shamos (TB). For more details, see Park and Wang (2018)<arXiv:1807.02215>. This work was partially supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (No. NRF-2017R1A2B4004169).

This package provides a toolkit for the analysis of paths from spatial tracking experiments and calculation of goal-finding strategies. This package is centered on an approach using machine learning for path classification.

Modularizes source code. Keeps the global environment clean, explicifies interdependencies. Inspired by RequireJS'<http://requirejs.org/>.

Visualizing crystal structures and selected area electron diffraction (SAED) patterns. It provides functions cry_demo() and dp_demo() to load a file in CIF (Crystallographic Information Framework) formats and display crystal structures and electron diffraction patterns. The function dp_demo() also performs simple simulation of powder X-ray diffraction (PXRD) patterns, and the results can be saved to a file in the working directory. The package has been tested on several platforms, including Linux on Crostini with a Coreâ ¢ m3-8100Y Chromebook, I found that even on this low-powered platform, the performance was acceptable. T. Hanashima (2001) <https://www2.kek.jp/imss/pf/tools/sasaki/sinram/sinram.html> Todd Helmenstine (2019) <https://sciencenotes.org/molecule-atom-colors-cpk-colors/> Wikipedia contributors (2023) <https://en.wikipedia.org/w/index.php?title=Atomic_radius&oldid=1179864711>.

This package provides a toolkit for Commodities analytics', risk management and trading professionals. Includes functions for API calls to <https://commodities.morningstar.com/#/>, <https://developer.genscape.com/>, and <https://www.bankofcanada.ca/valet/docs>.