This package provides an R interface to the Data Retriever <https://retriever.readthedocs.io/en/latest/> via the Data Retriever's command line interface. The Data Retriever automates the tasks of finding, downloading, and cleaning public datasets, and then stores them in a local database.

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.

Restricted Cubic Splines were performed to explore the shape of association form of "U, inverted U, L" shape and test linearity or non-linearity base on "Cox,Logistic,linear,quasipoisson" regression, and auto output Restricted Cubic Splines figures. rcssci package could automatically draw RCS graphics with Y-axis "OR,HR,RR,beta". The Restricted Cubic Splines method were based on Suli Huang (2022) <doi:10.1016/j.ecoenv.2022.113183>,Amit Kaura (2019) <doi:10.1136/bmj.l6055>, and Harrell Jr (2015, ISBN:978-3-319-19424-0 (Print) 978-3-319-19425-7 (Online)).

We provide functions to compute confidence intervals for a well-defined nonlinear function of the model parameters (e.g., product of k coefficients) in single--level and multilevel structural equation models. It also computes a chi-square test statistic for a function of indirect effects. Tofighi', D. and MacKinnon', D. P. (2011). RMediation An R package for mediation analysis confidence intervals. Behavior Research Methods, 43, 692--700. <doi:10.3758/s13428-011-0076-x>. Tofighi', D. (2020). Bootstrap Model-Based Constrained Optimization Tests of Indirect Effects. Frontiers in Psychology, 10, 2989. <doi:10.3389/fpsyg.2019.02989>.

Connect, query, and operate on information available from the Open Source Vulnerability database <https://osv.dev/>. Although CRAN has vulnerabilities listed, these are few compared to projects such as PyPI'. With tighter integration between R and Python', having an R specific package to access details about vulnerabilities from various sources is a worthwhile enterprise.

An RStudio addin providing shortcuts for writing in Markdown'. This package provides a series of functions that allow the user to be more efficient when using Markdown'. For example, you can select a word, and put it in bold or in italics, or change the alignment of elements inside you Rmd. The idea is to map all the functionalities from remedy on keyboard shortcuts, so that it provides an interface close to what you can find in any other text editor.

Randomization inference procedures for simple and complex randomized designs, including multi-armed trials, as described in Gerber and Green (2012, ISBN: 978-0393979954). Users formally describe their randomization procedure and test statistic. The randomization distribution of the test statistic under some null hypothesis is efficiently simulated.

This package implements the methodology of "Cannings, T. I. and Samworth, R. J. (2017) Random-projection ensemble classification, J. Roy. Statist. Soc., Ser. B. (with discussion), 79, 959--1035". The random projection ensemble classifier is a general method for classification of high-dimensional data, based on careful combination of the results of applying an arbitrary base classifier to random projections of the feature vectors into a lower-dimensional space. The random projections are divided into non-overlapping blocks, and within each block the projection yielding the smallest estimate of the test error is selected. The random projection ensemble classifier then aggregates the results of applying the base classifier on the selected projections, with a data-driven voting threshold to determine the final assignment.

Handle climate data from the DWD ('Deutscher Wetterdienst', see <https://www.dwd.de/EN/climate_environment/cdc/cdc_node_en.html> for more information). Choose observational time series from meteorological stations with selectDWD()'. Find raster data from radar and interpolation according to <https://bookdown.org/brry/rdwd/raster-data.html>. Download (multiple) data sets with progress bars and no re-downloads through dataDWD()'. Read both tabular observational data and binary gridded datasets with readDWD()'.

Relevant Component Analysis (RCA) tries to find a linear transformation of the feature space such that the effect of irrelevant variability is reduced in the transformed space.

The real-time quantitative polymerase chain reaction (qPCR) technical data sets by Ruijter et al. (2013) <doi:10.1016/j.ymeth.2012.08.011>: (i) the four-point 10-fold dilution series; (ii) 380 replicates; and (iii) the competimer data set. These three data sets can be used to benchmark qPCR methods. Original data set is available at <https://medischebiologie.nl/wp-content/uploads/2019/02/qpcrdatamethods.zip>. This package fixes incorrect annotations in the original data sets.

Gene-environment (GÃ E) interactions have important implications to elucidate the etiology of complex diseases beyond the main genetic and environmental effects. Outliers and data contamination in disease phenotypes of GÃ E studies have been commonly encountered, leading to the development of a broad spectrum of robust penalization methods. Nevertheless, within the Bayesian framework, the issue has not been taken care of in existing studies. We develop a robust Bayesian variable selection method for GÃ E interaction studies. The proposed Bayesian method can effectively accommodate heavy-tailed errors and outliers in the response variable while conducting variable selection by accounting for structural sparsity. In particular, the spike-and-slab priors have been imposed on both individual and group levels to identify important main and interaction effects. An efficient Gibbs sampler has been developed to facilitate fast computation. The Markov chain Monte Carlo algorithms of the proposed and alternative methods are efficiently implemented in C++.

This package provides a collection of methods for the robust analysis of univariate and multivariate functional data, possibly in high-dimensional cases, and hence with attention to computational efficiency and simplicity of use. See the R Journal publication of Ieva et al. (2019) <doi:10.32614/RJ-2019-032> for an in-depth presentation of the roahd package. See Aleman-Gomez et al. (2021) <arXiv:2103.08874> for details about the concept of depthgram.

Rcmdr interface to the sos package. The plug-in renders the sos searching functionality easily accessible via the Rcmdr menus. It also simplifies the task of performing multiple searches and subsequently obtaining the union or the intersection of the results.

Packed bar charts are a variation of treemaps for visualizing skewed data. The concept was introduced by Xan Gregg at JMP'.

This package provides bioaccumulation factors from a toxicokinetic model fitted to accumulation-depuration data. It is designed to fulfil the requirements of regulators when examining applications for market authorization of active substances.

This provides a robust estimator for stochastic frontier models, employing the Minimum Density Power Divergence Estimator (MDPDE) for enhanced robustness against outliers. Additionally, it includes a function to recommend the optimal tuning parameter, alpha, which controls the robustness of the MDPDE. The methods implemented in this package are based on Song et al. (2017) <doi:10.1016/j.csda.2016.08.005>.

Minimally adjust the values of numerical records in a data.frame, such that each record satisfies a predefined set of equality and/or inequality constraints. The constraints can be defined using the validate package. The core algorithms have recently been moved to the lintools package, refer to lintools for a more basic interface and access to a version of the algorithm that works with sparse matrices.

Get information (boards, pins and users) from the Pinterest <http://www.pinterest.com> API.

Fit and simulate any kind of physiologically-based kinetic ('PBK') models whatever the number of compartments. Moreover, it allows to account for any link between pairs of compartments, as well as any link of each of the compartments with the external medium. Such generic PBK models have today applications in pharmacology (PBPK models) to describe drug effects, in toxicology and ecotoxicology (PBTK models) to describe chemical substance effects. In case of exposure to a parent compound (drug or chemical) the rPBK package allows to consider metabolites, whatever their number and their phase (I, II, ...). Last but not least, package rPBK can also be used for dynamic flux balance analysis (dFBA) to deal with metabolic networks. See also Charles et al. (2022) <doi:10.1101/2022.04.29.490045>.

The expander functions rely on the mathematics developed for the Hessian-definiteness invariance theorem for linear projection transformations of variables, described in authors paper, to generate the full, high-dimensional gradient and Hessian from the lower-dimensional derivative objects. This greatly relieves the computational burden of generating the regression-function derivatives, which in turn can be fed into any optimization routine that utilizes such derivatives. The theorem guarantees that Hessian definiteness is preserved, meaning that reasoning about this property can be performed in the low-dimensional space of the base distribution. This is often a much easier task than its equivalent in the full, high-dimensional space. Definiteness of Hessian can be useful in selecting optimization/sampling algorithms such as Newton-Raphson optimization or its sampling equivalent, the Stochastic Newton Sampler. Finally, in addition to being a computational tool, the regression expansion framework is of conceptual value by offering new opportunities to generate novel regression problems.

This is a sudoku game package with a shiny application for playing .

This package provides an R interface to the NiftyReg image registration tools <https://github.com/KCL-BMEIS/niftyreg>. Linear and nonlinear registration are supported, in two and three dimensions.

An ODBC database interface.