C++ classes to embed R in C++ (and C) applications A C++ class providing the R interpreter is offered by this package making it easier to have "R inside" your C++ application. As R itself is embedded into your application, a shared library build of R is required. This works on Linux, OS X and even on Windows provided you use the same tools used to build R itself. Numerous examples are provided in the nine subdirectories of the examples/ directory of the installed package: standard, mpi (for parallel computing), qt (showing how to embed RInside inside a Qt GUI application), wt (showing how to build a "web-application" using the Wt toolkit), armadillo (for RInside use with RcppArmadillo'), eigen (for RInside use with RcppEigen'), and c_interface for a basic C interface and Ruby illustration. The examples use GNUmakefile(s) with GNU extensions, so a GNU make is required (and will use the GNUmakefile automatically). Doxygen'-generated documentation of the C++ classes is available at the RInside website as well.

This package provides a resource represents some data or a computation unit. It is described by a URL and credentials. This package proposes a Resource model with "resolver" and "client" classes to facilitate the access and the usage of the resources.

Generate utils::globalVariables() from roxygen2 @global and @autoglobal tags.

We introduce a robust matrix factor model that explicitly incorporates tail behavior and employs a mean-shift term to avoid efficiency losses through pre-centering of observed matrices. More details on the methods related to our paper are currently under submission. A full reference to the paper will be provided in future versions once the paper is published.

This package provides tools for generation of (life-history) traits and diversity maps on hexagonal or square grids. Valcu et al.(2012) <doi:10.1111/j.1466-8238.2011.00739.x>.

Mappable vector library provides convenient way to access large datasets. Use all of your data at once, with few limits. Memory mapped data can be shared between multiple R processes. Access speed depends on storage medium, so solid state drive is recommended, preferably with PCI Express (or M.2 nvme) interface or a fast network file system. The data is memory mapped into R and then accessed using usual R list and array subscription operators. Convenience functions are provided for merging, grouping and indexing large vectors and data.frames. The layout of underlying MVL files is optimized for large datasets. The vectors are stored to guarantee alignment for vector intrinsics after memory map. The package is built on top of libMVL, which can be used as a standalone C library. libMVL has simple C API making it easy to interchange datasets with outside programs. Large MVL datasets are distributed via Academic Torrents <https://academictorrents.com/collection/mvl-datasets>.

Load data from vk.com api about your communiti users and views, ads performance, post on user wall and etc. For more information see API Documentation <https://vk.com/dev/first_guide>.

Adds menu items to the R Commander for implementing case 1 (object case) best-worst scaling (BWS1) from designing choice sets to measuring preferences for items. BWS1 is a question-based survey method that constructs various combinations of items (choice sets) using the experimental designs, asks respondents to select the best and worst items in each choice set, and then measures preferences for the items by analyzing the responses. For details, refer to Aizaki and Fogarty (2023) <doi:10.1016/j.jocm.2022.100394>.

An algorithm is proposed to estimate regression kink model proposed by the paper, Lixiong Yang and Jen-Je Su (2018) <doi:10.1016/j.jimonfin.2018.06.002>.

Calculate common survey data quality indicators for multi-item scales and matrix questions. Currently supports the calculation of response style indicators and response distribution indicators. For an overview on response quality indicators see Bhaktha N, Henning S, Clemens L (2024). Characterizing response quality in surveys with multi-item scales: A unified framework <https://osf.io/9gs67/>.

This package provides a lightweight implementation of the geomorphon terrain form classification algorithm of Jasiewicz and Stepinski (2013) <doi:10.1016/j.geomorph.2012.11.005> based largely on the GRASS GIS r.geomorphon module. This implementation employs a novel algorithm written in C++ and RcppParallel'.

This package provides functions for cleaning and summarising water quality data for use in National Pollutant Discharge Elimination Service (NPDES) permit reasonable potential analyses and water quality-based effluent limitation calculations. Procedures are based on those contained in the "Technical Support Document for Water Quality-based Toxics Control", United States Environmental Protection Agency (1991).

This package provides a series of functions that allow users to access the LinkedIn API to get information about connections, search for people and jobs, share updates with their network, and create group discussions. For more information about using the API please visit <https://developer.linkedin.com/>.

This package provides methods and tools for implementing regularized multivariate functional principal component analysis ('ReMFPCA') for multivariate functional data whose variables might be observed over different dimensional domains. ReMFPCA is an object-oriented interface leveraging the extensibility and scalability of R6. It employs a parameter vector to control the smoothness of each functional variable. By incorporating smoothness constraints as penalty terms within a regularized optimization framework, ReMFPCA generates smooth multivariate functional principal components, offering a concise and interpretable representation of the data. For detailed information on the methods and techniques used in ReMFPCA', please refer to Haghbin et al. (2023) <doi:10.48550/arXiv.2306.13980>.

Search R files for not installed packages and run install.packages.

This package provides a comprehensive suite of statistical tools for Quality Management, designed around the Define, Measure, Analyze, Improve, and Control (DMAIC) cycle used in Six Sigma methodology. Based on the discontinued CRAN package qualitytools', this package refactors its original design by incorporating R6 object-oriented programming for increased flexibility and performance. It replaces traditional graphics with modern, interactive visualizations using ggplot2 and plotly'. Built on tidyverse principles, it simplifies data manipulation and visualization, offering an intuitive approach to quality science.

Mixture Composer (Biernacki (2015) <https://inria.hal.science/hal-01253393v1>) is a project to perform clustering using mixture models with heterogeneous data and partially missing data. Mixture models are fitted using a SEM algorithm. It includes 8 models for real, categorical, counting, functional and ranking data.

This package creates JavaScript charts with the nvd3 library. So far only the multibar chart, the horizontal multibar chart, the line chart and the line chart with focus are available.

Reversion mutations are secondary mutations that reverse the deleterious effects of an original pathogenic mutation, partially or fully restoring the gene's function. The revert package detects reversion mutations for a specific pathogenic mutation from DNA-seq bam files.

Utility functions to download data from the RESOURCECODE hindcast database of sea-states, time series of sea-state parameters and time series of 1D and 2D wave spectra. See <https://resourcecode.ifremer.fr> for more details about the available data. Also provides facilities to plot and analyse downloaded data, such as computing the sea-state parameters from both the 1D and 2D surface elevation variance spectral density.

An implementation of a number of Global Trend models for time series forecasting that are Bayesian generalizations and extensions of some Exponential Smoothing models. The main differences/additions include 1) nonlinear global trend, 2) Student-t error distribution, and 3) a function for the error size, so heteroscedasticity. The methods are particularly useful for short time series. When tested on the well-known M3 dataset, they are able to outperform all classical time series algorithms. The models are fitted with MCMC using the rstan package.

The rearrangement operator (Hardy, Littlewood, and Polya 1952) for univariate, bivariate, and trivariate point estimates of monotonic functions. The package additionally provides a function that creates simultaneous confidence intervals for univariate functions and applies the rearrangement operator to these confidence intervals.

Tu & Zhou (1999) <doi:10.1002/(SICI)1097-0258(19991030)18:20%3C2749::AID-SIM195%3E3.0.CO;2-C> showed that comparing the means of populations whose data-generating distributions are non-negative with excess zero observations is a problem of great importance in the analysis of medical cost data. In the same study, Tu & Zhou discuss that it can be difficult to control type-I error rates of general-purpose statistical tests for comparing the means of these particular data sets. This package allows users to perform a modified bootstrap-based t-test that aims to better control type-I error rates in these situations.

This package provides an efficient procedure for fitting the entire solution path for high-dimensional regularized quadratic generalized linear models with interactions effects under the strong or weak heredity constraint.