Enter the query into the form above. You can look for specific version of a package by using @ symbol like this: gcc@10.
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GET /api/packages?search=hello&page=1&limit=20
where search is your query, page is a page number and limit is a number of items on a single page. Pagination information (such as a number of pages and etc) is returned
in response headers.
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Testing the equality of two means using Ranked Set Sampling and Median Ranked Set Sampling are provided under normal distribution. Data generation functions are also given RSS and MRSS. Also, data generation functions are given under imperfect ranking data for Ranked Set Sampling and Median Ranked Set Sampling. Ozdemir Y.A., Ebegil M., & Gokpinar F. (2019), <doi:10.1007/s40995-018-0558-0> Ozdemir Y.A., Ebegil M., & Gokpinar F. (2017), <doi:10.1080/03610918.2016.1263736>.
This package provides a collection of efficient and effective tools and algorithms for subgroup discovery and analytics. The package integrates an R interface to the org.vikamine.kernel library of the VIKAMINE system <http://www.vikamine.org> implementing subgroup discovery, pattern mining and analytics in Java.
This package contains functions to generate random numbers from the beta distribution and random vectors from the Dirichlet distribution.
This package provides a set of tools to process and calculate metrics on point clouds derived from terrestrial LiDAR (Light Detection and Ranging; TLS). Its creation is based on key aspects of the TLS application in forestry and ecology. Currently, the main routines are based on filtering, neighboring features of points, voxelization, canopy structure, and the creation of artificial stands. It is written using data.table and C++ language and in most of the functions it is possible to use parallel processing to speed-up the routines.
Designed for longitudinal data analysis using Hidden Markov Models (HMMs). Tailored for applications in healthcare, social sciences, and economics, the main emphasis of this package is on regularization techniques for fitting HMMs. Additionally, it provides an implementation for fitting HMMs without regularization, referencing Zucchini et al. (2017, ISBN:9781315372488).
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.
FRACTRAN is an obscure yet tantalizing programming language invented by John Conway of Game of Life fame. The code consists of a sequence of fractions. The rules are simple. First, select an integer to initialize the process. Second, multiply the integer by the first fraction. If an integer results, start again with the new integer. If not, try the next fraction. Finally, if no such multiplication yields an integer, terminate the program. For more information, see <https://en.wikipedia.org/wiki/FRACTRAN> .
The cnpy library written by Carl Rogers provides read and write facilities for files created with (or for) the NumPy extension for Python'. Vectors and matrices of numeric types can be read or written to and from files as well as compressed files. Support for integer files is available if the package has been built with as C++11 which should be the default on all platforms since the release of R 3.3.0.
Determine the number of dimensions to retain in exploratory factor analysis. The main function, nest(), returns the solution and the plot(nest()) returns a plot.
Implementation of an alternating direction method of multipliers algorithm for fitting a linear model with tree-based lasso regularization, which is proposed in Algorithm 1 of Yan and Bien (2020) <doi:10.1080/01621459.2020.1796677>. The package allows efficient model fitting on the entire 2-dimensional regularization path for large datasets. The complete set of functions also makes the entire process of tuning regularization parameters and visualizing results hassle-free.
Bundles the datasets and functions featured in Philip H. Pollock and Barry C. Edwards<https://edge.sagepub.com/pollock>, "An R Companion to Political Analysis, 3rd Edition," Thousand Oaks, CA: Sage Publications.
The RQuantLib package makes parts of QuantLib accessible from R The QuantLib project aims to provide a comprehensive software framework for quantitative finance. The goal is to provide a standard open source library for quantitative analysis, modeling, trading, and risk management of financial assets.
Finds the k nearest neighbours for every point in a given dataset using Jose Luis nanoflann library. There is support for exact searches, fixed radius searches with kd trees and two distances, the Euclidean and Manhattan'. For more information see <https://github.com/jlblancoc/nanoflann>. Also, the nanoflann library is exported and ready to be used via the linking to mechanism.
Native R only allows PDF exports of reference manuals. The Rd2md package converts the package documentation files into markdown files and combines them into a markdown version of the package reference manual.
Enhances the R Optimization Infrastructure ('ROI') package with the alabama solver for solving nonlinear optimization problems.
Minimal and lightweight configuration tool that provides basic support for YAML configuration files without requiring additional package dependencies. It offers a simple method for loading and parsing configuration settings, making it ideal for quick prototypes and lightweight projects.
This package provides functions to allow users to build and analyze design consistent tree and random forest models using survey data from a complex sample design. The tree model algorithm can fit a linear model to survey data in each node obtained by recursively partitioning the data. The splitting variables and selected splits are obtained using a randomized permutation test procedure which adjusted for complex sample design features used to obtain the data. Likewise the model fitting algorithm produces design-consistent coefficients to any specified least squares linear model between the dependent and independent variables used in the end nodes. The main functions return the resulting binary tree or random forest as an object of "rpms" or "rpms_forest" type. The package also provides methods modeling a "boosted" tree or forest model and a tree model for zero-inflated data as well as a number of functions and methods available for use with these object types.
Build interactive Reliability Probability Plots with plotly by Carson Sievert (2020) <https://plotly.com/r/>, an interactive web-based graphing library.
This framework aims to provide classes and methods for manipulating and processing of raster time series data (e.g. a time series of satellite images).
Enables the diagnostics and enhancement of regression model calibration.It offers both global and local visualization tools for calibration diagnostics and provides one recalibration method: Torres R, Nott DJ, Sisson SA, Rodrigues T, Reis JG, Rodrigues GS (2024) <doi:10.48550/arXiv.2403.05756>. The method leverages on Probabilistic Integral Transform (PIT) values to both evaluate and perform the calibration of statistical models. For a more detailed description of the package, please refer to the bachelor's thesis available bellow.
Reservoir Systems Standard Operation Policy. A system for simulation of supply reservoirs. It proposes functionalities for plotting and evaluation of supply reservoirs systems.
Build regular expressions piece by piece using human readable code. This package is designed for interactive use. For package development, use the rebus.* dependencies.
R Markdown output formats based on JavaScript libraries such as Scrollama (<https://github.com/russellsamora/scrollama>) for storytelling.
Facilitating the creation of reproducible statistical report templates. Once created, rapport templates can be exported to various external formats (HTML, LaTeX, PDF, ODT etc.) with pandoc as the converter backend.