Data sets for Chihara and Hesterberg (2022, ISBN: 978-1-119-87404-1) "Mathematical Statistics with Resampling in R" (3rd Ed).

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.

Simulate random matrices and ensembles and compute their eigenvalue spectra and dispersions.

Access to Boost Date_Time functionality for dates, durations (both for days and date time objects), time zones, and posix time ('ptime') is provided by using Rcpp modules'. The posix time implementation can support high-resolution of up to nano-second precision by using 96 bits (instead of 64 with R) to present a ptime object (but this needs recompilation with a #define set).

This package creates and maintains a build process for complex analytic tasks in R. Package allows to easily generate Makefile for the (GNU) make tool, which drives the build process by (in parallel) executing build commands in order to update results accordingly to given dependencies on changed data or updated source files.

This package contains functions for analysing relative survival data, including nonparametric estimators of net (marginal relative) survival, relative survival ratio, crude mortality, methods for fitting and checking additive and multiplicative regression models, transformation approach, methods for dealing with population mortality tables. Work has been described in Pohar Perme, Pavlic (2018) <doi:10.18637/jss.v087.i08>.

Assists in statistical model building to find optimal and semi-optimal higher order interactions and best subsets. Uses the lm(), glm(), and other R functions to fit models generated from a feasible solution algorithm. Discussed in Subset Selection in Regression, A Miller (2002). Applied and explained for least median of squares in Hawkins (1993) <doi:10.1016/0167-9473(93)90246-P>. The feasible solution algorithm comes up with model forms of a specific type that can have fixed variables, higher order interactions and their lower order terms.

Perform wavelet analysis (orthogonal,translation invariant, tensorial, 1-2-3d transforms, thresholding, block thresholding, linear,...) with applications to data compression or denoising/regression. The core of the code is a port of MATLAB Wavelab toolbox written by D. Donoho, A. Maleki and M. Shahram (<https://statweb.stanford.edu/~wavelab/>).

The aim of the report package is to bridge the gap between Râ s output and the formatted results contained in your manuscript. This package converts statistical models and data frames into textual reports suited for publication, ensuring standardization and quality in results reporting.

This package implements reversal association pattern analysis for categorical data. Detects sub-tables exhibiting reversal associations in contingency tables, provides visualization tools, and supports simulation-based validation for complex I Ã J tables.

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.

An R Commander "plug-in" extending functionality of linear models and providing an interface to Partial Least Squares Regression and Linear and Quadratic Discriminant analysis. Several statistical summaries are extended, predictions are offered for additional types of analyses, and extra plots, tests and mixed models are available.

This package provides tools for working with rotational data, including simulation from the most commonly used distributions on SO(3), methods for different Bayes, mean and median type estimators for the central orientation of a sample, confidence/credible regions for the central orientation based on those estimators and a novel visualization technique for rotation data. Most recently, functions to identify potentially discordant (outlying) values have been added. References: Bingham, Melissa A. and Nordman, Dan J. and Vardeman, Steve B. (2009), Bingham, Melissa A and Vardeman, Stephen B and Nordman, Daniel J (2009), Bingham, Melissa A and Nordman, Daniel J and Vardeman, Stephen B (2010), Leon, C.A. and Masse, J.C. and Rivest, L.P. (2006), Hartley, R and Aftab, K and Trumpf, J. (2011), Stanfill, Bryan and Genschel, Ulrike and Hofmann, Heike (2013), Maonton, Jonathan (2004), Mardia, KV and Jupp, PE (2000, ISBN:9780471953333), Rancourt, D. and Rivest, L.P. and Asselin, J. (2000), Chang, Ted and Rivest, Louis-Paul (2001), Fisher, Nicholas I. (1996, ISBN:0521568900).

General purpose R client for ERDDAPâ ¢ servers. Includes functions to search for datasets', get summary information on datasets', and fetch datasets', in either csv or netCDF format. ERDDAPâ ¢ information: <https://upwell.pfeg.noaa.gov/erddap/information.html>.

For whole-genome analysis, idiograms are virtually the most intuitive and effective way to map and visualize the genome-wide information. RIdeogram was developed to visualize and map whole-genome data on idiograms with no restriction of species.

Useful tools for determining whether two samples are from the same distribution. Utilizes a robust method to address the problematic structure of the similarity graph constructed from high-dimensional data. The method is provided in Yichuan Bai and Lynna Chu (2023) <arXiv:2307.12325>.

Interface to JDemetra+ 3.x (<https://github.com/jdemetra>) time series analysis software. It offers full access to txt, csv, xml and spreadsheets files which are meant to be read by JDemetra+ Graphical User Interface.

This package provides tools to automate the morphological delineation of riverside urban areas based on a method introduced in Forgaci (2018) <doi:10.7480/abe.2018.31>. Delineation entails the identification of corridor boundaries, segmentation of the corridor, and delineation of the river space using two-dimensional spatial information from street network data and digital elevation data in a projected CRS. The resulting delineation can be used to characterise spatial phenomena that can be related to the river as a central element.

This package provides tools for robust regression model fitting using the RANSAC (Random Sample Consensus) algorithm. RANSAC is an iterative method to estimate parameters of a model from a dataset that contains outliers. This package allows fitting both linear lm and nonlinear nls models using RANSAC, helping users obtain more reliable models in the presence of noisy or corrupted data. The methods are particularly useful in contexts where traditional least squares regression fails due to the influence of outliers. Implementations include support for performance metrics such as RMSE, MAE, and R² based on the inlier subset. For further details, see Fischler and Bolles (1981) <doi:10.1145/358669.358692>.

Create plots to visualize the alignment of a corporate lending financial portfolio to climate change scenarios based on climate indicators (production and emission intensities) across key climate relevant sectors of the PACTA methodology (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 SPRITE algorithm creates possible distributions of discrete responses based on reported sample parameters, such as mean, standard deviation and range (Heathers et al., 2018, <doi:10.7287/peerj.preprints.26968v1>). This package implements it, drawing heavily on the code for Nick Brown's rSPRITE Shiny app <https://shiny.ieis.tue.nl/sprite/>. In addition, it supports the modeling of distributions based on multi-item (Likert-type) scales and the use of restrictions on the frequency of particular responses.

Search by keywords in R packages, task views, CRAN, the web and display the results in the console or in txt, html or pdf files. Download the package documentation (html index, README, NEWS, pdf manual, vignettes, source code, binaries) with a single instruction. Visualize the package dependencies and CRAN checks. Compare the package versions, unload and install the packages and their dependencies in a safe order. Explore CRAN archives. Use the above functions for task view maintenance. Access web search engines from the console thanks to 80+ bookmarks. All functions accept standard and non-standard evaluation.

We provide an implementation for Sum of Ranking Differences (SRD), a novel statistical test introduced by Héberger (2010) <doi:10.1016/j.trac.2009.09.009>. The test allows the comparison of different solutions through a reference by first performing a rank transformation on the input, then calculating and comparing the distances between the solutions and the reference - the latter is measured in the L1 norm. The reference can be an external benchmark (e.g. an established gold standard) or can be aggregated from the data. The calculated distances, called SRD scores, are validated in two ways, see Héberger and Kollár-Hunek (2011) <doi:10.1002/cem.1320>. A randomization test (also called permutation test) compares the SRD scores of the solutions to the SRD scores of randomly generated rankings. The second validation option is cross-validation that checks whether the rankings generated from the solutions come from the same distribution or not. For a detailed analysis about the cross-validation process see Sziklai, Baranyi and Héberger (2021) <doi:10.48550/arXiv.2105.11939>. The package offers a wide array of features related to SRD including the computation of the SRD scores, validation options, input preprocessing and plotting tools.

Analyze download logs from the CRAN RStudio mirror (<http://cran.rstudio.com/>). This CRAN mirror is the default one used in RStudio. The available data is the result of parsed and anonymised raw log data from that CRAN mirror.