This package provides a platform-independent GUI for design of experiments. The package is implemented as a plugin to the R-Commander, which is a more general graphical user interface for statistics in R based on tcl/tk. DoE functionality can be accessed through the menu Design that is added to the R-Commander menus.

An interactive web application for reliability analysis using the shiny <https://shiny.posit.co/> framework. The app provides an easy-to-use interface for performing reliability analysis using WeibullR <https://cran.r-project.org/package=WeibullR> and ReliaGrowR <https://cran.r-project.org/package=ReliaGrowR>.

Use rprofile::load() inside a project .Rprofile file to ensure that the user-global .Rprofile is loaded correctly regardless of its location, and other common resources (in particular renv') are also set up correctly.

This package provides tools for working with Type S (Sign) and Type M (Magnitude) errors, as proposed in Gelman and Tuerlinckx (2000) <doi:10.1007/s001800000040> and Gelman & Carlin (2014) <doi:10.1177/1745691614551642>. In addition to simply calculating the probability of Type S/M error, the package includes functions for calculating these errors across a variety of effect sizes for comparison, and recommended sample size given "tolerances" for Type S/M errors. To improve the speed of these calculations, closed forms solutions for the probability of a Type S/M error from Lu, Qiu, and Deng (2018) <doi:10.1111/bmsp.12132> are implemented. As of 1.0.0, this includes support only for simple research designs. See the package vignette for a fuller exposition on how Type S/M errors arise in research, and how to analyze them using the type of design analysis proposed in the above papers.

This package provides a simple and efficient way to read data from Paradox database files (.db) directly into R as modern tibble data frames. It uses the underlying pxlib C library, to handle the low-level file format details and provides a clean, user-friendly R interface.

Simple and fast tool for transforming phytosociological vegetation data into digital form for the following analysis. Danihelka, Chrtek, and Kaplan (2012, ISSN:00327786). Hennekens, and Schaminée (2001) <doi:10.2307/3237010>. Tichý (2002) <doi:10.1111/j.1654-1103.2002.tb02069.x>. Wickham, François, Henry, Müller (2022) <https://CRAN.R-project.org/package=dplyr>.

An implementation of algorithms for estimation of the graphical lasso regularization parameter described in Pedro Cisneros-Velarde, Alexander Petersen and Sang-Yun Oh (2020) <http://proceedings.mlr.press/v108/cisneros20a.html>.

Easy installation, loading, and control of packages for redistricting data downloading, spatial data processing, simulation, analysis, and visualization. This package makes it easy to install and load multiple redistverse packages at once. The redistverse is developed and maintained by the Algorithm-Assisted Redistricting Methodology (ALARM) Project. For more details see <https://alarm-redist.org>.

Interface to the flsgen neutral landscape generator <https://github.com/dimitri-justeau/flsgen>. It allows to - Generate fractal terrain; - Generate landscape structures satisfying user targets over landscape indices; - Generate landscape raster from landscape structures.

Linear and logistic ridge regression functions. Additionally includes special functions for genome-wide single-nucleotide polymorphism (SNP) data. More details can be found in <doi: 10.1002/gepi.21750> and <doi: 10.1186/1471-2105-12-372>.

This package provides functions for reading data sets in different formats for testing machine learning tools are provided. This allows to run a loop over several data sets in their original form, for example if they are downloaded from UCI Machine Learning Repository. The data are not part of the package and have to be downloaded separately.

Robust Clustering of Time Series (RCTS) has the functionality to cluster time series using both the classical and the robust interactive fixed effects framework. The classical framework is developed in Ando & Bai (2017) <doi:10.1080/01621459.2016.1195743>. The implementation within this package excludes the SCAD-penalty on the estimations of beta. This robust framework is developed in Boudt & Heyndels (2022) <doi:10.1016/j.ecosta.2022.01.002> and is made robust against different kinds of outliers. The algorithm iteratively updates beta (the coefficients of the observable variables), group membership, and the latent factors (which can be common and/or group-specific) along with their loadings. The number of groups and factors can be estimated if they are unknown.

Implementation of Nelson rules for control charts in R'. The Rspc implements some Statistical Process Control methods, namely Levey-Jennings type of I (individuals) chart, Shewhart C (count) chart and Nelson rules (as described in Montgomery, D. C. (2013) Introduction to statistical quality control. Hoboken, NJ: Wiley.). Typical workflow is taking the time series, specify the control limits, and list of Nelson rules you want to evaluate. There are several options how to modify the rules (one sided limits, numerical parameters of rules, etc.). Package is also capable of calculating the control limits from the data (so far only for i-chart and c-chart are implemented).

This package provides a programmatic interface to web-services of YouTheria. YouTheria is an online database of mammalian trait data <http://www.utheria.org/>.

Collection of tools to calculate portfolio performance metrics. Portfolio performance is a key measure for investors. These metrics are important to analyse how effectively their money has been invested. This package uses portfolio theories to give investor tools to evaluate their portfolio performance. For more information see, Markowitz, H.M. (1952), <doi:10.2307/2975974>. Analysis of Investments & Management of Portfolios [2012, ISBN:978-8131518748].

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.

Praat <https://www.fon.hum.uva.nl/praat/> is a widely used tool for manipulating, annotating and analyzing speech and acoustic data. It stores annotation data in a format called a TextGrid'. This package provides a way to read these files into R.

This package provides realistic synthetic example datasets for the R4SUB (R for Regulatory Submission) ecosystem. Includes a pharma study evidence table, ADaM (Analysis Data Model) and SDTM (Study Data Tabulation Model) metadata following CDISC (Clinical Data Interchange Standards Consortium) conventions (<https://www.cdisc.org>), traceability mappings, a risk register based on ICH (International Council for Harmonisation) Q9 quality risk management principles (<https://www.ich.org/page/quality-guidelines>), and regulatory indicator definitions. Designed for demos, vignettes, and package testing.

Estimates Pareto-optimal solution for personnel selection with 3 objectives using Normal Boundary Intersection (NBI) algorithm introduced by Das and Dennis (1998) <doi:10.1137/S1052623496307510>. Takes predictor intercorrelations and predictor-objective relations as input and generates a series of solutions containing predictor weights as output. Accepts between 3 and 10 selection predictors. Maximum 2 objectives could be adverse impact objectives. Partially modeled after De Corte (2006) TROFSS Fortran program <https://users.ugent.be/~wdecorte/trofss.pdf> and updated from ParetoR package described in Song et al. (2017) <doi:10.1037/apl0000240>. For details, see Study 3 of Zhang et al. (2023).

This package provides an interactive wrapper for the tmpinv() function from the rtmpinv package with options extending its functionality to pre- and post-estimation processing and streamlined incorporation of prior cell information. The Tabular Matrix Problems via Pseudoinverse Estimation (TMPinv) is a two-stage estimation method that reformulates structured table-based systems - such as allocation problems, transaction matrices, and input-output tables - as structured least-squares problems. Based on the Convex Least Squares Programming (CLSP) framework, TMPinv solves systems with row and column constraints, block structure, and optionally reduced dimensionality by (1) constructing a canonical constraint form and applying a pseudoinverse-based projection, followed by (2) a convex-programming refinement stage to improve fit, coherence, and regularization (e.g., via Lasso, Ridge, or Elastic Net).

Routines to select and visualize the maxima for a given strict partial order. This especially includes the computation of the Pareto frontier, also known as (Top-k) Skyline operator (see Börzsönyi, et al. (2001) <doi:10.1109/ICDE.2001.914855>), and some generalizations known as database preferences (see Kieà ling (2002) <doi:10.1016/B978-155860869-6/50035-4>).

Allows work with Management API for load counters, segments, filters, user permissions and goals list from Yandex Metrica, Reporting API allows you to get information about the statistics of site visits and other data without using the web interface, Logs API allows to receive non-aggregated data and Compatible with Google Analytics Core Reporting API v3 allows receive information about site traffic and other data using field names from Google Analytics Core API. For more information see official documents <https://yandex.ru/dev/metrika/doc/api2/concept/about-docpage>.

Adds menu items to the R Commander for parametric analysis of dichotomous choice contingent valuation (DCCV) data. CV is a question-based survey method to elicit individuals preferences for goods and services. This package depends on functions regarding parametric DCCV analysis in the package DCchoice. See Carson and Hanemann (2005) <doi:10.1016/S1574-0099(05)02017-6> for DCCV.

Fits linear models to repeated ordinal scores using GEE methodology.