Automated performance of common transformations used to fulfill parametric assumptions of normality and identification of the best performing method for the user. Output for various normality tests (Thode, 2002) corresponding to the best performing method and a descriptive statistical report of the input data in its original units (5-number summary and mathematical moments) are also presented. Lastly, the Rankit, an empirical normal quantile transformation (ENQT) (Soloman & Sawilowsky, 2009), is provided to accommodate non-standard use cases and facilitate adoption. <DOI: 10.1201/9780203910894>. <DOI: 10.22237/jmasm/1257034080>.

Load data by campaigns, ads, ad sets and insights, ad account and business manager from Facebook Marketing API into R. For more details see official documents by Facebook Marketing API <https://developers.facebook.com/docs/marketing-api>.

In order to facilitate R instruction for actuaries, we have organized several sets of publicly available data of interest to non-life actuaries. In addition, we suggest a set of packages, which most practicing actuaries will use routinely. Finally, there is an R markdown skeleton for basic reserve analysis.

This package provides a model agnostic tool for white-box model trained on features extracted from a black-box model. For more information see: Gosiewska et al. (2020) <doi:10.1016/j.dss.2021.113556>.

We provide functions to perform taxometric analyses. This package contains 46 functions, but only 5 should be called directly by users. CheckData() should be run prior to any taxometric analysis to ensure that the data are appropriate for taxometric analysis. RunTaxometrics() performs taxometric analyses for a sample of data. RunCCFIProfile() performs a series of taxometric analyses to generate a CCFI profile. CreateData() generates a sample of categorical or dimensional data. ClassifyCases() assigns cases to groups using the base-rate classification method.

This package provides a colour list and colour metric based on the ISCC-NBS System of Color Designation for use with the roloc package for converting colour specifications to colour names.

Receiver Operating Characteristic (ROC) analysis is performed assuming samples are from the proposed distributions. In addition, the volume under the ROC surface and true positive fractions values are evaluated by ROC surface analysis.

The quantitative measurement and detection of molecules in HPLC should be carried out by an accurate description of chromatographic peaks. In this package non-linear fitting using a modified Gaussian model with a parabolic variance (PVMG) has been implemented to obtain the retention time and height at the peak maximum. This package also includes the traditional Van Deemter approach and two alternatives approaches to characterize chromatographic column.

Multiple interactive codes to view and analyze seismic data, via spectrum analysis, wavelet transforms, particle motion, hodograms. Includes general time-series tools, plotting, filtering, interactive display.

This package provides a single key function, Require that makes rerun-tolerant versions of install.packages and `require` for CRAN packages, packages no longer on CRAN (i.e., archived), specific versions of packages, and GitHub packages. This approach is developed to create reproducible workflows that are flexible and fast enough to use while in development stages, while able to build snapshots once a stable package collection is found. As with other functions in a reproducible workflow, this package emphasizes functions that return the same result whether it is the first or subsequent times running the function, with subsequent times being sufficiently fast that they can be run every time without undue waiting burden on the user or developer.

Download large sections of GenBank <https://www.ncbi.nlm.nih.gov/genbank/> and generate a local SQL-based database. A user can then query this database using restez functions or through rentrez <https://CRAN.R-project.org/package=rentrez> wrappers.

Datasets with energy consumption data of different data measurement frequencies. The data stems from several publicly funded research projects of the Chair of Information Systems and Energy Efficient Systems at the University of Bamberg.

This package implements the "Stemming Algorithm for the Portuguese Language" <DOI:10.1109/SPIRE.2001.10024>.

This package provides a programmatic interface to the Web Service methods provided by ITALIC (<https://italic.units.it>). ITALIC is a database of lichen data in Italy and bordering European countries. ritalic includes functions for retrieving information about lichen scientific names, geographic distribution, ecological data, morpho-functional traits and identification keys. More information about the data is available at <https://italic.units.it/?procedure=base&t=59&c=60>. The API documentation is available at <https://italic.units.it/?procedure=api>.

Implementation of the Johnson Quantile-Parameterised Distribution in R. The Johnson Quantile-Parameterised Distribution (J-QPD) is a flexible distribution system that is parameterised by a symmetric percentile triplet of quantile values (typically the 10th-50th-90th) along with known support bounds for the distribution. The J-QPD system was developed by Hadlock and Bickel (2017) <doi:10.1287/deca.2016.0343>. This package implements the density, quantile, CDF and random number generator functions.

This package provides a collection of efficient implementations of popular offline change-point detection algorithms, featuring a consistent, object-oriented interface for practical use.

Microbenchmarks for determining the run time performance of aspects of the R programming environment and packages relevant to high-performance computation. The benchmarks are divided into three categories: dense matrix linear algebra kernels, sparse matrix linear algebra kernels, and machine learning functionality.

Polynomially bounded algorithms to aggregate complete rankings under Kemeny's axiomatic framework. RankAggSIgFUR (pronounced as rank-agg-cipher) contains two heuristics algorithms: FUR and SIgFUR. For details, please see Badal and Das (2018) <doi:10.1016/j.cor.2018.06.007>.

This package performs Random Subspace Method (RSM) for high-dimensional linear regression to obtain variable importance measures. The final model is chosen based on validation set or Generalized Information Criterion.

The basic algorithm to perform the folding test of unimodality. Given a dataset X (d dimensional, n samples), the test checks whether the distribution of the data are rather unimodal or rather multimodal. This package stems from the following research publication: Siffer Alban, Pierre-Alain Fouque, Alexandre Termier, and Christine Largouët. "Are your data gathered?" In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery Data Mining, pp. 2210-2218. ACM, 2018. <doi:10.1145/3219819.3219994>.

Estimates robust rank-based fixed effects and predicts robust random effects in two- and three- level random effects nested models. The methodology is described in Bilgic & Susmann (2013) <https://journal.r-project.org/archive/2013/RJ-2013-027/>.

Enhances the R Optimization Infrastructure ('ROI') package by registering the ipop solver from package kernlab'.

Takes user-provided baseline data from groups of randomised controlled data and assesses whether the observed distribution of baseline p-values, numbers of participants in each group, or categorical variables are consistent with the expected distribution, as an aid to the assessment of integrity concerns in published randomised controlled trials. References (citations in PubMed format in details of each function): Bolland MJ, Avenell A, Gamble GD, Grey A. (2016) <doi:10.1212/WNL.0000000000003387>. Bolland MJ, Gamble GD, Avenell A, Grey A, Lumley T. (2019) <doi:10.1016/j.jclinepi.2019.05.006>. Bolland MJ, Gamble GD, Avenell A, Grey A. (2019) <doi:10.1016/j.jclinepi.2019.03.001>. Bolland MJ, Gamble GD, Grey A, Avenell A. (2020) <doi:10.1111/anae.15165>. Bolland MJ, Gamble GD, Avenell A, Cooper DJ, Grey A. (2021) <doi:10.1016/j.jclinepi.2020.11.012>. Bolland MJ, Gamble GD, Avenell A, Grey A. (2021) <doi:10.1016/j.jclinepi.2021.05.002>. Bolland MJ, Gamble GD, Avenell A, Cooper DJ, Grey A. (2023) <doi:10.1016/j.jclinepi.2022.12.018>. Carlisle JB, Loadsman JA. (2017) <doi:10.1111/anae.13650>. Carlisle JB. (2017) <doi:10.1111/anae.13938>.

Exchange rate for Kenya Shilling against other currencies, US DOLLAR, EURO, STERLING POUND, Tanzania Shilling, Uganda Shilling.