The Preference Selection Index Method was created in (2010) and provides an innovative approach to determining the relative importance of criteria without pairwise comparisons, unlike the Analytic Hierarchy Process. The Preference Selection Index Method uses statistical methods to calculate the criteria weights and reflects their relative importance in the final decision-making process, offering an objective and non-subjective solution. This method is beneficial in multi-criteria decision analysis. The PSIM package provides a practical and accessible tool for implementing the Preference Selection Index Method in R. It calculates the weights of criteria and makes the method available to researchers, analysts, and professionals without the need to develop complex calculations manually. More details about the Preference Selection Index Method can be found in Maniya K. and Bhatt M. G.(2010) <doi:10.1016/j.matdes.2009.11.020>.

Allows for data to be transformed before using it to construct models. Builds structures to allow functions in the PMML package to output transformation details in addition to the model in the resulting PMML file. The Predictive Model Markup Language (PMML) is an XML-based language which provides a way for applications to define machine learning, statistical and data mining models and to share models between PMML compliant applications. More information about the PMML industry standard and the Data Mining Group can be found at <http://www.dmg.org>. The generated PMML can be imported into any PMML consuming application, such as Zementis Predictive Analytics products, which integrate with web services, relational database systems and deploy natively on Hadoop in conjunction with Hive, Spark or Storm, as well as allow predictive analytics to be executed for IBM z Systems mainframe applications and real-time, streaming analytics platforms.

Penalized orthogonal-components regression (POCRE) is a supervised dimension reduction method for high-dimensional data. It sequentially constructs orthogonal components (with selected features) which are maximally correlated to the response residuals. POCRE can also construct common components for multiple responses and thus build up latent-variable models.

This package provides functions to aid in micro and macro economic analysis and handling of price and currency data. Includes extraction of relevant inflation and exchange rate data from World Bank API, data cleaning/parsing, and standardisation. Inflation adjustment calculations as found in Principles of Macroeconomics by Gregory Mankiw et al (2014). Current and historical end of day exchange rates for 171 currencies from the European Central Bank Statistical Data Warehouse (2020).

Threshold model, panel version of Hylleberg et al. (1990) <DOI:10.1016/0304-4076(90)90080-D> seasonal unit root tests, and panel unit root test of Chang (2002) <DOI:10.1016/S0304-4076(02)00095-7>.

Given a project schedule and associated costs, this package calculates the earned value to date. It is an implementation of Project Management Body of Knowledge (PMBOK) methodologies (reference Project Management Institute. (2021). A guide to the Project Management Body of Knowledge (PMBOK guide) (7th ed.). Project Management Institute, Newtown Square, PA, ISBN 9781628256673 (pdf)).

This package provides functions for graph-based multiple-sample testing and visualization of microbiome data, in particular data stored in phyloseq objects. The tests are based on those described in Friedman and Rafsky (1979) <http://www.jstor.org/stable/2958919>, and the tests are described in more detail in Callahan et al. (2016) <doi:10.12688/f1000research.8986.1>.

Spectral emission data for some frequently used lamps including bulbs and flashlights based on led emitting diodes (LEDs) but excluding LEDs available as electronic components. Original spectral irradiance data for incandescent-, LED- and discharge lamps are included. They are complemented by data on the effect of temperature on the emission by fluorescent tubes. Part of the r4photobiology suite, Aphalo P. J. (2015) <doi:10.19232/uv4pb.2015.1.14>.

This package implements the copula-based estimator for univariate long-range dependent processes, introduced in Pumi et al. (2023) <doi:10.1007/s00362-023-01418-z>. Notably, this estimator is capable of handling missing data and has been shown to perform exceptionally well, even when up to 70% of data is missing (as reported in <arXiv:2303.04754>) and has been found to outperform several other commonly applied estimators.

Extracts features from amplification curve data of quantitative Polymerase Chain Reactions (qPCR) according to Pabinger et al. 2014 <doi:10.1016/j.bdq.2014.08.002> for machine learning purposes. Helper functions prepare the amplification curve data for processing as functional data (e.g., Hausdorff distance) or enable the plotting of amplification curve classes (negative, ambiguous, positive). The hookreg() and hookregNL() functions of Burdukiewicz et al. (2018) <doi:10.1016/j.bdq.2018.08.001> can be used to predict amplification curves with an hook effect-like curvature. The pcrfit_single() function can be used to extract features from an amplification curve.

This package provides function declarations and inline function definitions that facilitate cleaning strings in C++ code before passing them to R.

Provision of a set of models and methods for use in the allocation and management of capital in financial portfolios.

Proof of concept for implementing grammar of graphics using base plot. The bbplot() function initializes a bbplot object to store input data, aesthetic mapping, a list of layers and theme elements. The object will be rendered as a graphic using base plot command if it is printed.

Tokenizers break text into pieces that are more usable by machine learning models. Many tokenizers share some preparation steps. This package provides those shared steps, along with a simple tokenizer.

Inspects provenance collected by the rdt or rdtLite packages, or other tools providing compatible PROV JSON output created by the execution of a script, and find differences between two provenance collections. Factors under examination included the hardware and software used to execute the script, versions of attached libraries, use of global variables, modified inputs and outputs, and changes in main and sourced scripts. Based on detected changes, provExplainR can be used to study how these factors affect the behavior of the script and generate a promising diagnosis of the causes of different script results. More information about rdtLite and associated tools is available at <https://github.com/End-to-end-provenance/> and Barbara Lerner, Emery Boose, and Luis Perez (2018), Using Introspection to Collect Provenance in R, Informatics, <doi:10.3390/informatics5010012>.

Calculates the Probability Plot Correlation Coefficient (PPCC) between a continuous variable X and a specified distribution. The corresponding composite hypothesis test that was first introduced by Filliben (1975) <doi: 10.1080/00401706.1975.10489279> can be performed to test whether the sample X is element of either the Normal, log-Normal, Exponential, Uniform, Cauchy, Logistic, Generalized Logistic, Gumbel (GEVI), Weibull, Generalized Extreme Value, Pearson III (Gamma 2), Mielke's Kappa, Rayleigh or Generalized Logistic Distribution. The PPCC test is performed with a fast Monte-Carlo simulation.

In linear LS regression, calculate for a given design matrix the multiplier K of coefficient standard errors such that the confidence intervals [b - K*SE(b), b + K*SE(b)] have a guaranteed coverage probability for all coefficient estimates b in any submodels after performing arbitrary model selection.

An implementation of prediction intervals for random-effects meta-analysis: Higgins et al. (2009) <doi:10.1111/j.1467-985X.2008.00552.x>, Partlett and Riley (2017) <doi:10.1002/sim.7140>, and Nagashima et al. (2019) <doi:10.1177/0962280218773520>, <arXiv:1804.01054>.

This package provides analytic and simulation tools to estimate the minimum sample size required for achieving a target prediction mean-squared error (PMSE) or a specified proportional PMSE reduction (pPMSEr) in linear regression models. Functions implement the criteria of Ma (2023) <https://digital.wpi.edu/downloads/0g354j58c>, support covariance-matrix handling, and include helpers for root-finding and diagnostic plotting.

This package provides a simple package to grab a Bible proverb corresponding to the day of the month.

Data files and documentation for PEDiatric vALidation oF vAriableS in TBI (PEDALFAST). The data was used in "Functional Status Scale in Children With Traumatic Brain Injury: A Prospective Cohort Study" by Bennett, Dixon, et al (2016) <doi:10.1097/PCC.0000000000000934>.

This package provides an interface to PDFMiner <https://github.com/pdfminer/pdfminer.six> a Python package for extracting information from PDF'-files. PDFMiner has the goal to get all information available in a PDF'-file, position of the characters, font type, font size and informations about lines. Which makes it the perfect starting point for extracting tables from PDF'-files. More information can be found in the package README'-file.

It provides utility functions for investigating changes within R packages. The pkgInfo() function extracts package information such as exported and non-exported functions as well as their arguments. The pkgDiff() function compares this information for two versions of a package and creates a diff file viewable in a browser.

This package implements univariate polynomial operations in R, including polynomial arithmetic, finding zeros, plotting, and some operations on lists of polynomials.