ARFIMA, in-mean, external regressors and various GARCH flavors, with methods for fit, forecast, simulation, inference and plotting.

The Brazilian Central Bank API delivers many datasets which regard economic activity, regional economy, international economy, public finances, credit indicators and many more. For more information please see <http://dadosabertos.bcb.gov.br/>. These datasets can be accessed through rbcb functions and can be obtained in different data structures common to R ('tibble', data.frame', xts', ...).

This package provides methods for comparing different regression algorithms for describing the temporal dynamics of secondary tree growth (xylem and phloem). Users can compare the accuracy of the most common fitting methods usually used to analyse xylem and phloem data, i.e., Gompertz function, Double Gompertz function, General Additive Models (GAMs); and an algorithm newly introduced to the field, i.e., Bayesian Regularised Neural Networks (brnn). The core function of the package is XPSgrowth(), while the results can be interpreted using implemented generic S3 methods, such as plot() and summary().

Radioactive doses estimation using individual chromosomal aberrations information. See Higueras M, Puig P, Ainsbury E, Rothkamm K. (2015) <doi:10.1088/0952-4746/35/3/557>.

Robust tail dependence estimation for bivariate models. This package is based on two papers by the authors:'Robust and bias-corrected estimation of the coefficient of tail dependence and Robust and bias-corrected estimation of probabilities of extreme failure sets'. This work was supported by a research grant (VKR023480) from VILLUM FONDEN and an international project for scientific cooperation (PICS-6416).

This package implements two methods of estimating runs scored in a softball scenario: (1) theoretical expectation using discrete Markov chains and (2) empirical distribution using multinomial random simulation. Scores are based on player-specific input probabilities (out, single, double, triple, walk, and homerun). Optional inputs include probability of attempting a steal, probability of succeeding in an attempted steal, and an indicator of whether a player is "fast" (e.g. the player could stretch home). These probabilities may be calculated from common player statistics that are publicly available on team's webpages. Scores are evaluated based on a nine-player lineup and may be used to compare lineups, evaluate base scenarios, and compare the offensive potential of individual players. Manuscript forthcoming. See Bukiet & Harold (1997) <doi:10.1287/opre.45.1.14> for implementation of discrete Markov chains.

The provided package implements the statistical tests for the functional repeated measures analysis problem (Kurylo and Smaga, 2023, <arXiv:2306.03883>). These procedures enable us to verify the overall hypothesis regarding equality, as well as hypotheses for pairwise comparisons (i.e., post hoc analysis) of mean functions corresponding to repeated experiments.

R wrapper for the JPMML-R library <https://github.com/jpmml/jpmml-r>, which converts R models to Predictive Model Markup Language (PMML).

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 a collection of data sets relating to ADHD (Attention Deficit Hyperactivity Disorder) which have been sourced from other packages on CRAN or from publications on other websites such as Kaggle <http://www.kaggle.com/>.The package also includes some simple functions for analysing data sets. The data sets and descriptions of the data sets may differ from what is on CRAN or other source websites. The aim of this package is to bring together data sets from a variety of ADHD research publications. This package would be useful for those interested in finding out what research has been done on the topic of ADHD, or those interested in comparing the results from different existing works. I started this project because I wanted to put together a collection of the data sets relevant to ADHD research, which I have a personal interest in. This work was conducted with the support of my mentor within the Global Talent Mentoring platform. <https://globaltalentmentoring.org/>.

QuantLib bindings are provided for R using Rcpp via an updated variant of the header-only Quantuccia project (put together initially by Peter Caspers) offering an essential subset of QuantLib (and now maintained separately for the calendaring subset). See the included file AUTHORS for a full list of contributors to both QuantLib and Quantuccia'.

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.

Adds menu items to the R Commander for implementing case 1 (object case) best-worst scaling (BWS1) from designing choice sets to measuring preferences for items. BWS1 is a question-based survey method that constructs various combinations of items (choice sets) using the experimental designs, asks respondents to select the best and worst items in each choice set, and then measures preferences for the items by analyzing the responses. For details, refer to Aizaki and Fogarty (2023) <doi:10.1016/j.jocm.2022.100394>.

The main purpose of this package is to streamline the generation of exams that include random elements in exercises. Exercises can be defined in a table, based on text and figures, and may contain gaps to be filled with provided options. Exam documents can be generated in various formats. It allows us to generate a version for conducting the assessment and another version that facilitates correction, linked through a code.

R utilities for gff files, either general feature format (GFF3) or gene transfer format (GTF) formatted files. This package includes functions for producing summary stats, check for consistency and sorting errors, conversion from GTF to GFF3 format, file sorting, visualization and plotting of feature hierarchy, and exporting user defined feature subsets to SAF format. This tool was developed by the BioinfoGP core facility at CNB-CSIC.

Graphics for statistics on a sphere, as applied to geological fault data, crystallography, earthquake focal mechanisms, radiation patterns, ternary plots and geographical/geological maps. Non-double couple plotting of focal spheres and source type maps are included for statistical analysis of moment tensors.

We utilize approximate Bayesian machinery to fit two-level conjugate hierarchical models on overdispersed Gaussian, Poisson, and Binomial data and evaluates whether the resulting approximate Bayesian interval estimates for random effects meet the nominal confidence levels via frequency coverage evaluation. The data that Rgbp assumes comprise observed sufficient statistic for each random effect, such as an average or a proportion of each group, without population-level data. The approximate Bayesian tool equipped with the adjustment for density maximization produces approximate point and interval estimates for model parameters including second-level variance component, regression coefficients, and random effect. For the Binomial data, the package provides an option to produce posterior samples of all the model parameters via the acceptance-rejection method. The package provides a quick way to evaluate coverage rates of the resultant Bayesian interval estimates for random effects via a parametric bootstrapping, which we call frequency method checking.

Algorithms for solving a self-calibrated l1-regularized quadratic programming problem without parameter tuning. The algorithm, called DECODE, can handle high-dimensional data without cross-validation. It is found useful in high dimensional portfolio selection (see Pun (2018) <https://ssrn.com/abstract=3179569>) and large precision matrix estimation and sparse linear discriminant analysis (see Pun and Hadimaja (2019) <https://ssrn.com/abstract=3422590>).

Simulation of random orthonormal matrices from linear and quadratic exponential family distributions on the Stiefel manifold. The most general type of distribution covered is the matrix-variate Bingham-von Mises-Fisher distribution. Most of the simulation methods are presented in Hoff(2009) "Simulation of the Matrix Bingham-von Mises-Fisher Distribution, With Applications to Multivariate and Relational Data" <doi:10.1198/jcgs.2009.07177>. The package also includes functions for optimization on the Stiefel manifold based on algorithms described in Wen and Yin (2013) "A feasible method for optimization with orthogonality constraints" <doi:10.1007/s10107-012-0584-1>.

Easy to use interface for conducting meta-analysis in R. This package is an Rcmdr-plugin, which allows the user to conduct analyses in a menu-driven, graphical user interface environment (e.g., CMA, SPSS). It uses recommended procedures as described in The Handbook of Research Synthesis and Meta-Analysis (Cooper, Hedges, & Valentine, 2009).

An R Interface to EPP-lab v1.0. EPP-lab is a Java program for projection pursuit using genetic algorithms written by Alain Berro and S. Larabi Marie-Sainte and is included in the package.

Supports analysis of spatial data processed with the GeoPAT 2 software <https://github.com/Nowosad/geopat2>. Available features include creation of a grid based on the GeoPAT 2 grid header file and reading a GeoPAT 2 text outputs.

This package provides a list of functions for the statistical analysis and the post-processing of the Markov Chains simulated by ChronoModel (see <http://www.chronomodel.fr> for more information). ChronoModel is a friendly software to construct a chronological model in a Bayesian framework. Its output is a sampled Markov chain from the posterior distribution of dates component the chronology. The functions can also be applied to the analyse of mcmc output generated by Oxcal software.

Processes standard recommendation datasets (e.g., a user-item rating matrix) as input and generates rating predictions and lists of recommended items. Standard algorithm implementations which are included in this package are the following: Global/Item/User-Average baselines, Weighted Slope One, Item-Based KNN, User-Based KNN, FunkSVD, BPR and weighted ALS. They can be assessed according to the standard offline evaluation methodology (Shani, et al. (2011) <doi:10.1007/978-0-387-85820-3_8>) for recommender systems using measures such as MAE, RMSE, Precision, Recall, F1, AUC, NDCG, RankScore and coverage measures. The package (Coba, et al.(2017) <doi: 10.1007/978-3-319-60042-0_36>) is intended for rapid prototyping of recommendation algorithms and education purposes.