This package provides tools for the practical management of financial portfolios: backtesting investment and trading strategies, computing profit/loss and returns, analysing trades, handling lists of transactions, reporting, and more. The package provides a small set of reliable, efficient and convenient tools for processing and analysing trade/portfolio data. The manual provides all the details; it is available from <https://enricoschumann.net/R/packages/PMwR/manual/PMwR.html>. Examples and descriptions of new features are provided at <https://enricoschumann.net/notes/PMwR/>.

This package provides convenience functions and pre-programmed Stan models related to the paired comparison factor model. Its purpose is to make fitting paired comparison data using Stan easy. This package is described in Pritikin (2020) <doi:10.1016/j.heliyon.2020.e04821>.

Simulating particle movement in 2D space has many application. The particles package implements a particle simulator based on the ideas behind the d3-force JavaScript library. particles implements all forces defined in d3-force as well as others such as vector fields, traps, and attractors.

Calculate (stratified) percentiles on a data.frame Stratification will split the data.frame into subgroups and calculate percentiles for each independently.

Manipulation and analysis of phylogenetically simulated data sets and phylogenetically based analyses using GLS.

This package implements a range of facilities for post-hoc analysis and summarizing linear models, generalized linear models and generalized linear mixed models, including grouping and clustering via pairwise comparisons using graph representations and efficient algorithms for finding maximal cliques of a graph. Includes also non-parametric toos for post-hoc analysis. It has S3 methods for printing summarizing, and producing plots, line and barplots suitable for post-hoc analyses.

This package provides a system for fast, accurate, and flexible whole genome bisulfite sequencing (WGBS) data analysis of two-condition comparisons. Principal Component BiSulfite, PCBS', assigns methylated loci eigenvector values from the treatment-delineating principal component in lieu of running millions of pairwise statistical tests, which dramatically increases analysis flexibility and reduces computational requirements. Methods: <https://katlande.github.io/PCBS/articles/Differential_Methylation.html>.

Building patient level networks for prediction of medical outcomes and draw the cluster of network. This package is based on paper Personalized disease networks for understanding and predicting cardiovascular diseases and other complex processes (See Cabrera et al. <http://circ.ahajournals.org/content/134/Suppl_1/A14957>).

For a multivariate dataset with independent Poisson measurement error, calculates principal components of transformed latent Poisson means. T. Kenney, T. Huang, H. Gu (2019) <arXiv:1904.11745>.

This package provides tools for reshaping, plotting, and manipulating matrices of orthogonal polynomials.

Jointly segment several ChIP-seq samples to find the peaks which are the same and different across samples. The fast approximate maximum Poisson likelihood algorithm is described in "PeakSegJoint: fast supervised peak detection via joint segmentation of multiple count data samples" <doi:10.48550/arXiv.1506.01286> by TD Hocking and G Bourque.

This package provides a suite of multivariate methods and data visualization tools to implement profile analysis and cross-validation techniques described in Davison & Davenport (2002) <DOI: 10.1037/1082-989X.7.4.468>, Bulut (2013), and other published and unpublished resources. The package includes routines to perform criterion-related profile analysis, profile analysis via multidimensional scaling, moderated profile analysis, profile analysis by group, and a within-person factor model to derive score profiles.

Allows the user to convert PDF tables to formats more amenable to analysis ('.csv', .xml', or .xlsx') by wrapping the PDFTables API. In order to use the package, the user needs to sign up for an API account on the PDFTables website (<https://pdftables.com/pdf-to-excel-api>). The package works by taking a PDF file as input, uploading it to PDFTables, and returning a file with the extracted data.

The use of overparameterization is proposed with combinatorial analysis to test a broader spectrum of possible ARIMA models. In the selection of ARIMA models, the most traditional methods such as correlograms or others, do not usually cover many alternatives to define the number of coefficients to be estimated in the model, which represents an estimation method that is not the best. The popstudy package contains several tools for statistical analysis in demography and time series based in Shryock research (Shryock et. al. (1980) <https://books.google.co.cr/books?id=8Oo6AQAAMAAJ>).

We extend dplyr and fuzzyjoin join functions with features to preprocess the data, apply various data checks, and deal with conflicting columns.

This package provides a doubly robust precision medicine approach to fit, cross-validate and visualize prediction models for the conditional average treatment effect (CATE). It implements doubly robust estimation and semiparametric modeling approach of treatment-covariate interactions as proposed by Yadlowsky et al. (2020) <doi:10.1080/01621459.2020.1772080>.

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>.

Computes nonparametric p-values for the potential class memberships of new observations as well as cross-validated p-values for the training data. The p-values are based on permutation tests applied to an estimated Bayesian likelihood ratio, using a plug-in statistic for the Gaussian model, k nearest neighbors', weighted nearest neighbors or penalized logistic regression'. Additionally, it provides graphical displays and quantitative analyses of the p-values.

Estimating causal effects in the presence of post-treatment confounding using principal stratification. PStrata allows for customized monotonicity assumptions and exclusion restriction assumptions, with automatic full Bayesian inference supported by Stan'. The main workflow is PStrataModel() to specify the model, fit() to run MCMC sampling, estimate() to extract potential outcomes, and contrast() to compute causal effects. Visualization tools are provided for diagnosis and interpretation. See Liu and Li (2023) <doi:10.48550/arXiv.2304.02740> for details.

Calculates various functions needed for design and monitoring survival trials accounting for complex situations such as delayed treatment effect, treatment crossover, non-uniform accrual, and different censoring distributions between groups. The event time distribution is assumed to be piecewise exponential (PWE) distribution and the entry time is assumed to be piecewise uniform distribution. As compared with Version 1.2.1, two more types of hybrid crossover are added. A bug is corrected in the function "pwecx" that calculates the crossover-adjusted survival, distribution, density, hazard and cumulative hazard functions. Also, to generate the crossover-adjusted event time random variable, a more efficient algorithm is used and the output includes crossover indicators.

Collection of tutorials for working with Positron and for learning how to apply generative AI when coding with R. Covers scripts, Quarto documents, Git', GitHub', and Quarto websites. Makes extensive use of the tools in the tutorial.helpers package.

This package provides functions for landscape analysis and data retrieval. The package allows users to download environmental variables from global datasets (e.g., WorldClim, CHELSA, ESA WorldCover, Nighttime Lights), and to compute spatial and landscape metrics using a hexagonal grid system based on the H3 spatial index. It is useful for ecological modeling, biodiversity studies, and spatial data processing in landscape ecology. Fick and Hijmans (2017) <doi:10.1002/joc.5086>. Zanaga et al. (2022) <doi:10.5281/zenodo.7254221>. Uber Technologies Inc. (2022) "H3: Hexagonal hierarchical spatial index". Román et al. (2018) <doi:10.1016/j.rse.2018.03.017>. Karger et al. (2017) <doi:10.1038/sdata.2017.122>. Brun et al. (2022) <doi:10.5194/essd-14-5573-2022>.

There are two main functions: (1) To estimate the power of testing for linkage using an affected sib pair design, as a function of the recurrence risk ratios. We will use analytical power formulae as implemented in R. These are based on a Mathematica notebook created by Martin Farrall. (2) To examine how the power of the transmission disequilibrium test (TDT) depends on the disease allele frequency, the marker allele frequency, the strength of the linkage disequilibrium, and the magnitude of the genetic effect. We will use an R program that implements the power formulae of Abel and Muller-Myhsok (1998). These formulae allow one to quickly compute power of the TDT approach under a variety of different conditions. This R program was modeled on Martin Farrall's Mathematica notebook.

This package provides a set of tools to extract bibliographic content from PubMed database using NCBI REST API <https://www.ncbi.nlm.nih.gov/home/develop/api/>. It includes functions to search, download, and convert PubMed bibliographic records into data frames compatible with the bibliometrix package. Features include programmatic query building, batch downloading by PMID, citation enrichment via NCBI E-Link, and robust error handling with automatic retry logic.