This package provides a comprehensive set of tools to simulate, evaluate, and compare model-assisted designs for early-phase (Phase I/II) clinical trials, including: - BOIN12 (Bayesian optimal interval phase 1/11 trial design; Lin et al. (2020) <doi:10.1200/PO.20.00257>), - BOIN-ET (Takeda, K., Taguri, M., & Morita, S. (2018) <doi:10.1002/pst.1864>), - EffTox (Thall, P. F., & Cook, J. D. (2004) <doi:10.1111/j.0006-341X.2004.00218.x>), - Ji3+3 (Joint i3+3 design; Lin, X., & Ji, Y. (2020) <doi:10.1080/10543406.2020.1818250>), - PRINTE (probability intervals of toxicity and efficacy design; Lin, X., & Ji, Y. (2021) <doi:10.1177/0962280220977009>), - STEIN (simple toxicity and efficacy interval design; Lin, R., & Yin, G. (2017) <doi:10.1002/sim.7428>), - TEPI (toxicity and efficacy probability interval design; Li, D. H., Whitmore, J. B., Guo, W., & Ji, Y. (2017) <doi:10.1158/1078-0432.CCR-16-1125>), - uTPI (utility-based toxicity Probability interval design; Shi, H., Lin, R., & Lin, X. (2024) <doi:10.1002/sim.8922>). Includes flexible simulation parameters that allow researchers to efficiently compute operating characteristics under various fixed and random trial scenarios and export the results.

This package provides a bunch of convenience functions that transform the results of some basic statistical analyses into table format nearly ready for publication. This includes descriptive tables, tables of logistic regression and Cox regression results as well as forest plots.

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.

This package performs genomic prediction of hybrid performance using eight GS methods including GBLUP, BayesB, RKHS, PLS, LASSO, Elastic net, XGBoost and LightGBM. GBLUP: genomic best liner unbiased prediction, RKHS: reproducing kernel Hilbert space, PLS: partial least squares regression, LASSO: least absolute shrinkage and selection operator, XGBoost: extreme gradient boosting, LightGBM: light gradient boosting machine. It also provides fast cross-validation and mating design scheme for training population (Xu S et al (2016) <doi:10.1111/tpj.13242>; Xu S (2017) <doi:10.1534/g3.116.038059>). A complete manual for this package is provided in the manual folder of the package installation directory. You can locate the manual by running the following command in R: system.file("manual", package = "predhy.GUI").

Data analysis for Project Risk Management via the Second Moment Method, Monte Carlo Simulation, Contingency Analysis, Sensitivity Analysis, Earned Value Management, Learning Curves, Design Structure Matrices, and more.

An implementation of the "Design Analysis" proposed by Gelman and Carlin (2014) <doi:10.1177/1745691614551642>. It combines the evaluation of Power-Analysis with other inferential-risks as Type-M error (i.e. Magnitude) and Type-S error (i.e. Sign). See also Altoè et al. (2020) <doi:10.3389/fpsyg.2019.02893> and Bertoldo et al. (2020) <doi:10.31234/osf.io/q9f86>.

This package implements entrywise splitting cross-validation (ECV) and its penalized variant (pECV) for selecting the number of factors in generalized factor models.

Bayesian toolbox for quantitative proteomics. In particular, this package provides functions to generate synthetic datasets, execute Bayesian differential analysis methods, and display results as, described in the associated article Marie Chion and Arthur Leroy (2023) <arXiv:2307.08975>.

Achieve internal conversions of mass units, molar units, and volume units commonly used in pharmacokinetics, as well as conversions between mass units and molar units.

This package provides data sets and functions for exploration of Pakistan Population Census 2023 (<https://www.pbs.gov.pk/>).

Function to read PX-Web data into R via API. The example code reads data from the three national statistical institutes, Statistics Norway, Statistics Sweden and Statistics Finland.

Calculates an acceptance sampling plan, (sample size and acceptance number) based in MIL STD 105E, Dodge Romig and MIL STD 414 tables and procedures. The arguments for each function are related to lot size, inspection level and quality level. The specific plan operating curve (OC), is calculated by the binomial distribution.

Portfolio optimization and analysis routines and graphics.

This package provides functionality for the prior and posterior projected Polya tree for the analysis of circular data (Nieto-Barajas and Nunez-Antonio (2019) <arXiv:1902.06020>).

This package provides data set and function for exploration of Multiple Indicator Cluster Survey 2014 Women (age 15-49 years) questionnaire data for Punjab, Pakistan.

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.

Kernel density estimation on the polysphere, (hyper)sphere, and circle. Includes functions for density estimation, regression estimation, ridge estimation, bandwidth selection, kernels, samplers, and homogeneity tests. Companion package to Garcà a-Portugués and Meilán-Vila (2025) <doi:10.1080/01621459.2025.2521898> and Garcà a-Portugués and Meilán-Vila (2023) <doi:10.1007/978-3-031-32729-2_4>.

This package provides tools to compute unbiased pleiotropic heritability estimates of complex diseases from genome-wide association studies (GWAS) summary statistics. We estimate pleiotropic heritability from GWAS summary statistics by estimating the proportion of variance explained from an estimated genetic correlation matrix (Bulik-Sullivan et al. 2015 <doi:10.1038/ng.3406>) and employing a Monte-Carlo bias correction procedure to account for sampling noise in genetic correlation estimates.

Two functions for financial portfolio optimization by linear programming are provided. One function implements Benders decomposition algorithm and can be used for very large data sets. The other, applicable for moderate sample sizes, finds optimal portfolio which has the smallest distance to a given benchmark portfolio.

Chromatin immunoprecipitation DNA sequencing results in genomic tracks that show enriched regions or peaks where proteins are bound. This package implements fast C code that computes the true and false positives with respect to a database of annotated region labels.

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.

Uses provenance collected by rdtLite package or comparable tool to display information about input files, output files, and exchanged files for a single R script or a series of R scripts.

This package provides a shiny app that supports merging of PDF and/or image files with page selection, removal, or rotation options. It is a fast, free, and secure alternative to commercial software or various online websites which require users to sign-up, and it avoids any potential risks associated with uploading files elsewhere.

The main function, plot_mm(), is used for (gg)plotting output from mixture models, including both densities and overlaying mixture weight component curves from the fit models in line with the tidy principles. The package includes several additional functions for added plot customization. Supported model objects include: mixtools', EMCluster', and flexmix', with more from each in active dev. Supported mixture model specifications include mixtures of univariate Gaussians, multivariate Gaussians, Gammas, logistic regressions, linear regressions, and Poisson regressions.