Simulate pedigree, genetic merits and phenotypes with random/non-random matings followed by random/non-random selection with different intensities and patterns in males and females. Genotypes can be simulated for a given pedigree, or an appended pedigree to an existing pedigree with genotypes. Mrode, R. A. (2005) <ISBN:9780851989969, 0851989969>; Nilforooshan, M.A. (2022) <doi:10.37496/rbz5120210131>.

Visualizes a matrix object plainly as heatmap. It provides S3 functions to plot simple matrices and loading matrices.

Using Electronic Health Record (EHR) is difficult because most of the time the true characteristic of the patient is not available. Instead we can retrieve the International Classification of Disease code related to the disease of interest or we can count the occurrence of the Unified Medical Language System. None of them is the true phenotype which needs chart review to identify. However chart review is time consuming and costly. PheVis is an algorithm which is phenotyping (i.e identify a characteristic) at the visit level in an unsupervised fashion. It can be used for chronic or acute diseases. An example of how to use PheVis is available in the vignette. Basically there are two functions that are to be used: `train_phevis()` which trains the algorithm and `test_phevis()` which get the predicted probabilities. The detailed method is described in preprint by Ferté et al. (2020) <doi:10.1101/2020.06.15.20131458>.

Simulation of species diversification, fossil records, and phylogenies. While the literature on species birth-death simulators is extensive, including important software like paleotree and APE', we concluded there were interesting gaps to be filled regarding possible diversification scenarios. Here we strove for flexibility over focus, implementing a large array of regimens for users to experiment with and combine. In this way, paleobuddy can be used in complement to other simulators as a flexible jack of all trades, or, in the case of scenarios implemented only here, can allow for robust and easy simulations for novel situations. Environmental data modified from that in RPANDA': Morlon H. et al (2016) <doi:10.1111/2041-210X.12526>.

Enables the manufacturing, analysis and display of pressure volume curves. From the progression of the curves, turgor loss point, osmotic potential and apoplastic fraction can be derived. Methods adapted from Bartlett, Scoffoni and Sack (2012) <doi:10.1111/j.1461-0248.2012.01751.x>.

This package provides a method for fitting the entire regularization path of the principal components lasso for linear and logistic regression models. The algorithm uses cyclic coordinate descent in a path-wise fashion. See URL below for more information on the algorithm. See Tay, K., Friedman, J. ,Tibshirani, R., (2014) Principal component-guided sparse regression <arXiv:1810.04651>.

This package provides tools for statistical testing of correlation coefficients through robust permutation method and large sample approximation method. Tailored to different types of correlation coefficients including Pearson correlation coefficient, weighted Pearson correlation coefficient, Spearman correlation coefficient, and Lin's concordance correlation coefficient.The robust permutation test controls type I error under general scenarios when sample size is small and two variables are dependent but uncorrelated. The large sample approximation test generally controls type I error when the sample size is large (>200).

Estimates DNA target concentration by classifying digital PCR (polymerase chain reaction) droplets as positive, negative, or rain, using Expectation-Maximization Clustering. The fitting is accomplished using the EMMIXskew R package (v. 1.0.3) by Kui Wang, Angus Ng, and Geoff McLachlan (2018) as based on their paper "Multivariate Skew t Mixture Models: Applications to Fluorescence-Activated Cell Sorting Data" <doi:10.1109/DICTA.2009.88>.

Calculates seat allocation using the D-Hondt method, Sainte-Lague method, and Modified Sainte-Lague method, all commonly used in proportional representation electoral systems. For more information on these methods, see Michael Gallagher (1991)<doi:10.1016/0261-3794(91)90004-C>.

This package provides tools for loading and processing passive acoustic data. Read in data that has been processed in Pamguard (<https://www.pamguard.org/>), apply a suite processing functions, and export data for reports or external modeling tools. Parameter calculations implement methods by Oswald et al (2007) <doi:10.1121/1.2743157>, Griffiths et al (2020) <doi:10.1121/10.0001229> and Baumann-Pickering et al (2010) <doi:10.1121/1.3479549>.

Computes the Patient-Reported Outcomes (PROs) Joint Contrast (PJC), a residual-based summary that captures information left over after accounting for the clinical Disease Activity index for Psoriatic Arthritis (cDAPSA). PROs (pain and patient global assessment) and joint counts (swollen and tender) are standardized, then each component is adjusted for standardized cDAPSA using natural spline coefficients that were derived from previously published models. The resulting residuals are standardized and combined using fixed principal component loadings, to yield a continuous PJC score and quartile groupings. This package provides a calculator for applying those published coefficients to new datasets; it does not itself estimate spline models or principal components.

This package creates aesthetically pleasing and informative pie charts, ring charts, bar charts and box plots with colors, patterns, and images.

Bivariate additive categorical regression via penalized maximum likelihood. Under a multinomial framework, the method fits bivariate models where both responses are nominal, ordinal, or a mix of the two. Partial proportional odds models are supported, with flexible (non-)uniform association structures. Various logit types and parametrizations can be specified for both marginals and the association, including Daleâ s model. The association structure can be regularized using polynomial-type penalty terms. Additive effects are modeled using P-splines. Standard methods such as summary(), residuals(), and predict() are available.

This package provides a collection of R Markdown templates for creating simple and easy to personalize single page websites.

This package implements data processing described in <doi:10.1126/sciadv.abk3283> to align modern differentially private data with formatting of older US Census data releases. The primary goal is to read in Census Privacy Protected Microdata Files data in a reproducible way. This includes tools for aggregating to relevant levels of geography by creating geographic identifiers which match the US Census Bureau's numbering. Additionally, there are tools for grouping race numeric identifiers into categories, consistent with OMB (Office of Management and Budget) classifications. Functions exist for downloading and linking to existing sources of privacy protected microdata.

This package provides tools for scraping match statistics and player data from the Athletes Unlimited (UA) website <https://auprosports.com/volleyball/>, the League One Volleyball website <https://lovb.com>, and the Major League (MLV) website <https://provolleyball.com>.

This package provides functions and example data to teach and increase the reproducibility of the methods and code underlying the Propensity to Cycle Tool (PCT), a research project and web application hosted at <https://www.pct.bike/>. For an academic paper on the methods, see Lovelace et al (2017) <doi:10.5198/jtlu.2016.862>.

This package provides a collection of functions and data sets that support teaching a quantitative finance MS level course on Portfolio Construction and Risk Analysis, and the writing of a textbook for such a course. The package provides several real-world data sets for problem assignments and student projects, including cross-sections of stock data from the Center for Research on Security Prices, LLC (CRSP), corresponding factor exposures from S&P Global, and several S&P 500 data sets.

This package implements partial transfer learning (PTL) for causal effect estimation using source and target data, with bootstrap-based source detection. Provides data generating processes and nuisance functions for simulation.

This package implements optimization techniques for Lasso regression, R.Tibshirani(1996)<doi:10.1111/j.2517-6161.1996.tb02080.x> using Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) and Iterative Shrinkage-Thresholding Algorithm (ISTA) based on proximal operators, A.Beck(2009)<doi:10.1137/080716542>. The package is useful for high-dimensional regression problems and includes cross-validation procedures to select optimal penalty parameters.

This package provides an R implementation of the Particle Metropolis within Gibbs sampler for model parameter, covariance matrix and random effect estimation. A more general implementation of the sampler based on the paper by Gunawan, D., Hawkins, G. E., Tran, M. N., Kohn, R., & Brown, S. D. (2020) <doi:10.1016/j.jmp.2020.102368>. An HTML tutorial document describing the package is available at <https://university-of-newcastle-research.github.io/samplerDoc/> and includes several detailed examples, some background and troubleshooting steps.

An implementation of the pediatric complex chronic conditions (CCC) classification system using R and C++.

Sample size calculations for practical equivalence trial design with a time to event endpoint.

This package provides a comprehensive set of tools for describing and visualizing panel data structures, as well as for summarizing and visualizing variables within a panel data context.