Consists of custom wrapper functions using packages openxlsx', flextable', and officer to create highly formatted MS office friendly output of your data frames. These viewer friendly outputs are intended to match expectations of professional looking presentations in business and consulting scenarios. The functions are opinionated in the sense that they expect the input data frame to have certain properties in order to take advantage of the automated formatting.

Investigate (analytically or visually) the inputs and outputs of probabilistic analyses of health economic models using standard health economic visualisation and metamodelling methods.

Fit a model with potentially many linear and smooth predictors. Interaction effects can also be quantified. Variable selection is done using penalisation. For l1-type penalties we use iterative steps alternating between using linear predictors (lasso) and smooth predictors (generalised additive model).

Includes functions to calculate several physicochemical properties and indices for amino-acid sequences as well as to read and plot XVG output files from the GROMACS molecular dynamics package.

Survey sampling using permanent random numbers (PRN's). A solution to the problem of unknown overlap between survey samples, which leads to a low precision in estimates when the survey is repeated or combined with other surveys. The PRN solution is to supply the U(0, 1) random numbers to the sampling procedure, instead of having the sampling procedure generate them. In Lindblom (2014) <doi:10.2478/jos-2014-0047>, and therein cited papers, it is shown how this is carried out and how it improves the estimates. This package supports two common fixed-size sampling procedures (simple random sampling and probability-proportional-to-size sampling) and includes a function for transforming the PRN's in order to control the sample overlap.

We included functions to assess the performance of risk models. The package contains functions for the various measures that are used in empirical studies, including univariate and multivariate odds ratios (OR) of the predictors, the c-statistic (or area under the receiver operating characteristic (ROC) curve (AUC)), Hosmer-Lemeshow goodness of fit test, reclassification table, net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Also included are functions to create plots, such as risk distributions, ROC curves, calibration plot, discrimination box plot and predictiveness curves. In addition to functions to assess the performance of risk models, the package includes functions to obtain weighted and unweighted risk scores as well as predicted risks using logistic regression analysis. These logistic regression functions are specifically written for models that include genetic variables, but they can also be applied to models that are based on non-genetic risk factors only. Finally, the package includes function to construct a simulated dataset with genotypes, genetic risks, and disease status for a hypothetical population, which is used for the evaluation of genetic risk models.

This package provides a robust framework for analyzing the extent to which differential survival with respect to higher level trait variation is reducible to lower level variation. In addition to its primary test, it also provides functions for simulation-based power analysis, reading in common data set formats, and visualizing results. Temporarily contains an edited version of function hr.mcp() from package wild1', written by Glen Sargeant. For tutorial see: http://evolve.zoo.ox.ac.uk/Evolve/Perspectev.html.

Tool for producing Pen's parade graphs, useful for visualizing inequalities in income, wages or other variables, as proposed by Pen (1971, ISBN: 978-0140212594). Income or another economic variable is captured by the vertical axis, while the population is arranged in ascending order of income along the horizontal axis. Pen's income parades provide an easy-to-interpret visualization of economic inequalities.

Perform tests for pleiotropy of multiple traits of various variable types on genotypes for a genetic marker.

This package provides a simple implementation of the Predictive Information Index ('PII').

This package provides functions used to fit and test the phenology of species based on counts. Based on Girondot, M. (2010) <doi:10.3354/esr00292> for the phenology function, Girondot, M. (2017) <doi:10.1016/j.ecolind.2017.05.063> for the convolution of negative binomial, Girondot, M. and Rizzo, A. (2015) <doi:10.2993/etbi-35-02-337-353.1> for Bayesian estimate, Pfaller JB, ..., Girondot M (2019) <doi:10.1007/s00227-019-3545-x> for tag-loss estimate, Hancock J, ..., Girondot M (2019) <doi:10.1016/j.ecolmodel.2019.04.013> for nesting history, Laloe J-O, ..., Girondot M, Hays GC (2020) <doi:10.1007/s00227-020-03686-x> for aggregating several seasons.

Format and submit few-shot prompts to OpenAI's Large Language Models (LLMs). Designed to be particularly useful for text classification problems in the social sciences. Methods are described in Ornstein, Blasingame, and Truscott (2024) <https://joeornstein.github.io/publications/ornstein-blasingame-truscott.pdf>.

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

This package provides a thin wrapper over PLINK 2's core libraries which provides an R interface for reading .pgen files. A minimal .pvar loader is also included. Chang et al. (2015) <doi:10.1186/s13742-015-0047-8>.

Portfolio optimization and analysis routines and graphics.

Validation of risk predictions obtained from survival models and competing risk models based on censored data using inverse weighting and cross-validation. Most of the pec functionality has been moved to riskRegression'.

This package performs minimax linkage hierarchical clustering. Every cluster has an associated prototype element that represents that cluster as described in Bien, J., and Tibshirani, R. (2011), "Hierarchical Clustering with Prototypes via Minimax Linkage," The Journal of the American Statistical Association, 106(495), 1075-1084.

This package performs the explicit calculation -- not estimation! -- of the Rasch item parameters for dichotomous and polytomous item responses, using a pairwise comparison approach. Person parameters (WLE) are calculated according to Warm's weighted likelihood approach.

This package provides a PNAS'-alike style for rmarkdown', derived from the Proceedings of the National Academy of Sciences of the United States of America ('PNAS') LaTeX style, and adapted for use with markdown and pandoc'.

Fast functions for dealing with prime numbers, such as testing whether a number is prime and generating a sequence prime numbers. Additional functions include finding prime factors and Ruth-Aaron pairs, finding next and previous prime numbers in the series, finding or estimating the nth prime, estimating the number of primes less than or equal to an arbitrary number, computing primorials, prime k-tuples (e.g., twin primes), finding the greatest common divisor and smallest (least) common multiple, testing whether two numbers are coprime, and computing Euler's totient function. Most functions are vectorized for speed and convenience.

Presentation of a new goodness-of-fit normality test based on the Lilliefors method. For details on this method see: Sulewski (2019) <doi:10.1080/03610918.2019.1664580>.

When using pooled p-values to adjust for multiple testing, there is an inherent balance that must be struck between rejection based on weak evidence spread among many tests and strong evidence in a few, explored in Salahub and Olford (2023) <arXiv:2310.16600>. This package provides functionality to compute marginal and central rejection levels and the centrality quotient for p-value pooling functions and provides implementations of the chi-squared quantile pooled p-value (described in Salahub and Oldford (2023)) and a proposal from Heard and Rubin-Delanchy (2018) <doi:10.1093/biomet/asx076> to control the quotient's value.

This package implements the primePCA algorithm, developed and analysed in Zhu, Z., Wang, T. and Samworth, R. J. (2019) High-dimensional principal component analysis with heterogeneous missingness. <arXiv:1906.12125>.

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.