This package provides a user friendly way to create patient level prediction models using the Observational Medical Outcomes Partnership Common Data Model. Given a cohort of interest and an outcome of interest, the package can use data in the Common Data Model to build a large set of features. These features can then be used to fit a predictive model with a number of machine learning algorithms. This is further described in Reps (2017) <doi:10.1093/jamia/ocy032>.

An R interface to pikchr (<https://pikchr.org>, pronounced â pictureâ ), a PIC'-like markup language for creating diagrams within technical documentation. Originally developed by Brian Kernighan, PIC has been adapted into pikchr by D. Richard Hipp, the creator of SQLite'. pikchr is designed to be embedded in fenced code blocks of Markdown or other documentation markup languages, making it ideal for generating diagrams in text-based formats. This package allows R users to seamlessly integrate the descriptive syntax of pikchr for diagram creation directly within the R environment.

Permutation (randomisation) test for single-case phase design data with two phases (e.g., pre- and post-treatment). Correction for dependency of observations is done through stepwise resampling the time series while varying the distance between observations. The required distance 0,1,2,3.. is determined based on repeated dependency testing while stepwise increasing the distance. In preparation: Vroegindeweij et al. "A Permutation distancing test for single-case observational AB phase design data: A Monte Carlo simulation study".

Download economic and financial time series from public sources, including the St Louis Fed's FRED system, Yahoo Finance, the US Bureau of Labor Statistics, the US Energy Information Administration, the World Bank, Eurostat, the European Central Bank, the Bank of England, the UK's Office of National Statistics, Deutsche Bundesbank, and INSEE.

Measure productivity and efficiency using Data Envelopment Analysis (DEA). Available methods include DEA under different technology assumptions, bootstrapping of efficiency scores and calculation of the Malmquist productivity index. Analyses can be performed either in the console or with the provided shiny app. See Banker, R.; Charnes, A.; Cooper, W.W. (1984) <doi:10.1287/mnsc.30.9.1078>, Färe, R.; Grosskopf, S. (1996) <doi:10.1007/978-94-009-1816-0>.

This package contains all phrasal verbs listed in <https://www.englishclub.com/ref/Phrasal_Verbs/> as data frame. Useful for educational purpose as well as for text mining.

This package provides methods to calculate and present PHENTHAUproc', an early warning and decision support system for hazard assessment and control of oak processionary moth (OPM) using local and spatial temperature data. It was created by Halbig et al. 2024 (<doi:10.1016/j.foreco.2023.121525>) at FVA (<https://www.fva-bw.de/en/homepage/>) Forest Research Institute Baden-Wuerttemberg, Germany and at BOKU - University of Natural Ressources and Life Sciences, Vienna, Austria.

Allows to parse Java properties files in the context of R Service Bus applications.

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.

Extends the popular lavaan package by adding penalized estimation capabilities. It supports penalty on individual parameters as well as the difference between parameters.

We innovatively defined a pathway mutation accumulate perturbation score (PMAPscore) to reflect the position and the cumulative effect of the genetic mutations at the pathway level. Based on the PMAPscore of pathways, identified prognosis-related pathways altered by somatic mutation and predict immunotherapy efficacy by constructing a multiple-pathway-based risk model (Tarca, Adi Laurentiu et al (2008) <doi:10.1093/bioinformatics/btn577>).

This package provides functions for easily reading and processing binary data files created by Pamguard (<https://www.pamguard.org/>). All functions for directly reading the binary data files are based on MATLAB code written by Michael Oswald.

Prepare pharmacokinetic/pharmacodynamic (PK/PD) data for PK/PD analyses. This package provides functions to standardize infusion and bolus dose data while linking it to drug level or concentration data.

This package provides a collection of functions for modelling mutations in pedigrees with marker data, as used e.g. in likelihood computations with microsatellite data. Implemented models include equal, proportional and stepwise models, as well as random models for experimental work, and custom models allowing the user to apply any valid mutation matrix. Allele lumping is done following the lumpability criteria of Kemeny and Snell (1976), ISBN:0387901922.

Following the method of Bailey et al., computes for a collection of candidate models the probability of backtest overfitting, the performance degradation and probability of loss, and the stochastic dominance.

Power and sample size calculations for a variety of study designs and outcomes. Methods include t tests, ANOVA (including tests for interactions, simple effects and contrasts), proportions, categorical data (chi-square tests and proportional odds), linear, logistic and Poisson regression, alternative and coprimary endpoints, power for confidence intervals, correlation coefficient tests, cluster randomized trials, individually randomized group treatment trials, multisite trials, treatment-by-covariate interaction effects and nonparametric tests of location. Utilities are provided for computing various effect sizes. Companion package to the book "Power and Sample Size in R", Crespi (2025, ISBN:9781138591622). Further resources available at <https://powerandsamplesize.org/>.

This package provides tools for Natural Language Processing in French and texts from Marcel Proust's collection "A La Recherche Du Temps Perdu". The novels contained in this collection are "Du cote de chez Swann ", "A l'ombre des jeunes filles en fleurs","Le Cote de Guermantes", "Sodome et Gomorrhe I et II", "La Prisonniere", "Albertine disparue", and "Le Temps retrouve".

This package provides a graphical user interface for viewing and designing various types of graphs of the data. The graphs can be saved in different formats of an image.

This package provides general linear model facilities (single y-variable, multiple x-variables with arbitrary mixture of continuous and categorical and arbitrary interactions) for cross-species data. The method is, however, based on the nowadays rather uncommon situation in which uncertainty about a phylogeny is well represented by adopting a single polytomous tree. The theory is in A. Grafen (1989, Proc. R. Soc. B 326, 119-157) and aims to cope with both recognised phylogeny (closely related species tend to be similar) and unrecognised phylogeny (a polytomy usually indicates ignorance about the true sequence of binary splits).

This software has evolved from fisheries research conducted at the Pacific Biological Station (PBS) in Nanaimo', British Columbia, Canada. It extends the R language to include two-dimensional plotting features similar to those commonly available in a Geographic Information System (GIS). Embedded C code speeds algorithms from computational geometry, such as finding polygons that contain specified point events or converting between longitude-latitude and Universal Transverse Mercator (UTM) coordinates. Additionally, we include C++ code developed by Angus Johnson for the Clipper library, data for a global shoreline, and other data sets in the public domain. Under the user's R library directory .libPaths()', specifically in ./PBSmapping/doc', a complete user's guide is offered and should be consulted to use package functions effectively.

Automates the process of creating a scale bar and north arrow in any package that uses base graphics to plot in R. Bounding box tools help find and manipulate extents. Finally, there is a function to automate the process of setting margins, plotting the map, scale bar, and north arrow, and resetting graphic parameters upon completion.

Consider a possibly nonlinear nonparametric regression with p regressors. We provide evaluations by 13 methods to rank regressors by their practical significance or importance using various methods, including machine learning tools. Comprehensive methods are as follows. m6=Generalized partial correlation coefficient or GPCC by Vinod (2021)<doi:10.1007/s10614-021-10190-x> and Vinod (2022)<https://www.mdpi.com/1911-8074/15/1/32>. m7= a generalization of psychologists effect size incorporating nonlinearity and many variables. m8= local linear partial (dy/dxi) using the np package for kernel regressions. m9= partial (dy/dxi) using the NNS package. m10= importance measure using the NNS boost function. m11= Shapley Value measure of importance (cooperative game theory). m12 and m13= two versions of the random forest algorithm. Taraldsen's exact density for sampling distribution of correlations added.

This package provides tools for fitting periodic coefficients regression models to data where periodicity plays a crucial role. It allows users to model and analyze relationships between variables that exhibit cyclical or seasonal patterns, offering functions for estimating parameters and testing the periodicity of coefficients in linear regression models. For simple periodic coefficient regression model see Regui et al. (2024) <doi:10.1080/03610918.2024.2314662>.

Computes probabilities of the bivariate normal distribution in a vectorized R function (Drezner & Wesolowsky, 1990, <doi:10.1080/00949659008811236>).