An application to calculate a patient's pretest probability (PTP) for obstructive Coronary Artery Disease (CAD) from a collection of guidelines or studies. Guidelines usually comes from the American Heart Association (AHA), American College of Cardiology (ACC) or European Society of Cardiology (ESC). Examples of PTP scores that comes from studies are the 2020 Winther et al. basic, Risk Factor-weighted Clinical Likelihood (RF-CL) and Coronary Artery Calcium Score-weighted Clinical Likelihood (CACS-CL) models <doi:10.1016/j.jacc.2020.09.585>, 2019 Reeh et al. basic and clinical models <doi:10.1093/eurheartj/ehy806> and 2017 Fordyce et al. PROMISE Minimal-Risk Tool <doi:10.1001/jamacardio.2016.5501>. As diagnosis of CAD involves a costly and invasive coronary angiography procedure for patients, having a reliable PTP for CAD helps doctors to make better decisions during patient management. This ensures high risk patients can be diagnosed and treated early for CAD while avoiding unnecessary testing for low risk patients.

Historic Pell grant data as provided by the US Department of Education. This package contains data about how much pell grant was awarded by which institution in which year. This data comes from the US Department of Education. Raw data can be downloaded from here: <https://www2.ed.gov/finaid/prof/resources/data/pell-institution.html>.

Estimate coefficient of variation percent (CV%) for any arbitrary distribution, including some built-in estimates for commonly-used transformations in pharmacometrics. Methods are described in various sources, but applied here as summarized in: Prybylski, (2024) <doi:10.1007/s40262-023-01343-2>.

Patient Rule Induction Method (PRIM) for bump hunting in high-dimensional data.

Proteins reside in either the cell plasma or in the cell membrane. A membrane protein goes through the membrane at least once. Given the amino acid sequence of a membrane protein, the tool PureseqTM (<https://github.com/PureseqTM/pureseqTM_package>, as described in "Efficient And Accurate Prediction Of Transmembrane Topology From Amino acid sequence only.", Wang, Qing, et al (2019), <doi:10.1101/627307>), can predict the topology of a membrane protein. This package allows one to use PureseqTM from R.

This package provides functions for estimation and data generation for several piecewise lifetime distributions. The package implements the power piecewise Weibull model, which includes the piecewise Rayleigh and piecewise exponential models as special cases. See Feigl and Zelen (1965) <doi:10.2307/2528247> for methodological details.

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

Read R package news files, regardless of whether or not the package is installed.

This package provides a function PWI() that calculates prize winner indices based on bibliometric data is provided. The default is the Derek de Solla Price Memorial Medal'. Users can provide recipients of other prizes.

This package provides methods for plotting potentially large (raster) images interactively on a plain HTML canvas. In contrast to package mapview data are plotted without background map, but data can be projected to any spatial coordinate reference system. Supports plotting of classes RasterLayer', RasterStack', RasterBrick (from package raster') as well as png files located on disk. Interactivity includes zooming, panning, and mouse location information. In case of multi-layer RasterStacks or RasterBricks', RGB image plots are created (similar to raster::plotRGB - but interactive).

Accurate classification of breast cancer tumors based on gene expression data is not a trivial task, and it lacks standard practices.The PAM50 classifier, which uses 50 gene centroid correlation distances to classify tumors, faces challenges with balancing estrogen receptor (ER) status and gene centering. The PCAPAM50 package leverages principal component analysis and iterative PAM50 calls to create a gene expression-based ER-balanced subset for gene centering, avoiding the use of protein expression-based ER data resulting into an enhanced Breast Cancer subtyping.

Check compliance of event-data from (business) processes with respect to specified rules. Rules supported are of three types: frequency (activities that should (not) happen x number of times), order (succession between activities) and exclusiveness (and and exclusive choice between activities).

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 coding assistant using Perplexity's Large Language Models <https://www.perplexity.ai/> API. A set of functions and RStudio add-ins that aim to help R developers.

This package provides a comprehensive, user-friendly package for label-free proteomics data analysis and machine learning-based modeling. Data generated from MaxQuant can be easily used to conduct differential expression analysis, build predictive models with top protein candidates, and assess model performance. promor includes a suite of tools for quality control, visualization, missing data imputation (Lazar et. al. (2016) <doi:10.1021/acs.jproteome.5b00981>), differential expression analysis (Ritchie et. al. (2015) <doi:10.1093/nar/gkv007>), and machine learning-based modeling (Kuhn (2008) <doi:10.18637/jss.v028.i05>).

We extend two general methods of moment estimators to panel vector autoregression models (PVAR) with p lags of endogenous variables, predetermined and strictly exogenous variables. This general PVAR model contains the first difference GMM estimator by Holtz-Eakin et al. (1988) <doi:10.2307/1913103>, Arellano and Bond (1991) <doi:10.2307/2297968> and the system GMM estimator by Blundell and Bond (1998) <doi:10.1016/S0304-4076(98)00009-8>. We also provide specification tests (Hansen overidentification test, lag selection criterion and stability test of the PVAR polynomial) and classical structural analysis for PVAR models such as orthogonal and generalized impulse response functions, bootstrapped confidence intervals for impulse response analysis and forecast error variance decompositions.

Free UK geocoding using data from Office for National Statistics. It is using several functions to get information about post codes, outward codes, reverse geocoding, nearest post codes/outward codes, validation, or randomly generate a post code. API wrapper around <https://postcodes.io>.

The functions are designed to find the efficient mean-variance frontier or portfolio weights for static portfolio (called Markowitz portfolio) analysis in resource economics or nature conservation. Using the nonlinear programming solver ('Rsolnp'), this package deals with the quadratic minimization of the variance-covariances without shorting (i.e., non-negative portfolio weights) studied in Ando and Mallory (2012) <doi:10.1073/pnas.1114653109>. See the examples, testing versions, and more details from: <https://github.com/ysd2004/portn>.

Connects to the API of <https://pushshift.io/> to search for Reddit comments and submissions.

Wrapper of the Petfinder API <https://www.petfinder.com/developers/v2/docs/> that implements methods for interacting with and extracting data from the Petfinder database. The Petfinder REST API allows access to the Petfinder database, one of the largest online databases of adoptable animals and animal welfare organizations across North America.

Fitting and testing probabilistic knowledge structures, especially the basic local independence model (BLIM, Doignon & Flamagne, 1999) and the simple learning model (SLM), using the minimum discrepancy maximum likelihood (MDML) method (Heller & Wickelmaier, 2013 <doi:10.1016/j.endm.2013.05.145>).

Pooling, backward and forward selection of linear, logistic and Cox regression models in multiply imputed datasets. Backward and forward selection can be done from the pooled model using Rubin's Rules (RR), the D1, D2, D3, D4 and the median p-values method. This is also possible for Mixed models. The models can contain continuous, dichotomous, categorical and restricted cubic spline predictors and interaction terms between all these type of predictors. The stability of the models can be evaluated using (cluster) bootstrapping. The package further contains functions to pool model performance measures as ROC/AUC, Reclassification, R-squared, scaled Brier score, H&L test and calibration plots for logistic regression models. Internal validation can be done across multiply imputed datasets with cross-validation or bootstrapping. The adjusted intercept after shrinkage of pooled regression coefficients can be obtained. Backward and forward selection as part of internal validation is possible. A function to externally validate logistic prediction models in multiple imputed datasets is available and a function to compare models. For Cox models a strata variable can be included. Eekhout (2017) <doi:10.1186/s12874-017-0404-7>. Wiel (2009) <doi:10.1093/biostatistics/kxp011>. Marshall (2009) <doi:10.1186/1471-2288-9-57>.

Fits penalized linear mixed models that correct for unobserved confounding factors. plmmr infers and corrects for the presence of unobserved confounding effects such as population stratification and environmental heterogeneity. It then fits a linear model via penalized maximum likelihood. Originally designed for the multivariate analysis of single nucleotide polymorphisms (SNPs) measured in a genome-wide association study (GWAS), plmmr eliminates the need for subpopulation-specific analyses and post-analysis p-value adjustments. Functions for the appropriate processing of PLINK files are also supplied. For examples, see the package homepage. <https://pbreheny.github.io/plmmr/>.

This package provides tools for downloading, reading and analyzing the National Survey of Health - PNS, a household survey from Brazilian Institute of Geography and Statistics - IBGE. The data must be downloaded from the official website <https://www.ibge.gov.br/>. Further analysis must be made using package survey'.