This package provides tools for the test for the comparison of survival curves, the evaluation of the goodness-of-fit and the predictive capacity of the proportional hazards model.

This package provides quasi-Newton methods to minimize partially separable functions. The methods are largely described by Nocedal and Wright (2006) <doi:10.1007/978-0-387-40065-5>.

This package provides tools for analyzing data generated from conjoint survey experiments, a method widely used in the social sciences for studying multidimensional preferences. The package implements estimation of marginal means (MMs) and average marginal component effects (AMCEs), with corrections for measurement error. Methods include profile-level and choice-level estimators, bias correction using intra-respondent reliability (IRR), and visualization utilities. For details on the methodology, see Clayton, Horiuchi, Kaufman, King, and Komisarchik (2025) <https://gking.harvard.edu/conjointE>.

An interface to simplify organizing parameters used in a package, using external configuration files. This attempts to provide a cleaner alternative to options().

Normalizes city names for EEA countries and matches them to NUTS 3 regions using provided crosswalks. Features include comprehensive normalization rules, cascading matching logic (Exact NUTS -> Exact LAU -> Fuzzy), and single-source data synthesis. The package implements the NUTS classification as described in the NUTS methodology (Eurostat (2021) <https://ec.europa.eu/eurostat/web/nuts>).

This package provides access to a high performant random distribution sampler for the Polya Gamma Distribution using either C++ headers for Rcpp or RcppArmadillo and R'.

This package provides an interactive Shiny-based toolkit for conducting latent structure analyses, including Latent Profile Analysis (LPA), Latent Class Analysis (LCA), Latent Trait Analysis (LTA/IRT), Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), and Structural Equation Modeling (SEM). The implementation is grounded in established methodological frameworks: LPA is supported through tidyLPA (Rosenberg et al., 2018) <doi:10.21105/joss.00978>, LCA through poLCA (Linzer & Lewis, 2011) <doi:10.32614/CRAN.package.poLCA> & glca (Kim & Kim, 2024) <doi:10.32614/CRAN.package.glca>, LTA/IRT via mirt (Chalmers, 2012) <doi:10.18637/jss.v048.i06>, and EFA via psych (Revelle, 2025). SEM and CFA functionalities build upon the lavaan framework (Rosseel, 2012) <doi:10.18637/jss.v048.i02>. Users can upload datasets or use built-in examples, fit models, compare fit indices, visualize results, and export outputs without programming.

Most price indexes are made with a two-step procedure, where period-over-period elementary indexes are first calculated for a collection of elementary aggregates at each point in time, and then aggregated according to a price index aggregation structure. These indexes can then be chained together to form a time series that gives the evolution of prices with respect to a fixed base period. This package contains a collection of functions that revolve around this work flow, making it easy to build standard price indexes, and implement the methods described by Balk (2008, <doi:10.1017/CBO9780511720758>), von der Lippe (2007, <doi:10.3726/978-3-653-01120-3>), and the CPI manual (2020, <doi:10.5089/9781484354841.069>) for bilateral price indexes.

This is a wrapper for the Mercury Parser API. The Mercury Parser is a single API endpoint that takes a URL and gives you back the content reliably and easily. With just one API request, Mercury takes any web article and returns only the relevant content â headline, author, body text, relevant images and more â free from any clutter. Itâ s reliable, easy-to-use and free. See the webpage here: <https://mercury.postlight.com/>.

The image of the amino acid transform on the protein level is drawn, and the automatic routing of the functional elements such as the domain and the mutation site is completed.

This wrapper houses PathLit API endpoints for R. The usage of these endpoints require the use of an API key which can be obtained at <https://www.pathlit.io/docs/cli/>.

The Predictive Power Score (PPS) is an asymmetric, data-type-agnostic score that can detect linear or non-linear relationships between two variables. The score ranges from 0 (no predictive power) to 1 (perfect predictive power). PPS can be useful for data exploration purposes, in the same way correlation analysis is. For more information on PPS, see <https://github.com/paulvanderlaken/ppsr>.

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").

This package provides tools to import, clean, filter, and prepare Project FeederWatch data for analysis. Includes functions for taxonomic rollup, easy filtering, zerofilling, merging in site metadata, and more. Project FeederWatch data comes from <https://feederwatch.org/explore/raw-dataset-requests/>.

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.

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.

Examines the characteristics of a data frame and a formula to automatically choose the most suitable type of plot out of the following supported options: scatter, violin, box, bar, density, hexagon bin, spine plot, and heat map. The aim of the package is to let the user focus on what to plot, rather than on the "how" during exploratory data analysis. It also automates handling of observation weights, logarithmic axis scaling, reordering of factor levels, and overlaying smoothing curves and median lines. Plots are drawn using ggplot2'.

Evaluate a function across a grid of parameters. The function may be evaluated once, or many times for simulation. Parallel computing is facilitated. Utilities aim at performing analyses of power and sample size, allowing for easy search of minimum n (or min/max of any other parameter) to achieve a desired minimal level of power (or maximum of any other objective). Plotting functions are included that present the dependency of n and power in relation to further assumptions.

Generalized Least Squares (GLS) estimation of Seemingly Unrelated Regression (SUR) systems on unbalanced panel in the one/two-way cases also taking into account the possibility of cross equation restrictions. Methodological details can be found in Biørn (2004) <doi:10.1016/j.jeconom.2003.10.023> and Platoni, Sckokai, Moro (2012) <doi:10.1080/07474938.2011.607098>.

This package contains functions to classify the pixels of an image file by its colour. It implements a simple form of the techniques known as Support Vector Machine adapted to this particular problem.

R API for Pathling', a tool for querying and transforming electronic health record data that is represented using the Fast Healthcare Interoperability Resources (FHIR) standard - see <https://pathling.csiro.au/docs>.

Features unstructured, structured and reverse geocoding using the photon geocoding API <https://photon.komoot.io/>. Facilitates the setup of local photon instances to enable offline geocoding.

This package provides tools for analyzing Pakistan's Population Censuses data via the PakPC2023 and PakPC2017 R packages. Designed for researchers, policymakers, and professionals, the app enables in-depth numerical and graphical analysis, including detailed cross-tabulations and insights. With diverse statistical models and visualization options, it supports informed decision-making in social and economic policy. This tool enhances users ability to explore and interpret census data, providing valuable insights for effective planning and analysis across various fields.

This package provides a direct and flexible method for estimating an ICA model. This approach estimates the densities for each component directly via a tilted Gaussian. The tilt functions are estimated via a GAM Poisson model. Details can be found in "Elements of Statistical Learning (2nd Edition)" in Section 14.7.4.