This package implements the methods for assessing heterogeneous cluster-specific treatment effects in partially nested designs as described in Liu (2024) <doi:10.1037/met0000723>. The estimation uses the multiply robust method, allowing for the use of machine learning methods in model estimation (e.g., random forest, neural network, and the super learner ensemble). Partially nested designs (also known as partially clustered designs) are designs where individuals in the treatment arm are assigned to clusters (e.g., teachers, tutoring groups, therapists), whereas individuals in the control arm have no such clustering.

This R package provides power calculations via internal simulation methods. The package also provides a frontend to the now abandoned PBAT program (developed by Christoph Lange), and reads in the corresponding output and displays results and figures when appropriate. The license of this R package itself is GPL. However, to have the program interact with the PBAT program for some functionality of the R package, users must additionally obtain the PBAT program from Christoph Lange, and accept his license. Both the data analysis and power calculations have command line and graphical interfaces using tcltk.

Streamlines the steps for adding colour scales and associated legends when working with base R graphics, especially for interactive use. Popular palettes are included and pretty legends produced when mapping a large variety of vector classes to a colour scale. An additional helper for adding axes and grid lines complements the base::plot() work flow.

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.

This package provides a collection of tools to facilitate standardized analysis and graphical procedures when using the National Cancer Instituteâ s Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) and other PRO measurements.

This package provides functions to select samples using PPS (probability proportional to size) sampling. The package also includes a function for stratified simple random sampling, a function to compute joint inclusion probabilities for Sampford's method of PPS sampling, and a few utility functions. The user's guide pps-ug.pdf is included in the .../pps/doc directory. The methods are described in standard survey sampling theory books such as Cochran's "Sampling Techniques"; see the user's guide for references.

Receiver Operating Characteristic (ROC) analysis is performed assuming samples are from the Power Lindley distribution. Specificity, sensitivity, area under the curve and ROC curve are provided.

It provides functions to perform permutation conditional random one-sample and two-samples t-tests in a multivariate framework.

Computes predicted probabilities and marginal effects for binary & ordinal logit and probit, (partial) generalized ordinal & multinomial logit models estimated with the glm(), clm() (in the ordinal package), and vglm() (in the VGAM package) functions.

Projection pursuit (PP) with 17 methods and grand tour with 3 methods. Being that projection pursuit searches for low-dimensional linear projections in high-dimensional data structures, while grand tour is a technique used to explore multivariate statistical data through animation.

Win ratio approach to partially ordered data, such as multivariate ordinal responses under product (consensus) or prioritized order. Two-sample tests and multiplicative regression models are implemented (Mao, 2024, under revision).

Fits by ABC, the parameters of a stochastic process modelling the phylogeny and evolution of a suite of traits following the tree. The user may define an arbitrary Markov process for the trait and phylogeny. Importantly, trait-dependent speciation models are handled and fitted to data. See K. Bartoszek, P. Lio (2019) <doi:10.5506/APhysPolBSupp.12.25>. The suggested geiger package can be obtained from CRAN's archive <https://cran.r-project.org/src/contrib/Archive/geiger/>, suggested to take latest version. Otherwise its required code is present in the pcmabc package. The suggested distory package can be obtained from CRAN's archive <https://cran.r-project.org/src/contrib/Archive/distory/>, suggested to take latest version.

Preregistrations, or more generally, registrations, enable explicit timestamped and (often but not necessarily publicly) frozen documentation of plans and expectations as well as decisions and justifications. In research, preregistrations are commonly used to clearly document plans and facilitate justifications of deviations from those plans, as well as decreasing the effects of publication bias by enabling identification of research that was conducted but not published. Like reporting guidelines, (pre)registration forms often have specific structures that facilitate systematic reporting of important items. The preregr package facilitates specifying (pre)registrations in R and exporting them to a human-readable format (using R Markdown partials or exporting to an HTML file) as well as human-readable embedded data (using JSON'), as well as importing such exported (pre)registration specifications from such embedded JSON'.

This package contains functions to fit proportional hazards (PH) model to partly interval-censored (PIC) data (Pan et al. (2020) <doi:10.1177/0962280220921552>), PH model with spatial frailty to spatially dependent PIC data (Pan and Cai (2021) <doi:10.1080/03610918.2020.1839497>), and mixed effects PH model to clustered PIC data. Each random intercept/random effect can follow both a normal prior and a Dirichlet process mixture prior. It also includes the corresponding functions for general interval-censored data.

Data sets and functions used in the polish book "Przewodnik po pakiecie R" (The Hitchhiker's Guide to the R). See more at <http://biecek.pl/R>. Among others you will find here data about housing prices, cancer patients, running times and many others.

Systematic reviews should be described in a high degree of methodological detail. The PRISMA Statement calls for a high level of reporting detail in systematic reviews and meta-analyses. An integral part of the methodological description of a review is a flow diagram. This package produces an interactive flow diagram that conforms to the PRISMA2020 preprint. When made interactive, the reader/user can click on each box and be directed to another website or file online (e.g. a detailed description of the screening methods, or a list of excluded full texts), with a mouse-over tool tip that describes the information linked to in more detail. Interactive versions can be saved as HTML files, whilst static versions for inclusion in manuscripts can be saved as HTML, PDF, PNG, SVG, PS or WEBP files.

It includes functions to download and process the Planet NICFI (Norway's International Climate and Forest Initiative) Satellite Imagery utilizing the Planet Mosaics API <https://developers.planet.com/docs/basemaps/reference/#tag/Basemaps-and-Mosaics>. GDAL (library for raster and vector geospatial data formats) and aria2c (paralleled download utility) must be installed and configured in the user's Operating System.

Parametric bootstrap (PB) has been used for three-way ANOVA model with unequal group variances.

Jointly segment several ChIP-seq samples to find the peaks which are the same and different across samples. The fast approximate maximum Poisson likelihood algorithm is described in "PeakSegJoint: fast supervised peak detection via joint segmentation of multiple count data samples" <doi:10.48550/arXiv.1506.01286> by TD Hocking and G Bourque.

Bundles a number of established statistical methods to facilitate the visual interpretation of large datasets in sedimentary geology. Includes functionality for adaptive kernel density estimation, principal component analysis, correspondence analysis, multidimensional scaling, generalised procrustes analysis and individual differences scaling using a variety of dissimilarity measures. Univariate provenance proxies, such as single-grain ages or (isotopic) compositions are compared with the Kolmogorov-Smirnov, Kuiper, Wasserstein-2 or Sircombe-Hazelton L2 distances. Categorical provenance proxies such as chemical compositions are compared with the Aitchison and Bray-Curtis distances,and count data with the chi-square distance. Varietal data can either be converted to one or more distributional datasets, or directly compared using the multivariate Wasserstein distance. Also included are tools to plot compositional and count data on ternary diagrams and point-counting data on radial plots, to calculate the sample size required for specified levels of statistical precision, and to assess the effects of hydraulic sorting on detrital compositions. Includes an intuitive query-based user interface for users who are not proficient in R.

This package provides adds postfix and infix logic operators for if, then, unless, and otherwise.

This package provides an interface to PDFMiner <https://github.com/pdfminer/pdfminer.six> a Python package for extracting information from PDF'-files. PDFMiner has the goal to get all information available in a PDF'-file, position of the characters, font type, font size and informations about lines. Which makes it the perfect starting point for extracting tables from PDF'-files. More information can be found in the package README'-file.

Estimation of two- and three-way dynamic panel threshold regression models (Di Lascio and Perazzini (2024) <https://repec.unibz.it/bemps104.pdf>; Di Lascio and Perazzini (2022, ISBN:978-88-9193-231-0); Seo and Shin (2016) <doi:10.1016/j.jeconom.2016.03.005>) through the generalized method of moments based on the first difference transformation and the use of instrumental variables. The models can be used to find a change point detection in the time series. In addition, random number generation is also implemented.

This package implements a range of facilities for post-hoc analysis and summarizing linear models, generalized linear models and generalized linear mixed models, including grouping and clustering via pairwise comparisons using graph representations and efficient algorithms for finding maximal cliques of a graph. Includes also non-parametric toos for post-hoc analysis. It has S3 methods for printing summarizing, and producing plots, line and barplots suitable for post-hoc analyses.