This package provides a suite of diagnostic tools for univariate point processes. This includes tools for simulating and fitting both common and more complex temporal point processes. We also include functions to visualise these point processes and collect existing diagnostic tools of Brown et al. (2002) <doi:10.1162/08997660252741149> and Wu et al. (2021) <doi:10.1002/9781119821588.ch7>, which can be used to assess the fit of a chosen point process model.

An extensible repository of accurate, up-to-date functions to score commonly used patient-reported outcome (PRO), quality of life (QOL), and other psychometric and psychological measures. PROscorer', together with the PROscorerTools package, is a system to facilitate the incorporation of PRO measures into research studies and clinical settings in a scientifically rigorous and reproducible manner. These packages and their vignettes are intended to help establish and promote best practices for scoring PRO and PRO-like measures in research. The PROscorer Instrument Descriptions vignette contains descriptions of each instrument scored by PROscorer', complete with references. These instrument descriptions are suitable for inclusion in formal study protocol documents, grant proposals, and manuscript Method sections. Each PROscorer function is composed of helper functions from the PROscorerTools package, and users are encouraged to contribute new functions to PROscorer'. More scoring functions are currently in development and will be added in future updates.

This package provides functions for landscape analysis and data retrieval. The package allows users to download environmental variables from global datasets (e.g., WorldClim, CHELSA, ESA WorldCover, Nighttime Lights), and to compute spatial and landscape metrics using a hexagonal grid system based on the H3 spatial index. It is useful for ecological modeling, biodiversity studies, and spatial data processing in landscape ecology. Fick and Hijmans (2017) <doi:10.1002/joc.5086>. Zanaga et al. (2022) <doi:10.5281/zenodo.7254221>. Uber Technologies Inc. (2022) "H3: Hexagonal hierarchical spatial index". Román et al. (2018) <doi:10.1016/j.rse.2018.03.017>. Karger et al. (2017) <doi:10.1038/sdata.2017.122>. Brun et al. (2022) <doi:10.5194/essd-14-5573-2022>.

Function pip3d() tests whether a point in 3D space is within, exactly on, or outside an enclosed surface defined by a triangular mesh. Function pip2d() tests whether a point in 2D space is within, exactly on, or outside a polygon. For a reference, see: Liu et al., A new point containment test algorithm based on preprocessing and determining triangles, Computer-Aided Design 42(12):1143-1150.

Disk-based implementation of Functional Pruning Optimal Partitioning with up-down constraints <doi:10.18637/jss.v101.i10> for single-sample peak calling (independently for each sample and genomic problem), can handle huge data sets (10^7 or more).

Improved methods to construct prediction intervals for network meta-analysis. The parametric bootstrap and Kenward-Roger-type adjustment by Noma et al. (2022) <forthcoming> are implementable.

Conduct penalized meta-analysis, see Van Lissa, Van Erp, & Clapper (2023) <doi:10.31234/osf.io/6phs5>. In meta-analysis, there are often between-study differences. These can be coded as moderator variables, and controlled for using meta-regression. However, if the number of moderators is large relative to the number of studies, such an analysis may be overfit. Penalized meta-regression is useful in these cases, because it shrinks the regression slopes of irrelevant moderators towards zero.

This package provides methods for assessing the performance of a prediction model with respect to identifying patient-level treatment benefit. All methods are applicable for continuous and binary outcomes, and for any type of statistical or machine-learning prediction model as long as it uses baseline covariates to predict outcomes under treatment and control.

Calculates profile repeatability for replicate stress response curves, or similar time-series data. Profile repeatability is an individual repeatability metric that uses the variances at each timepoint, the maximum variance, the number of crossings (lines that cross over each other), and the number of replicates to compute the repeatability score. For more information see Reed et al. (2019) <doi:10.1016/j.ygcen.2018.09.015>.

Estimates when and where a model-guided treatment strategy may outperform a treat-all or treat-none approach by Monte Carlo simulation and evaluation of the Net Monetary Benefit. Details can be viewed in Parsons et al. (2023) <doi:10.21105/joss.05328>.

This package provides a collection of methods for commonly undertaken analytical tasks, primarily developed for Public Health Scotland (PHS) analysts, but the package is also generally useful to others working in the healthcare space, particularly since it has functions for working with Community Health Index (CHI) numbers. The package can help to make data manipulation and analysis more efficient and reproducible.

The Poisson-lognormal model and variants (Chiquet, Mariadassou and Robin, 2021 <doi:10.3389/fevo.2021.588292>) can be used for a variety of multivariate problems when count data are at play, including principal component analysis for count data, discriminant analysis, model-based clustering and network inference. Implements variational algorithms to fit such models accompanied with a set of functions for visualization and diagnostic.

Set of functions that implement the PoDBAY method, described in the publication A method to estimate probability of disease and vaccine efficacy from clinical trial immunogenicity data by Julie Dudasova, Regina Laube, Chandni Valiathan, Matthew C. Wiener, Ferdous Gheyas, Pavel Fiser, Justina Ivanauskaite, Frank Liu and Jeffrey R. Sachs (NPJ Vaccines, 2021), <doi:10.1038/s41541-021-00377-6>.

Calculates an acceptance sampling plan, (sample size and acceptance number) based in MIL STD 105E, Dodge Romig and MIL STD 414 tables and procedures. The arguments for each function are related to lot size, inspection level and quality level. The specific plan operating curve (OC), is calculated by the binomial distribution.

Inference and visualize gene regulatory network based on single-cell RNA sequencing pseudo-time information.

Palettes inspired by Paris 2024 Olympic and Paralympic Games for data visualizations. Length of color palettes is configurable.

Computes the exact probability density function of X/Y conditioned on positive quadrant for series of bivariate distributions,for more details see Nadarajah,Song and Si (2019) <DOI:10.1080/03610926.2019.1576893>.

This package provides a collection of miscellaneous functions for passive acoustics. Much of the content here is adapted to R from code written by other people. If you have any ideas of functions to add, please contact Taiki Sakai.

This package provides a wrapper for Paddle - The Merchant of Record for digital products API (Application Programming Interface) <https://developer.paddle.com/api-reference/overview>. Provides functions to manage and analyze products, customers, invoices and many more.

This package provides (weighted) Partial least squares Regression for generalized linear models and repeated k-fold cross-validation of such models using various criteria <doi:10.48550/arXiv.1810.01005>. It allows for missing data in the explanatory variables. Bootstrap confidence intervals constructions are also available.

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>.

Power and sample size calculation for testing fixed effect coefficients in multilevel linear mixed effect models with one or more than one independent populations. Laird, Nan M. and Ware, James H. (1982) <doi:10.2307/2529876>.

This package performs bivariate composite likelihood and full information maximum likelihood estimation for polytomous logit-normit (graded logistic) item response theory (IRT) models.

This package provides functions for pooling/combining the results (i.e., p-values) from (dependent) hypothesis tests. Included are Fisher's method, Stouffer's method, the inverse chi-square method, the Bonferroni method, Tippett's method, and the binomial test. Each method can be adjusted based on an estimate of the effective number of tests or using empirically derived null distribution using pseudo replicates. For Fisher's, Stouffer's, and the inverse chi-square method, direct generalizations based on multivariate theory are also available (leading to Brown's method, Strube's method, and the generalized inverse chi-square method). An introduction can be found in Cinar and Viechtbauer (2022) <doi:10.18637/jss.v101.i01>.