This package provides a system containing easy-to-use tools to compute the bioequivalence assessment in the univariate framework using the methods proposed in Boulaguiem et al. (2023) <doi:10.1101/2023.03.11.532179>.

This package implements a method for identifying and removing the cell-cycle effect from scRNA-Seq data. The description of the method is in Barron M. and Li J. (2016) <doi:10.1038/srep33892>. Identifying and removing the cell-cycle effect from single-cell RNA-Sequencing data. Submitted. Different from previous methods, ccRemover implements a mechanism that formally tests whether a component is cell-cycle related or not, and thus while it often thoroughly removes the cell-cycle effect, it preserves other features/signals of interest in the data.

Circumplex models, which organize constructs in a circle around two underlying dimensions, are popular for studying interpersonal functioning, mood/affect, and vocational preferences/environments. This package provides tools for analyzing and visualizing circular data, including scoring functions for relevant instruments and a generalization of the bootstrapped structural summary method from Zimmermann & Wright (2017) <doi:10.1177/1073191115621795> and functions for creating publication-ready tables and figures from the results.

Collection of indices and tools relating to clinical research that aid epidemiological cohort or retrospective chart review with big data. All indices and tools take commonly used lab values, patient demographics, and clinical measurements to compute various risk and predictive values for survival or further classification/stratification. References to original literature and validation contained in each function documentation. Includes all commonly available calculators available online.

This package provides tools for advanced analysis of continuous glucose monitoring (CGM) time-series, implementing GRID (Glucose Rate Increase Detector) and GRID-based algorithms for postprandial peak detection, and detection of hypoglycemic and hyperglycemic episodes (Levels 1/2/Extended) aligned with international consensus CGM metrics. Core algorithms are implemented in optimized C++ using Rcpp to provide accurate and fast analysis on large datasets.

Perform post hoc analysis based on residuals of Pearson's Chi-squared Test for Count Data based on T. Mark Beasley & Randall E. Schumacker (1995) <doi: 10.1080/00220973.1995.9943797>.

Multidimensional scaling (MDS) methods that aim at pronouncing the clustered appearance of the configuration (Rusch, Mair & Hornik, 2021, <doi:10.1080/10618600.2020.1869027>). They achieve this by transforming proximities/distances with explicit power functions and penalizing the fitting criterion with a clusteredness index, the OPTICS Cordillera (Rusch, Hornik & Mair, 2018, <doi:10.1080/10618600.2017.1349664>). There are two variants: One for finding the configuration directly (COPS-C) with given explicit power transformations and implicit ratio, interval and non-metric optimal scaling transformations (Borg & Groenen, 2005, ISBN:978-0-387-28981-6), and one for using the augmented fitting criterion to find optimal hyperparameters for the explicit transformations (P-COPS). The package contains various functions, wrappers, methods and classes for fitting, plotting and displaying a large number of different MDS models (most of the functionality in smacofx) in the COPS framework. The package further contains a function for pattern search optimization, the ``Adaptive Luus-Jaakola Algorithm (Rusch, Mair & Hornik, 2021,<doi:10.1080/10618600.2020.1869027>) and a functions to calculate the phi-distances for count data or histograms.

Helps create alerts and determine trends by using various methods to analyze public health surveillance data. The primary analysis method is based upon a published analytics strategy by Benedetti (2019) <doi:10.5588/pha.19.0002>.

Provided data containing an outcome variable, compositional variables and additional covariates (optional); linearly regress the outcome variable on an isometric log ratio (ilr) transformation of the linearly dependent compositional variables. The package provides predictions (with confidence intervals) in the change (delta) in the outcome/response variable based on the multiple linear regression model and evenly spaced reallocations of the compositional values. The compositional data analysis approach implemented is outlined in Dumuid et al. (2017a) <doi:10.1177/0962280217710835> and Dumuid et al. (2017b) <doi:10.1177/0962280217737805>.

This package provides the source and examples for James P. Howard, II, "Computational Methods for Numerical Analysis with R," <https://jameshoward.us/cmna/>, a book on numerical methods in R.

These functions implement collocation-inference for continuous-time and discrete-time stochastic processes. They provide model-based smoothing, gradient-matching, generalized profiling and forwards prediction error methods.

Computes a structural similarity metric (after the style of MS-SSIM for images) for binary and categorical 2D and 3D images. Can be based on accuracy (simple matching), Cohen's kappa, Rand index, adjusted Rand index, Jaccard index, Dice index, normalized mutual information, or adjusted mutual information. In addition, has fast computation of Cohen's kappa, the Rand indices, and the two mutual informations. Implements the methods of Thompson and Maitra (2020) <doi:10.48550/arXiv.2004.09073>.

In the context of paid research studies and clinical trials, budget considerations and patient sampling from available populations are subject to inherent constraints. We introduce the CDsampling package, which integrates optimal design theories within the framework of constrained sampling. This package offers the possibility to find both D-optimal approximate and exact allocations for samplings with or without constraints. Additionally, it provides functions to find constrained uniform sampling as a robust sampling strategy with limited model information. Our package offers functions for the computation of the Fisher information matrix under generalized linear models (including regular linear regression model) and multinomial logistic models.To demonstrate the applications, we also provide a simulated dataset and a real dataset embedded in the package. Yifei Huang, Liping Tong, and Jie Yang (2025)<doi:10.5705/ss.202022.0414>.

Facilitates local polynomial regression for state dependent covariates in state-space models. The functionality can also be used from C++ based model builder tools such as Rcpp'/'inline', TMB', or JAGS'.

This package provides a likelihood-based hypothesis testing approach is implemented for assessing causal mediation. Described in Millstein, Chen, and Breton (2016), <DOI:10.1093/bioinformatics/btw135>, it could be used to test for mediation of a known causal association between a DNA variant, the instrumental variable', and a clinical outcome or phenotype by gene expression or DNA methylation, the potential mediator. Another example would be testing mediation of the effect of a drug on a clinical outcome by the molecular target. The hypothesis test generates a p-value or permutation-based FDR value with confidence intervals to quantify uncertainty in the causal inference. The outcome can be represented by either a continuous or binary variable, the potential mediator is continuous, and the instrumental variable can be continuous or binary and is not limited to a single variable but may be a design matrix representing multiple variables.

Encryption wrappers, using low-level support from sodium and openssl'. cyphr tries to smooth over some pain points when using encryption within applications and data analysis by wrapping around differences in function names and arguments in different encryption providing packages. It also provides high-level wrappers for input/output functions for seamlessly adding encryption to existing analyses.

This package implements bound constrained optimal sample size allocation (BCOSSA) framework described in Bulus & Dong (2021) <doi:10.1080/00220973.2019.1636197> for power analysis of multilevel regression discontinuity designs (MRDDs) and multilevel randomized trials (MRTs) with continuous outcomes. Minimum detectable effect size (MDES) and power computations for MRDDs allow polynomial functional form specification for the score variable (with or without interaction with the treatment indicator). See Bulus (2021) <doi:10.1080/19345747.2021.1947425>.

This package provides tools for working with the International Classification of Diseases ('ICD-10 Chile official MINSAL'/'DEIS v2018). Includes optimized SQL search with SQLite', fuzzy matching of medical terms (Jaro-Winkler), Charlson and Elixhauser comorbidity calculation, WHO ICD-11 API integration, and hierarchical code validation. Data from Centro FIC Chile DEIS <https://deis.minsal.cl/centrofic/>.

This package contains a function, also called cchs', that calculates Estimator III of Borgan et al (2000), <DOI:10.1023/A:1009661900674>. This estimator is for fitting a Cox proportional hazards model to data from a case-cohort study where the subcohort was selected by stratified simple random sampling.

This package implements the model-free multiscale idealisation approaches: Jump-Segmentation by MUltiResolution Filter (JSMURF), Hotz et al. (2013) <doi:10.1109/TNB.2013.2284063>, JUmp Local dEconvolution Segmentation filter (JULES), Pein et al. (2018) <doi:10.1109/TNB.2018.2845126>, and Heterogeneous Idealization by Local testing and DEconvolution (HILDE), Pein et al. (2021) <doi:10.1109/TNB.2020.3031202>. Further details on how to use them are given in the accompanying vignette.

This package provides a convenient tool to store and format browser cookies and use them in HTTP requests (for example, through httr2', httr or curl').

Allows clinicians to predict survival probabilities over the next two years for cystic fibrosis patients, based on the clinical prediction models published in Stanojevic et al. (2019) <doi:10.1183/13993003.00224-2019>.

This package provides a helpful R6 class and methods for interacting with the Posit Connect Server API along with some meaningful utility functions for regular tasks. API documentation varies by Posit Connect installation and version, but the latest documentation is also hosted publicly at <https://docs.posit.co/connect/api/>.

Predict the course of clinical trial with a time-to-event endpoint for both two-arm and single-arm design. Each of the four primary study design parameters (the expected number of observed events, the number of subjects enrolled, the observation time, and the censoring parameter) can be derived analytically given the other three parameters. And the simulation datasets can be generated based on the design settings.