Various visual and numerical diagnosis methods for the nonlinear mixed effect model, including visual predictive checks, numerical predictive checks, and coverage plots (Karlsson and Holford, 2008, <https://www.page-meeting.org/?abstract=1434>).

Nonparametric Tests for Main Effects, Simple Effects and Interaction Effect with Censored Data and Two Factorial Influencing Variables.

Nonparametric tests for clustered data in pre-post intervention design documented in Cui and Harrar (2021) <doi:10.1002/bimj.201900310> and Harrar and Cui (2022) <doi:10.1016/j.jspi.2022.05.009>. Other than the main test results mentioned in the reference paper, this package also provides a function to calculate the sample size allocations for the input long format data set, and also a function for adjusted/unadjusted confidence intervals calculations. There are also functions to visualize the distribution of data across different intervention groups over time, and also the adjusted/unadjusted confidence intervals.

Next-Generation Clustered Heat Maps (NG-CHMs) allow for dynamic exploration of heat map data in a web browser. NGCHM allows users to create both stand-alone HTML files containing a Next-Generation Clustered Heat Map, and .ngchm files to view in the NG-CHM viewer. See Ryan MC, Stucky M, et al (2020) <doi:10.12688/f1000research.20590.2> for more details.

This comprehensive toolkit provide a consistent and extensible framework for working with missing values in vectors. The companion package tidyimpute provides similar functionality for list-like and table-like structures). Functions exist for detection, removal, replacement, imputation, recollection, etc. of NAs'.

Color palettes for data visualization inspired by National Parks. Currently contains 15 color schemes and checks for colorblind-friendliness of palettes.

This allows you to generate reporting workflows around nlmixr2 analyses with outputs in Word and PowerPoint. You can specify figures, tables and report structure in a user-definable YAML file. Also you can use the internal functions to access the figures and tables to allow their including in other outputs (e.g. R Markdown).

Access the United States National Provider Identifier Registry API <https://npiregistry.cms.hhs.gov/api/>. Obtain and transform administrative data linked to a specific individual or organizational healthcare provider, or perform advanced searches based on provider name, location, type of service, credentials, and other attributes exposed by the API.

Waiting list management using queuing theory to analyse, predict and manage queues, based on the approach described in Fong et al. (2022) <doi:10.1101/2022.08.23.22279117>. Aimed at UK National Health Service (NHS) applications, waiting list summary statistics, target-value calculations, waiting list simulation, and scheduling functions are included.

NeuroAnatomy Toolbox (nat) enables analysis and visualisation of 3D biological image data, especially traced neurons. Reads and writes 3D images in NRRD and Amira AmiraMesh formats and reads surfaces in Amira hxsurf format. Traced neurons can be imported from and written to SWC and Amira LineSet and SkeletonGraph formats. These data can then be visualised in 3D via rgl', manipulated including applying calculated registrations, e.g. using the CMTK registration suite, and analysed. There is also a simple representation for neurons that have been subjected to 3D skeletonisation but not formally traced; this allows morphological comparison between neurons including searches and clustering (via the nat.nblast extension package).

These routines create multiple imputations of missing at random categorical data, and create multiply imputed synthesis of categorical data, with or without structural zeros. Imputations and syntheses are based on Dirichlet process mixtures of multinomial distributions, which is a non-parametric Bayesian modeling approach that allows for flexible joint modeling, described in Manrique-Vallier and Reiter (2014) <doi:10.1080/10618600.2013.844700>.

This package provides functions for working with (grouped) multivariate normal variance mixture distributions (evaluation of distribution functions and densities, random number generation and parameter estimation), including Student's t distribution for non-integer degrees-of-freedom as well as the grouped t distribution and copula with multiple degrees-of-freedom parameters. See <doi:10.18637/jss.v102.i02> for a high-level description of select functionality.

This package provides a simple function for easier package loading and auto-installation.

Includes functions and examples to compute NEAT, the Network Enrichment Analysis Test described in Signorelli et al. (2016, <DOI:10.1186/s12859-016-1203-6>).

Assist novice developers when preparing a single package or a set of integrated packages to submit to CRAN. Automate the following individual or batch processing: check local source packages; build local .tar.gz source files; install packages from local .tar.gz files; detect conflicts between function names in the environment.

This package provides tools for working with nonlinear least squares problems. For the estimation of models reliable and robust tools than nls(), where the the Gauss-Newton method frequently stops with singular gradient messages. This is accomplished by using, where possible, analytic derivatives to compute the matrix of derivatives and a stabilization of the solution of the estimation equations. Tools for approximate or externally supplied derivative matrices are included. Bounds and masks on parameters are handled properly.

An application for the empirical extrapolation of time features selecting and summarizing the most relevant patterns in time sequences.

This package provides methods to reduce confounding bias from unmeasured confounders in observational studies of vaccine efficacy using negative control outcomes.

This package provides gradient-based MCMC sampling algorithms for use with the MCMC engine provided by the nimble package. This includes two versions of Hamiltonian Monte Carlo (HMC) No-U-Turn (NUTS) sampling, and (under development) Langevin samplers. The `NUTS_classic` sampler implements the original HMC-NUTS algorithm as described in Hoffman and Gelman (2014) <doi:10.48550/arXiv.1111.4246>. The `NUTS` sampler is a modern version of HMC-NUTS sampling matching the HMC sampler available in version 2.32.2 of Stan (Stan Development Team, 2023). In addition, convenience functions are provided for generating and modifying MCMC configuration objects which employ HMC sampling. Functionality of the nimbleHMC package is described further in Turek, et al (2024) <doi: 10.21105/joss.06745>.

This package provides a number series generator that creates number series items based on cognitive models.

To add the table of numbers at risk below the Kaplan-Meier plot.

This package provides efficient methods to compute co-occurrence matrices, pointwise mutual information (PMI) and singular value decomposition (SVD). In the biomedical and clinical settings, one challenge is the huge size of databases, e.g. when analyzing data of millions of patients over tens of years. To address this, this package provides functions to efficiently compute monthly co-occurrence matrices, which is the computational bottleneck of the analysis, by using the RcppAlgos package and sparse matrices. Furthermore, the functions can be called on SQL databases, enabling the computation of co-occurrence matrices of tens of gigabytes of data, representing millions of patients over tens of years. Partly based on Hong C. (2021) <doi:10.1038/s41746-021-00519-z>.

Build and run spatially explicit agent-based models using only the R platform. NetLogoR follows the same framework as the NetLogo software (Wilensky (1999) <https://www.netlogo.org>) and is a translation in R of the structure and functions of NetLogo'. NetLogoR provides new R classes to define model agents and functions to implement spatially explicit agent-based models in the R environment. This package allows benefiting of the fast and easy coding phase from the highly developed NetLogo framework, coupled with the versatility, power and massive resources of the R software. Examples of two models from the NetLogo software repository (Ants <https://ccl.northwestern.edu/netlogo/models/Ants>) and Wolf-Sheep-Predation (<https://ccl.northwestern.edu/netlogo/models/WolfSheepPredation>), and a third, Butterfly, from Railsback and Grimm (2012) <https://www.railsback-grimm-abm-book.com/>, all written using NetLogoR are available. The NetLogo code of the original version of these models is provided alongside. A programming guide inspired from the NetLogo Programming Guide (<https://docs.netlogo.org/programming.html>) and a dictionary of NetLogo primitives (<https://docs.netlogo.org/dictionary.html>) equivalences are also available. NOTE: To increment time', these functions can use a for loop or can be integrated with a discrete event simulator, such as SpaDES (<https://cran.r-project.org/package=SpaDES>).

Basic implementation of a Gibbs sampler for a Chinese Restaurant Process along with some visual aids to help understand how the sampling works. This is developed as part of a postgraduate school project for an Advanced Bayesian Nonparametric course. It is inspired by Tamara Broderick's presentation on Nonparametric Bayesian statistics given at the Simons institute.