Does prediction in the case of a censored survival outcome, or a regression outcome, using the "supervised principal component" approach. Superpc is especially useful for high-dimensional data when the number of features p dominates the number of samples n (p >> n paradigm), as generated, for instance, by high-throughput technologies.

Develop outstanding shiny apps for iOS and Android as well as beautiful shiny gadgets. shinyMobile is built on top of the latest Framework7 template <https://framework7.io>. Discover 14 new input widgets (sliders, vertical sliders, stepper, grouped action buttons, toggles, picker, smart select, ...), 2 themes (light and dark), 12 new widgets (expandable cards, badges, chips, timelines, gauges, progress bars, ...) combined with the power of server-side notifications such as alerts, modals, toasts, action sheets, sheets (and more) as well as 3 layouts (single, tabs and split).

Implementation of small area estimation using Hierarchical Bayesian (HB) Method when auxiliary variable measured with error. The rjags package is employed to obtain parameter estimates. For the references, see Rao and Molina (2015) <doi:10.1002/9781118735855>, Ybarra and Lohr (2008) <doi:10.1093/biomet/asn048>, and Ntzoufras (2009, ISBN-10: 1118210352).

This package provides a rule-based simulation environment for modeling real-world athlete performance, dynamic training sessions, hierarchical variance, and missing telemetry data.

This package provides a tool to plot data with a large sample size using shiny and plotly'. Relatively small samples are obtained from the original data using a specific algorithm. The samples are updated according to a user-defined x range. Jonas Van Der Donckt, Jeroen Van Der Donckt, Emiel Deprost (2022) <https://github.com/predict-idlab/plotly-resampler>.

This package implements a general algorithm for estimating Sparse Multiple Index (SMI) models for nonparametric forecasting and prediction. Estimation of SMI models requires the Gurobi mixed integer programming (MIP) solver via the gurobi R package. To use this functionality, the Gurobi Optimizer must be installed, and a valid license obtained and activated from <https://www.gurobi.com>. The gurobi R package must then be installed and configured following the instructions at <https://support.gurobi.com/hc/en-us/articles/14462206790033-How-do-I-install-Gurobi-for-R>. The package also includes functions for fitting nonparametric additive models with backward elimination, group-wise additive index models, and projection pursuit regression models as benchmark comparison methods. In addition, it provides tools for generating prediction intervals to quantify uncertainty in point forecasts produced by the SMI model and benchmark models, using the classical block bootstrap and a new method called conformal bootstrap, which integrates block bootstrap with split conformal prediction.

Regularized version of partial least square approaches providing sparse, group, and sparse group versions of partial least square regression models (Liquet, B., Lafaye de Micheaux, P., Hejblum B., Thiebaut, R. (2016) <doi:10.1093/bioinformatics/btv535>). Version of PLS Discriminant analysis is also provided.

This package provides functions for sample size estimation and simulation in clinical trials. Includes methods for selecting the best group using the Indifference-zone approach, as well as designs for non-inferiority, equivalence, and negative binomial models. For the sample size calculation for non-inferiority of vaccines, the approach is based on Fleming, Powers, and Huang (2021) <doi:10.1177/1740774520988244>. The Indifference-zone approach is based on Sobel and Huyett (1957) <doi:10.1002/j.1538-7305.1957.tb02411.x> and Bechhofer, Santner, and Goldsman (1995, ISBN:978-0-471-57427-9).

Normalization based a subset of negative control probes as described in Subset quantile normalization using negative control features'. Wu Z, Aryee MJ, J Comput Biol. 2010 Oct;17(10):1385-95 [PMID 20976876].

Builds regression trees and random forests for longitudinal or functional data using a spline projection method. Implements and extends the work of Yu and Lambert (1999) <doi:10.1080/10618600.1999.10474847>. This method allows trees and forests to be built while considering either level and shape or only shape of response trajectories.

Add indicators (spinner, progress bar, gif) in your shiny applications to show the user that the server is busy. And other tools to let your users know something is happening (send notifications, reports, ...).

This package provides a coherent, quota-aware workflow layer over the Elsevier Scopus Search API <https://dev.elsevier.com/sc_apis.html>. It builds reproducible search plans, retrieves records with rate-limit handling, retry with back-off and optional resumable caching, normalises results to a stable tidy schema, extracts and tracks changes in Digital Object Identifiers (DOIs), sizes sets of concepts and their intersections, compares publication trends across topics, writes the search up as a reproducible record for a methods section following the PRISMA-S reporting standard (Rethlefsen and others, 2021) <doi:10.1186/s13643-020-01542-z> and exports to formats compatible with downstream bibliometric tools. Network and API errors are surfaced as typed conditions so that callers can respond to them programmatically. Scopus is a trademark of Elsevier. This package is an independent client and is not affiliated with or endorsed by Elsevier.

R language bindings for SolveBio's API. SolveBio is a biomedical knowledge hub that enables life science organizations to collect and harmonize the complex, disparate "multi-omic" data essential for today's R&D and BI needs.

An easy to use implementation of routine structural missing data diagnostics with functions to visualize the proportions of missing observations, investigate missing data patterns and conduct various empirical missing data diagnostic tests. Reference: Weberpals J, Raman SR, Shaw PA, Lee H, Hammill BG, Toh S, Connolly JG, Dandreo KJ, Tian F, Liu W, Li J, Hernández-Muñoz JJ, Glynn RJ, Desai RJ. smdi: an R package to perform structural missing data investigations on partially observed confounders in real-world evidence studies. JAMIA Open. 2024 Jan 31;7(1):ooae008. <doi:10.1093/jamiaopen/ooae008>.

This package implements an approach aimed at assessing the accuracy and effectiveness of raw scores obtained in scales that contain locally dependent items. The program uses as input the calibration (structural) item estimates obtained from fitting extended unidimensional factor-analytic solutions in which the existing local dependencies are included. Measures of reliability (Omega) and information are proposed at three levels: (a) total score, (b) bivariate-doublet, and (c) item-by-item deletion, and are compared to those that would be obtained if all the items had been locally independent. All the implemented procedures can be obtained from: (a) linear factor-analytic solutions in which the item scores are treated as approximately continuous, and (b) non-linear solutions in which the item scores are treated as ordered-categorical. A detailed guide can be obtained at the following url.

This package provides tools for smoothing and tidying spatial features (i.e. lines and polygons) to make them more aesthetically pleasing. Smooth curves, fill holes, and remove small fragments from lines and polygons.

This package provides a small POSIX sockets interface to R, enabling direct network communication from R for applications that need low-level socket control or lightweight client and server connections. This package provides IPv4 and IPv6 support with TCP/UDP protocols. It functional API with socket_create and an R6 interface through Socket object.

Computes confidence intervals for variance using the Chi-Square distribution, without requiring raw data. Wikipedia (2025) <https://en.wikipedia.org/wiki/Chi-squared_distribution>. All-in-One Chi Distribution CI provides functions to calculate confidence intervals for the population variance based on a chi-squared distribution, utilizing a sample variance and sample size. It offers only a simple all-in-one method for quick calculations to find the CI for Chi Distribution.

This package provides a meta-package that loads the complete sitrep ecosystem for applied epidemiology analysis. This package provides report templates and automatically loads companion packages, including epitabulate (for epidemiological tables), epidict (for data dictionaries), epikit (for epidemiological utilities), and apyramid (for age-sex pyramids). Simply load sitrep to access all functions from the ecosystem.

It provides miscellaneous sequence analysis functions for describing episodes in individual sequences, measuring association between domains in multidimensional sequence analysis (see Piccarreta (2017) <doi:10.1177/0049124115591013>), heat maps of sequence data, Globally Interdependent Multidimensional Sequence Analysis (see Robette et al (2015) <doi:10.1177/0081175015570976>), smoothing sequences for index plots (see Piccarreta (2012) <doi:10.1177/0049124112452394>), coding sequences for Qualitative Harmonic Analysis (see Deville (1982)), measuring stress from multidimensional scaling factors (see Piccarreta and Lior (2010) <doi:10.1111/j.1467-985X.2009.00606.x>), symmetrical (or canonical) Partial Least Squares (see Bry (1996)).

Allows users to list data structures using path-based navigation. Provides intuitive methods for storing, accessing, and manipulating nested data through simple path strings. Key features include strict mode validation, path existence checking, recursive operations, and automatic parent-level creation. Designed for use cases requiring organized storage of complex nested data while maintaining simple access patterns. Particularly useful for configuration management, nested settings, and any application where data naturally forms a tree-like structure.

An algorithm that trains a meta-learning procedure that combines screening and wrapper methods to find a set of extremely low-dimensional attribute combinations. This package works on top of the caret package and proceeds in a forward-step manner. More specifically, it builds and tests learners starting from very few attributes until it includes a maximal number of attributes by increasing the number of attributes at each step. Hence, for each fixed number of attributes, the algorithm tests various (randomly selected) learners and picks those with the best performance in terms of training error. Throughout, the algorithm uses the information coming from the best learners at the previous step to build and test learners in the following step. In the end, it outputs a set of strong low-dimensional learners.

This is an all-encompassing suite to facilitate the simulation of so-called quantities of interest by way of a multivariate normal distribution of the regression model's coefficients and variance-covariance matrix.

Assessment of the distributions of baseline continuous and categorical variables in randomised trials. This method is based on the Carlisle-Stouffer method with Monte Carlo simulations. It calculates p-values for each trial baseline variable, as well as combined p-values for each trial - these p-values measure how compatible are distributions of trials baseline variables with random sampling. This package also allows for graphically plotting the cumulative frequencies of computed p-values. Please note that code was partly adapted from Carlisle JB, Loadsman JA. (2017) <doi:10.1111/anae.13650>.