Implementation for kernel functional partial least squares (KFPLS) method. KFPLS method is developed for functional nonlinear models, and the method does not require strict constraints for the nonlinear structures. The crucial function of this package is KFPLS().

Framework for adding authentication to shiny applications. Provides flexibility as compared to other options for where user credentials are saved, allows users to create their own accounts, and password reset functionality. Bryer (2024) <doi:10.5281/zenodo.10987876>.

Maximum likelihood Gaussian process modeling for univariate and multi-dimensional outputs with diagnostic plots following Santner et al (2003) <doi:10.1007/978-1-4757-3799-8>. Contact the maintainer for a package version that includes sensitivity analysis.

Estimation of relatively complex nonlinear mixed-effects models, including the Sigmoidal Mixed Model and the Piecewise Linear Mixed Model with abrupt or smooth transition, through a single intuitive line of code and with automated generation of starting values.

This package provides tools for 4D nucleome imaging. Quantitative analysis of the 3D nuclear landscape recorded with super-resolved fluorescence microscopy. See Volker J. Schmid, Marion Cremer, Thomas Cremer (2017) <doi:10.1016/j.ymeth.2017.03.013>.

Perform flexible and quick calculations for Demand and Supply Planning, such as projected inventories and coverages, as well as replenishment plan. For any time bucket, daily, weekly or monthly, and any granularity level, product or group of products.

Fits the Piecewise Exponential distribution with random time grids using the clustering structure of the Product Partition Models. Details of the implemented model can be found in Demarqui et al. (2008) <doi:10.1007/s10985-008-9086-0>.

Computes clustering by fitting Gaussian mixture models (GMM) via stochastic approximation following the methods of Nguyen and Jones (2018) <doi:10.1201/9780429446177>. It also provides some test data generation and plotting functionality to assist with this process.

To calculate the standard error of measurement (SEM) to assess the observer variability (inter- and intra-observer variation). The methods used in this package are referenced from Zoran B. PopoviÄ (2017) <doi:10.21037/cdt.2017.03.12>.

This package provides a collection of utility functions that facilitate looking up vector values from a lookup table, annotate values in at table for clearer viewing, and support a safer approach to vector sampling, sequence generation, and aggregation.

This package provides a part of precision agriculture is linked to the spectral image obtained from the cameras. With the image information of the agricultural experiment, the included functions facilitate the collection of spectral data associated with the experimental units. Some designs generated in R are linked to the images, which allows the use of the information of each pixel of the image in the experimental unit and the treatment. Tables and images are generated for the analysis of the precision agriculture experiment during the entire vegetative period of the crop.

This package implements the Ensemble of Gene Set Enrichment Analyses (EGSEA) method for gene set testing. EGSEA algorithm utilizes the analysis results of twelve prominent GSE algorithms in the literature to calculate collective significance scores for each gene set.

contains eight technical replicate data set and a three replicate dilution series of the MS Qual/Quant Quality Control Mix standard sample (Sigma-Aldrich, Buchs, Switzerland) measured on five different mass spectrometer platforms at the Functional Genomics Center Zurich.

scTHI is an R package to identify active pairs of ligand-receptors from single cells in order to study,among others, tumor-host interactions. scTHI contains a set of signatures to classify cells from the tumor microenvironment.

This package provides a framework for the quantification and analysis of short genomic reads. It covers a complete workflow starting from raw sequence reads, over creation of alignments and quality control plots, to the quantification of genomic regions of interest.

This package orders panels in scatterplot matrices and parallel coordinate displays by some merit index. It contains various indices of merit, ordering functions, and enhanced versions of pairs and parcoord which color panels according to their merit level.

This is a deprecated package for calculating pairwise multiple comparisons of mean rank sums. This package is superseded by the novel PMCMRplus package. The PMCMR package is no longer maintained, but kept for compatibility of dependent packages for some time.

This package provides tools for multiple imputation of missing data in multilevel modeling. It includes a user-friendly interface to the packages pan and jomo, and several functions for visualization, data management and the analysis of multiply imputed data sets.

This package simplifies custom CSS styling of both shiny and rmarkdown via Bootstrap Sass. It supports both Bootstrap 3 and 4 as well as their various Bootswatch themes. An interactive widget is also provided for previewing themes in real time.

Bayesian model and associated tools for generating estimates of total naloxone kit numbers distributed and used from naloxone kit orders data. Provides functions for generating simulated data of naloxone kit use and functions for generating samples from the posterior.

Bias- and Uncertainty-Corrected Sample Size. BUCSS implements a method of correcting for publication bias and uncertainty when planning sample sizes in a future study from an original study. See Anderson, Kelley, & Maxwell (2017; Psychological Science, 28, 1547-1562).

Running and comparing meta-analyses of data with hierarchical Bayesian models in Stan, including convenience functions for formatting data, plotting and pooling measures specific to meta-analysis. This implements many models from Meager (2019) <doi:10.1257/app.20170299>.

CemCO algorithm, a model-based (Gaussian) clustering algorithm that removes/minimizes the effects of undesirable covariates during the clustering process both in cluster centroids and in cluster covariance structures (Relvas C. & Fujita A., (2020) <arXiv:2004.02333>).

Efficiently create dummies of all factors and character vectors in a data frame. Support is included for learning the categories on one data set (e.g., a training set) and deploying them on another (e.g., a test set).