Performing cell type annotation based on cell markers from a unified database. The approach utilizes correlation-based approach combined with association analysis using Fisher-exact and phyper statistical tests (Upton, Graham JG. (1992) <DOI:10.2307/2982890>).

List of english scrabble words as listed in the OTCWL2014 <https://www.scrabbleplayers.org/w/Official_Tournament_and_Club_Word_List_2014_Edition>. Words are collated from the Word Game Dictionary <https://www.wordgamedictionary.com/word-lists/>.

Diagnostics for non-linear mixed-effects (population) models from NONMEM <https://www.iconplc.com/solutions/technologies/nonmem/>. xpose facilitates data import, creation of numerical run summary and provide ggplot2'-based graphics for data exploration and model diagnostics.

This package contains functions useful for reading in Licor 6800 files, correcting and analyzing rapid A/Ci response (RACiR) data. Requires some user interaction to adjust the calibration (empty chamber) data file to a useable range. Calibration uses a 1st to 5th order polynomial as suggested in Stinziano et al. (2017) <doi:10.1111/pce.12911>. Data can be processed individually or batch processed for all files paired with a given calibration file. RACiR is a trademark of LI-COR Biosciences, and used with permission.

CNViz takes probe, gene, and segment-level log2 copy number ratios and launches a Shiny app to visualize your sample's copy number profile. You can also integrate loss of heterozygosity (LOH) and single nucleotide variant (SNV) data.

This package tracks reading and writing within R scripts that are organized into a directed acyclic graph. It contains an interactive Shiny application adaprApp(). It uses Git and file hashes to track version histories of inputs and outputs.

This package provides implementations of the family of map() functions from the purrr package that can be resolved using any future-supported backend, e.g. parallel on the local machine or distributed on a compute cluster.

Iterated race is an extension of the Iterated F-race method for the automatic configuration of optimization algorithms, that is, (offline) tuning their parameters by finding the most appropriate settings given a set of instances of an optimization problem.

This package computes various confidence intervals (CI) for the Kaplan-Meier estimator, namely: Petos CI, Rothman CI, CIs based on Greenwoods variance, Thomas and Grunkemeier CI and the simultaneous confidence bands by Nair and Hall and Wellner.

Find an upper bound for the total amount of overstatement of assets in a set of accounts, or estimate the amount of sales tax owed on a collection of transactions (Meeden and Sargent, 2007, <doi:10.1080/03610920701386802>).

This package provides methods for fitting additive hazards model. Perform the maximum likelihood method as well as the traditional Aalen's method for estimating the additive hazards model. For details see Chengyuan Lu(2021) <arXiv:2004.06156>.

This package provides a minimalist web framework for developing application programming interfaces in R that provides a flexible framework for handling common HTTP-requests, errors, logging, and an ability to integrate any R code as server middle-ware.

Fits constrained groupwise additive index models and provides functions for inference and interpretation of these models. The method is described in Masselot, Chebana, Campagna, Lavigne, Ouarda, Gosselin (2022) "Constrained groupwise additive index models" <doi:10.1093/biostatistics/kxac023>.

The COSSO regularization method automatically estimates and selects important function components by a soft-thresholding penalty in the context of smoothing spline ANOVA models. Implemented models include mean regression, quantile regression, logistic regression and the Cox regression models.

This package implements the Changepoints for a Range of Penalties (CROPS) algorithm of Haynes et al. (2017) <doi:10.1080/10618600.2015.1116445> for finding all of the optimal segmentations for multiple penalty values over a continuous range.

This package contains the normalizing and variance stabilizing Data-Driven Haar-Fisz algorithm. Also contains related algorithms for simulating from certain microarray gene intensity models and evaluation of certain transformations. Contains cDNA and shipping credit flow data.

You can load a schema from a DTR (data type registry) as an R object. Use this schema to write your data in JSON-LD (JavaScript Object Notation for Linked Data) format to make it machine readable.

Fast fitting of generalised linear models on moderately large datasets, by taking an initial sample, fitting in memory, then evaluating the score function for the full data in the database. Thomas Lumley <doi:10.1080/10618600.2019.1610312>.

Adjust Estimates of Learning for Guessing. The package provides standard guessing correction, and a latent class model that leverages informative pre-post transitions. For details of the latent class model, see <https://gsood.com/research/papers/guess.pdf>.

Analysis of multivariate data using generalized linear latent variable models (gllvm). Estimation is performed using either the Laplace method, variational approximations, or extended variational approximations, implemented via TMB (Kristensen et al. (2016), <doi:10.18637/jss.v070.i05>).

Computing Global Sensitivity Indices from given data using Optimal Transport, as defined in Borgonovo et al (2024) <doi:10.1287/mnsc.2023.01796>. You provide an input sample, an output sample, decide the algorithm, and compute the indices.

Giac <https://www-fourier.ujf-grenoble.fr/~parisse/giac/doc/en/cascmd_en/cascmd_en.html> is a general purpose symbolic algebra software. It powers the graphical interface Xcas'. This package allows to execute Giac commands in R'.

Ridge regression provide biased estimators of the regression parameters with lower variance. The HDBRR ("High Dimensional Bayesian Ridge Regression") function fits Bayesian Ridge regression without MCMC, this one uses the SVD or QR decomposition for the posterior computation.

Several procedures for the hierarchical kernel extreme value process of Reich and Shaby (2012) <DOI:10.1214/12-AOAS591>, including simulation, estimation and spatial extrapolation. The spatial latent variable model <DOI:10.1214/11-STS376> is also included.