Estimates Boltzmannâ Lotkaâ Volterra (BLV) interaction model efficiently. Enables programmatic and graphical exploration of the solution space of BLV models when parameters are varied. See Wilson, A. (2008) <dx.doi.org/10.1098/rsif.2007.1288>.

Client for the Binance <https://www.binance.com/> Spot, Futures, and Options REST APIs. Provides helper functions for signed and unsigned requests, market data retrieval, account access, and order management with data.table output by default.

This package provides a Metropolis-coupled Markov chain Monte Carlo sampler, post-processing and parameter estimation functions, and plotting utilities for the generalized graded unfolding model of Roberts, Donoghue, and Laughlin (2000) <doi:10.1177/01466216000241001>.

Non-linear/linear hybrid method for batch-effect correction that uses Mutual Nearest Neighbors (MNNs) to identify similar cells between datasets. Reference: Loza M. et al. (NAR Genomics and Bioinformatics, 2020) <doi:10.1093/nargab/lqac022>.

This package provides functions for cost-sensitive multi-criteria ensemble selection (CSMES) (as described in De bock et al. (2020) <doi:10.1016/j.ejor.2020.01.052>) for cost-sensitive learning under unknown cost conditions.

Builds co-occurrence matrices based on spatial raster data. It includes creation of weighted co-occurrence matrices (wecoma) and integrated co-occurrence matrices (incoma; Vadivel et al. (2007) <doi:10.1016/j.patrec.2007.01.004>).

Estimates the sample size for a test or a trial based on repeated simulation using a model based approach. Implements a method by Maruo et al. (2018) <doi:10.1080/19466315.2017.1349689> and an extension.

Create engaging population and point plots charts in R. ggpop allows users to represent population data and points proportionally using customizable icons, facilitating the creation of circular representative population charts as well as any point-plots.

Convert GDP time series data from one unit to another. All common GDP units are included, i.e. current and constant local currency units, US$ via market exchange rates and international dollars via purchasing power parities.

This package provides methods for estimating univariate long memory-seasonal/cyclical Gegenbauer time series processes. See for example (2022) <doi:10.1007/s00362-022-01290-3>. Refer to the vignette for details of fitting these processes.

Interfaces GAMS data (*.gdx) files with data.table's using the GAMS R package gdxrrw'. The gdxrrw package is available on the GAMS wiki: <https://support.gams.com/doku.php?id=gdxrrw:interfacing_gams_and_r>.

Offers efficient algorithms for fitting regularization paths for lasso or elastic-net penalized regression models with Huber loss, quantile loss or squared loss. Reference: Congrui Yi and Jian Huang (2017) <doi:10.1080/10618600.2016.1256816>.

Exact significance tests for a changepoint in linear or multiple linear regression. Confidence regions with exact coverage probabilities for the changepoint. Based on Knowles, Siegmund and Zhang (1991) <doi:10.1093/biomet/78.1.15>.

This package implements Multi-Group Sparse Discriminant Analysis proposal of I.Gaynanova, J.Booth and M.Wells (2016), Simultaneous sparse estimation of canonical vectors in the p>>N setting, JASA <doi:10.1080/01621459.2015.1034318>.

This package provides tools for performing mathematical morphology operations, such as erosion and dilation, on data of arbitrary dimensionality. Can also be used for finding connected components, resampling, filtering, smoothing and other image processing-style operations.

This package provides utilities for processing of Oxy-Bisulfite microarray data (e.g. via the Illumina Infinium platform, <http://www.illumina.com>) with tandem arrays, one using conventional bisulfite conversion, the other using oxy-bisulfite conversion.

This package provides functions are available to calibrate designs over a range of posterior and predictive thresholds, to plot the various design options, and to obtain the operating characteristics of optimal accuracy and optimal efficiency designs.

An user-friendly framework to preprocess raw item scores of questionnaires into factors or scores and standardize them. Standardization can be made either by their normalization in representative sample, or by import of premade scoring table.

This package implements the structural forest methodology for the heterogeneous newsvendor model. The package provides tools to prepare data, fit honest newsvendor trees and forests, and obtain point and distributional predictions for demand decisions under uncertainty.

This package implements the Scout method for regression, described in "Covariance-regularized regression and classification for high-dimensional problems", by Witten and Tibshirani (2008), Journal of the Royal Statistical Society, Series B 71(3): 615-636.

Code for describing and manipulating scuba diving profiles (depth-time curves) and decompression models, for calculating the predictions of decompression models, for calculating maximum no-decompression time and decompression tables, and for performing mixed gas calculations.

This package provides a set of tools for managing time-series data, with a particular emphasis on defining various frequency types such as daily and weekly. It also includes functionality for converting data between different frequencies.

Implementation of Time-course Gene Set Analysis (TcGSA), a method for analyzing longitudinal gene-expression data at the gene set level. Method is detailed in: Hejblum, Skinner & Thiebaut (2015) <doi: 10.1371/journal.pcbi.1004310>.

Implementation of shiny app to visualize adverse events based on the Common Terminology Criteria for Adverse Events (CTCAE) using stacked correspondence analysis as described in Diniz et. al (2021)<doi:10.1186/s12874-021-01368-w>.