This package provides a function for the estimation of mixture of longitudinal factor analysis models using the iterative expectation-maximization algorithm (Ounajim, Slaoui, Louis, Billot, Frasca, Rigoard (2023) <doi:10.1002/sim.9804>) and several tools for visualizing and interpreting the models parameters.

An easy-to-use workflow that provides tools to create, update and fill literature matrices commonly used in research, specifically epidemiology and health sciences research. The project is born out of need as an easyâ toâ use tool for my research methods classes.

This package provides functions to estimate the kinship matrix of individuals from a large set of biallelic SNPs, and extract inbreeding coefficients and the generalized FST (Wright's fixation index). Method described in Ochoa and Storey (2021) <doi:10.1371/journal.pgen.1009241>.

Convenient structures for creating, sourcing, reading, writing and manipulating ordinal preference data. Methods for writing to/from PrefLib formats. See Nicholas Mattei and Toby Walsh "PrefLib: A Library of Preference Data" (2013) <doi:10.1007/978-3-642-41575-3_20>.

Integrates the 13C nuclear magnetic resonance spectra using different integration ranges. Output depends on the method chosen. For the Molecular Mixing Model, a measurement of the fitting quality is given by its R-factor. For more details see: <doi:10.5281/zenodo.10137768>.

Implementation for sparse logistic functional principal component analysis (SLFPCA). SLFPCA is specifically developed for functional binary data, and the estimated eigenfunction can be strictly zero on some sub-intervals, which is helpful for interpretation. The crucial function of this package is SLFPCA().

Builds, evaluates and validates a nomogram with survey data and right-censored outcomes. As described in Capanu (2015) <doi:10.18637/jss.v064.c01>, the package contains functions to create the nomogram, validate it using bootstrap, as well as produce the calibration plots.

Fit a threshold regression model for Interval Censored Data based on the first-hitting-time of a boundary by the sample path of a Wiener diffusion process. The threshold regression methodology is well suited to applications involving survival and time-to-event data.

This package provides functions to design phase 1 trials using an isotonic regression based design incorporating time-to-event information. Simulation and design functions are available, which incorporate information about followup and DLTs, and apply isotonic regression to devise estimates of DLT probability.

This package provides a collection of statistical tests for martingale difference hypothesis, including automatic portmanteau test (Escansiano and Lobato, 2009) <doi:10.1016/j.jeconom.2009.03.001> and automatic variance ratio test (Kim, 2009) <doi:10.1016/j.frl.2009.04.003>.

It shows the connections between selected clusters from the latest time point and the clusters from all the previous time points. The transition matrices between time point t and t+1 are obtained from Waddington-OT analysis <https://github.com/ScialdoneLab/WOTPLY>.

The package implements MBASED algorithm for detecting allele-specific gene expression from RNA count data, where allele counts at individual loci (SNVs) are integrated into a gene-specific measure of ASE, and utilizes simulations to appropriately assess the statistical significance of observed ASE.

The package ASGSCA (Association Study using Generalized Structured Component Analysis) provides tools to model and test the association between multiple genotypes and multiple traits, taking into account the prior biological knowledge. Genes, and clinical pathways are incorporated in the model as latent variables.

This package provides an implementation of Adaptive Base Error Model in Ultra-deep Sequencing data (ABEMUS), which combines platform-specific genetic knowledge and empirical signal to readily detect and quantify somatic single nucleotide variants (SNVs) in circulating cell free DNA (cfDNA).

This package provides a collection of high-performance utilities. It can be used to compute distances, correlations, autocorrelations, clustering, and other tasks. It also contains a graph clustering algorithm described in MetaCell analysis of single-cell RNA-seq data using K-nn graph partitions.

This package implements tools for weighted network visualization and analysis, as well as Gaussian graphical model computation. It contains graph plotting methods, and tools for psychometric data visualization and graphical model estimation. See Epskamp et al. (2012) doi:10.18637/jss.v048.i04.

The x-resize command detects physical display resolution changes via udev and invokes the xrandr command to reconfigure the active display resolution accordingly. It can be used to implement dynamic resize support for desktop environments that lack native support such as Xfce.

The tools in this package are intended to help researchers assess multiple treatment-covariate interactions with data from a parallel-group randomized controlled clinical trial. The methods implemented in the package were proposed in Kovalchik, Varadhan and Weiss (2013) <doi: 10.1002/sim.5881>.

Generates data for challenging machine learning models in Arena <https://arena.drwhy.ai> - an interactive web application. You can start the server with XAI (Explainable Artificial Intelligence) plots to be generated on-demand or precalculate and auto-upload data file beside shareable Arena URL.

Extract, visualize and summarize aerial movements of birds and insects from weather radar data. See Dokter, A. M. et al. (2018) "bioRad: biological analysis and visualization of weather radar data" <doi:10.1111/ecog.04028> for a software paper describing package and methodologies.

This package provides data import and offers 3 daily snapshot functions from securities of varying prices traded on the Bolivian Securities Exchange, website <https://www.bbv.com.bo/>. The snapshots include a detailed list, scatter plot correlation, and descriptive statistics table for the securities.

An implementation of Jon Kleinberg's burst detection algorithm (Kleinberg (2003) <doi:10.1023/A:1024940629314>). Uses an infinite Markov model to detect periods of increased activity in a series of discrete events with known times, and provides a simple visualization of the results.

This package implements bidirectional two-stage least squares (Bi-TSLS) estimation for identifying bidirectional causal effects between two variables in the presence of unmeasured confounding. The method uses proxy variables (negative control exposure and outcome) along with at least one covariate to handle confounding.

This package provides functions to perform Bayesian nonparametric univariate and multivariate density estimation and clustering, by means of Pitman-Yor mixtures, and dependent Dirichlet process mixtures for partially exchangeable data. See Corradin et al. (2021) <doi:10.18637/jss.v100.i15> for more details.