This package provides tools to extract information from the Intergovernmental Organizations ('IGO') Database (v3), provided by the Correlates of War Project <https://correlatesofwar.org/>. See also Pevehouse, J. C. et al. (2020) <doi:10.1177/0022343319881175>.

Implementing a computationally scalable false discovery rate control procedure for replicability analysis based on maximum of p-values. Please cite the manuscript corresponding to this package [Lyu, P. et al., (2023), <doi:10.1093/bioinformatics/btad366>].

Estimation of latent class models with individual covariates for capture-recapture data. See Bartolucci, F. and Forcina, A. (2022), Estimating the size of a closed population by modeling latent and observed heterogeneity, Biometrics, 80(2), ujae017.

Allows users to conduct multivariate distance matrix regression using analytic p-values and compute measures of effect size. For details on the method, see McArtor, Lubke, & Bergeman (2017) <doi:10.1007/s11336-016-9527-8>.

Gibbs sampler for fitting multivariate Bayesian linear regression with shrinkage priors (MBSP), using the three parameter beta normal family. The method is described in Bai and Ghosh (2018) <doi:10.1016/j.jmva.2018.04.010>.

Counting process structure is fundamental to model time varying covariates. This package restructures dataframes in the counting process format for one or more variables. F. W. Dekker, et al. (2008) <doi:10.1038/ki.2008.328>.

Several functions can be used to analyze neuroimaging data using multivariate methods based on the msma package. The functions used in the book entitled "Multivariate Analysis for Neuroimaging Data" (2021, ISBN-13: 978-0367255329) are contained.

This package performs variable selection using the structured screen-and-select (S3VS) framework in linear models, generalized linear models with binary data, and survival models such as the Cox model and accelerated failure time (AFT) model.

This package provides basic functionality for labeling iso- & anisotropic percolation clusters on 2D & 3D square lattices with various lattice sizes, occupation probabilities, von Neumann & Moore (1,d)-neighborhoods, and random variables weighting the percolation lattice sites.

This package provides a bioinformatics tool for the estimation of the tumor purity from sequencing data. It uses the set of putative clonal somatic single nucleotide variants within copy number neutral segments to call tumor cellularity.

This estimates precise weaning ages for a given skeletal population by analyzing the stable nitrogen isotope ratios of them. Bone collagen turnover rates estimated anew and the approximate Bayesian computation (ABC) were adopted in this package.

REAPER is a digital audio production application offering multitrack audio and MIDI recording, editing, processing, mixing and mastering toolset. It supports a vast range of hardware, digital formats and plugins, and can be comprehensively extended, scripted and modified.

This package provides a set of tools to process and calculate metrics on point clouds derived from terrestrial LiDAR (Light Detection and Ranging; TLS). Its creation is based on key aspects of the TLS application in forestry and ecology. Currently, the main routines are based on filtering, neighboring features of points, voxelization, canopy structure, and the creation of artificial stands. It is written using data.table and C++ language and in most of the functions it is possible to use parallel processing to speed-up the routines.

Linear model calculations are made for many random versions of data. Using residual randomization in a permutation procedure, sums of squares are calculated over many permutations to generate empirical probability distributions for evaluating model effects. Additionally, coefficients, statistics, fitted values, and residuals generated over many permutations can be used for various procedures including pairwise tests, prediction, classification, and model comparison. This package should provide most tools one could need for the analysis of high-dimensional data, especially in ecology and evolutionary biology, but certainly other fields, as well.

This package provides a framework for processing and visualization of chromatographically separated and single-spectra mass spectral data. It imports from AIA/ANDI NetCDF, mzXML, mzData and mzML files. It preprocesses data for high-throughput, untargeted analyte profiling.

GAMs, GAMMs and other generalized ridge regression with multiple smoothing parameter estimation by GCV, REML or UBRE/AIC. The library includes a gam() function, a wide variety of smoothers, JAGS support and distributions beyond the exponential family.

This package provides a system for organizing column names in data. It is aimed at supporting a prefix-based and suffix-based column naming scheme. It extends dplyr functionality to add ordering by function and more explicit renaming.

Assigning probability scores to protein interactions captured in affinity purification mass spectrometry (AP-MS) expriments to infer protein-protein interactions. The output would facilitate non-specific background removal as contaminants are commonly found in AP-MS data.

Pair of simple convenience functions to convert a vector of birth dates to age and age distributions. These functions may be helpful when related age and custom age distributions are desired given a vector of birth dates.

Fast tool to calculate the Adjusted Market Inefficiency Measure following Tran & Leirvik (2019) <doi:10.1016/j.frl.2019.03.004>. This tool provides rolling window estimates of the Adjusted Market Inefficiency Measure for multiple instruments simultaneously.

Instructor-developed tools for Analytics and Quantitative Methods (AQM) courses at Babson College. Included are compact descriptive statistics for data frames and lists, expanded reporting and graphics for linear regressions, and formatted reports for best subsets analyses.

Given a collection of intervals with integer start and end positions, find recurrently targeted regions and estimate the significance of finding. Randomization is implemented by parallel methods, either using local host machines, or submitting grid engine jobs.

Connectome Predictive Modelling (CPM) (Shen et al. (2017) <doi:10.1038/nprot.2016.178>) is a method to predict individual differences in behaviour from brain functional connectivity. cpmr provides a simple yet efficient implementation of this method.

Estimate the direct and indirect (mediation) effects of treatment on the outcome when intermediate variables (mediators) are compositional and high-dimensional. Sohn, M.B. and Li, H. (2017). Compositional Mediation Analysis for Microbiome Studies. (AOAS: In revision).