Interfaces the python library zuko implementing Masked Autoregressive Flows. See Rozet, Divo and Schnake (2023) <doi:10.5281/zenodo.7625672> and Papamakarios, Pavlakou and Murray (2017) <doi:10.48550/arXiv.1705.07057>.

This package provides functions to estimate weather variables at any position of a landscape [De Caceres et al. (2018) <doi:10.1016/j.envsoft.2018.08.003>].

This package provides functions for multivariate analysis and visualization of neuroimaging data. The package contains the functions and example data used in the book Multivariate Analysis for Neuroimaging Data by Kawaguchi (2021, ISBN: 978-0367255329). It includes utilities for image visualization, image data matrix construction, basis reconstruction, multicomponent visualization, predictive modeling, simulation, and multiblock analysis. Version 3.0 preserves the public interfaces used in the accompanying package vignettes.

Compute the average of a sequence of random vectors in a moving expanding window using a fixed amount of memory.

This package provides tools for calculating Laspeyres, Paasche, and Fisher price and quantity indices.

This package provides a set of functions, classes and methods for performing ABC and ABC/XYZ analyses, identifying overperforming, underperforming and constantly performing items, and plotting, analyzing as well as predicting the temporal development of items.

This package provides tools for general-purpose continuous optimization and feed-forward artificial neural network training using metaheuristic and gradient-based optimization algorithms. The package supports benchmark function optimization, regression, binary classification, and multi-class classification with multilayer perceptrons. The package implements several optimization methods, including particle swarm optimization Kennedy and Eberhart (1995) <doi:10.1109/ICNN.1995.488968>, differential evolution Storn and Price (1997) <doi:10.1023/A:1008202821328>, grey wolf optimizer Mirjalili et al. (2014) <doi:10.1016/j.advengsoft.2013.12.007>, secretary bird optimization Fu et al. (2024) <doi:10.1007/s10462-024-10729-y>, and Adam Kingma and Ba (2015) <doi:10.48550/arXiv.1412.6980>.

Generates multivariate subgaussian stable probabilities using the QRSVN algorithm as detailed in Genz and Bretz (2002) <DOI:10.1198/106186002394> but by sampling positive stable variates not chi/sqrt(nu).

This package provides one function, which is a wrapper around purrr::map() with some extras on top, including parallel computation, progress bars, error handling, and result caching.

Constructs a magic rectangle or a nearly magic rectangle of order p x q for every order for which one exists, together with existence classification and verification utilities. A magic rectangle arranges the integers 1 to p*q so that all row sums are equal and all column sums are equal; it exists exactly when p and q have the same parity, excluding 2 x 2 and degenerate single-row/column cases (Hagedorn, 1999, <doi:10.1016/S0012-365X(99)00041-2>). When p and q have opposite parity a nearly magic rectangle exists instead, with constant sums along one direction and sums differing by at most one along the other (Chai, Singh and Stufken, 2019, Journal of Combinatorial Designs 27(6), 368-376). Implements the constructions of De Los Reyes, Das, Midha and Vellaisamy (2009) for even by even orders, Chai, Das and Midha (2013) for odd by odd orders, and Chai, Singh and Stufken (2019) for the nearly magic (even by odd) case.

This GUI for the mi package walks the user through the steps of multiple imputation and the analysis of completed data.

Computing package for Multidimensional Poverty Index (MPI) using Alkire-Foster method. Given N individuals, each person has D indicators of deprivation, the package compute MPI value to represent the degree of poverty in a population. The inputs are 1) an N by D matrix, which has the element (i,j) represents whether an individual i is deprived in an indicator j (1 is deprived and 0 is not deprived), and 2) the deprivation threshold. The main output is the MPI value, which has the range between zero and one. MPI value is approaching one if almost all people are deprived in all indicators, and it is approaching zero if almost no people are deprived in any indicator. Please see Alkire S., Chatterjee, M., Conconi, A., Seth, S. and Ana Vaz (2014) <doi:10.35648/20.500.12413/11781/ii039> for The Alkire-Foster methodology.

Estimates membership for the Mandelbrot set.

Multivariate Analysis methods and data sets used in John Marden's book Multivariate Statistics: Old School (2015) <ISBN:978-1456538835>. This also serves as a companion package for the STAT 571: Multivariate Analysis course offered by the Department of Statistics at the University of Illinois at Urbana-Champaign ('UIUC').

This package provides weighted versions of several metrics and performance measures used in machine learning, including average unit deviances of the Bernoulli, Tweedie, Poisson, and Gamma distributions, see Jorgensen B. (1997, ISBN: 978-0412997112). The package also contains a weighted version of generalized R-squared, see e.g. Cohen, J. et al. (2002, ISBN: 978-0805822236). Furthermore, dplyr chains are supported.

There are two functions-meta2d and meta3d for detecting rhythmic signals from time-series datasets. For analyzing time-series datasets without individual information, meta2d is suggested, which could incorporates multiple methods from ARSER, JTK_CYCLE and Lomb-Scargle in the detection of interested rhythms. For analyzing time-series datasets with individual information, meta3d is suggested, which takes use of any one of these three methods to analyze time-series data individual by individual and gives out integrated values based on analysis result of each individual.

This package provides functions to estimate start and duration of moult from moult data, based on models developed in Underhill and Zucchini (1988, 1990).

Perform missing value imputation for biological data using the random forest algorithm, the imputation aim to keep the original mean and standard deviation consistent after imputation.

Interface to Apache Commons Email to send emails from R.

Curve Fitting of monotonic(sigmoidal) & non-monotonic(J-shaped) dose-response data. Predicting mixture toxicity based on reference models such as concentration addition', independent action', and generalized concentration addition'.

Implement multiverse style analyses (Steegen S., Tuerlinckx F, Gelman A., Vanpaemal, W., 2016) <doi:10.1177/1745691616658637> to show the robustness of statistical inference. Multiverse analysis is a philosophy of statistical reporting where paper authors report the outcomes of many different statistical analyses in order to show how fragile or robust their findings are. The multiverse package (Sarma A., Kale A., Moon M., Taback N., Chevalier F., Hullman J., Kay M., 2021) <doi:10.31219/osf.io/yfbwm> allows users to concisely and flexibly implement multiverse-style analysis, which involve declaring alternate ways of performing an analysis step, in R and R Notebooks.

Test for independence of two random vectors, learn and report the dependency structure. For more information, see Gorsky, Shai and Li Ma, Multiscale Fisher's Independence Test for Multivariate Dependence, Biometrika, accepted, January 2022.

This package provides a way to estimate and test marginal mediation effects for zero-inflated compositional mediators. Estimates of Natural Indirect Effect (NIE), Natural Direct Effect (NDE) of each taxon, as well as their standard errors and confident intervals, were provided as outputs. Zeros will not be imputed during analysis. See Wu et al. (2022) <doi:10.3390/genes13061049>.

This package provides a set of functions to manage data shared on a MOLGENIS Armadillo server.