Simulating and estimating (regime-switching) Markov chain Gaussian fields with covariance functions of the Gneiting class (Gneiting 2002) <doi:10.1198/016214502760047113>. It supports parameter estimation by weighted least squares and maximum likelihood methods, and produces Kriging forecasts and intervals for existing and new locations.

Evaluate hypotheses concerning the distribution of multinomial proportions using bridge sampling. The bridge sampling routine is able to compute Bayes factors for hypotheses that entail inequality constraints, equality constraints, free parameters, and mixtures of all three. These hypotheses are tested against the encompassing hypothesis, that all parameters vary freely or against the null hypothesis that all category proportions are equal. For more information see Sarafoglou et al. (2020) <doi:10.31234/osf.io/bux7p>.

This package offers three important components: (1) to construct a use-defined linear mixed model, (2) to employ one of linear mixed model approaches: minimum norm quadratic unbiased estimation (MINQUE) (Rao, 1971) for variance component estimation and random effect prediction; and (3) to employ a jackknife resampling technique to conduct various statistical tests. In addition, this package provides the function for model or data evaluations.This R package offers fast computations for large data sets analyses for various irregular data structures.

This package provides a collection of matrix functions for teaching and learning matrix linear algebra as used in multivariate statistical methods. Many of these functions are designed for tutorial purposes in learning matrix algebra ideas using R. In some cases, functions are provided for concepts available elsewhere in R, but where the function call or name is not obvious. In other cases, functions are provided to show or demonstrate an algorithm. In addition, a collection of functions are provided for drawing vector diagrams in 2D and 3D and for rendering matrix expressions and equations in LaTeX.

This package implements structural estimators to estimate preferences and correct for the sample selection bias of observed outcomes in matching markets. This includes one-sided matching of agents into groups (Klein, 2015) <doi:10.17863/CAM.5812> as well as two-sided matching of students to schools (Klein et al., 2024) <doi:10.1016/j.geb.2024.07.003>. The package also contains algorithms to find stable matchings in the three most common matching problems: the stable roommates problem (Irving, 1985) <doi:10.1016/0196-6774(85)90033-1>, the college admissions problem (Gale and Shapley, 1962) <doi:10.2307/2312726>, and the house allocation problem (Shapley and Scarf, 1974) <doi:10.1016/0304-4068(74)90033-0>.

Conduct multi-locus genome-wide association study under the framework of multi-locus random-SNP-effect mixed linear model (mrMLM). First, each marker on the genome is scanned. Bonferroni correction is replaced by a less stringent selection criterion for significant test. Then, all the markers that are potentially associated with the trait are included in a multi-locus genetic model, their effects are estimated by empirical Bayes and all the nonzero effects were further identified by likelihood ratio test for true QTL. Wen YJ, Zhang H, Ni YL, Huang B, Zhang J, Feng JY, Wang SB, Dunwell JM, Zhang YM, Wu R (2018) <doi:10.1093/bib/bbw145>.

Offers a general framework of multivariate mixed-effects models for the joint analysis of multiple correlated outcomes with clustered data structures and potential missingness proposed by Wang et al. (2018) <doi:10.1093/biostatistics/kxy022>. The missingness of outcome values may depend on the values themselves (missing not at random and non-ignorable), or may depend on only the covariates (missing at random and ignorable), or both. This package provides functions for two models: 1) mvMISE_b() allows correlated outcome-specific random intercepts with a factor-analytic structure, and 2) mvMISE_e() allows the correlated outcome-specific error terms with a graphical lasso penalty on the error precision matrix. Both functions are motivated by the multivariate data analysis on data with clustered structures from labelling-based quantitative proteomic studies. These models and functions can also be applied to univariate and multivariate analyses of clustered data with balanced or unbalanced design and no missingness.

Lightweight maps of mammals of the world. These maps are a comprehensive collection of maps aligned with the Mammal Diversity Database taxonomy of the American Society of Mammalogists. They are generated at low resolution for easy access, consultation and manipulation in shapefile format. The package connects to a binary backup hosted in the Digital Ocean cloud service and allows individual or batch download of any mammal species in the mdd taxonomy by providing the scientific species name.

Play and record games of minesweeper using a graphics device that supports event handling. Replay recorded games and save GIF animations of them. Based on classic minesweeper as detailed by Crow P. (1997) <https://minesweepergame.com/math/a-mathematical-introduction-to-the-game-of-minesweeper-1997.pdf>.

Perform multivariate modeling of evolved traits, with special attention to understanding the interplay of the multi-factorial determinants of their origins in complex ecological settings (Stephens, 2007 <doi:10.1016/j.tree.2006.12.003>). This software primarily concentrates on phylogenetic regression analysis, enabling implementation of tree transformation averaging and visualization functionality. Functions additionally support information theoretic approaches (Grueber, 2011 <doi:10.1111/j.1420-9101.2010.02210.x>; Garamszegi, 2011 <doi:10.1007/s00265-010-1028-7>) such as model averaging and selection of phylogenetic models. Accessory functions are also implemented for coef standardization (Cade 2015), selection uncertainty, and variable importance (Burnham & Anderson 2000). There are other numerous functions for visualizing confounded variables, plotting phylogenetic trees, as well as reporting and exporting modeling results. Lastly, as challenges to ecology are inherently multifarious, and therefore often multi-dataset, this package features several functions to support the identification, interpolation, merging, and updating of missing data and outdated nomenclature.

This package implements the method to analyse weighted mobility networks or distribution networks as outlined in: Block, P., Stadtfeld, C., & Robins, G. (2022) <doi:10.1016/j.socnet.2021.08.003>. The purpose of the model is to analyse the structure of mobility, incorporating exogenous predictors pertaining to individuals and locations known from classical mobility analyses, as well as modelling emergent mobility patterns akin to structural patterns known from the statistical analysis of social networks.

An S4 implementation of the unbiased extension of the model- assisted synthetic-regression estimator proposed by Mandallaz (2013) <DOI:10.1139/cjfr-2012-0381>, Mandallaz et al. (2013) <DOI:10.1139/cjfr-2013-0181> and Mandallaz (2014) <DOI:10.1139/cjfr-2013-0449>. It yields smaller variances than the standard bias correction, the generalised regression estimator.

Machine learning method specifically designed for pre-miRNA prediction. It takes advantage of unlabeled sequences to improve the prediction rates even when there are just a few positive examples, when the negative examples are unreliable or are not good representatives of its class. Furthermore, the method can automatically search for negative examples if the user is unable to provide them. MiRNAss can find a good boundary to divide the pre-miRNAs from other groups of sequences; it automatically optimizes the threshold that defines the classes boundaries, and thus, it is robust to high class imbalance. Each step of the method is scalable and can handle large volumes of data.

This package provides a model designed to be a reliable testbed where various gene drive interventions for mosquito-borne diseases control. It is being developed to accommodate the use of various mosquito-specific gene drive systems within a population dynamics framework that allows migration of individuals between patches in landscape. Previous work developing the population dynamics can be found in Deredec et al. (2001) <doi:10.1073/pnas.1110717108> and Hancock & Godfray (2007) <doi:10.1186/1475-2875-6-98>, and extensions to accommodate CRISPR homing dynamics in Marshall et al. (2017) <doi:10.1038/s41598-017-02744-7>.

Multivariate Adaptive Regression Spline (MARS) based Support Vector Regression (SVR) hybrid model is combined Machine learning hybrid approach which selects important variables using MARS and then fits SVR on the extracted important variables.

This package implements finite mixtures of matrix-variate contaminated normal distributions via expectation conditional-maximization algorithm for model-based clustering, as described in Tomarchio et al.(2020) <arXiv:2005.03861>. One key advantage of this model is the ability to automatically detect potential outlying matrices by computing their a posteriori probability of being typical or atypical points. Finite mixtures of matrix-variate t and matrix-variate normal distributions are also implemented by using expectation-maximization algorithms.

Implementation of imputation techniques based on locally stationary wavelet time series forecasting methods from Wilson, R. E. et al. (2021) <doi:10.1007/s11222-021-09998-2>.

Create tile grid maps, which are like choropleth maps except each region is represented with equal visual space.

Correlation coefficients for multivariate data, namely the squared correlation coefficient and the RV coefficient (multivariate generalization of the squared Pearson correlation coefficient). References include Mardia K.V., Kent J.T. and Bibby J.M. (1979). "Multivariate Analysis". ISBN: 978-0124712522. London: Academic Press.

This package provides a supervised learning algorithm inputs a train set, and outputs a prediction function, which can be used on a test set. If each data point belongs to a subset (such as geographic region, year, etc), then how do we know if subsets are similar enough so that we can get accurate predictions on one subset, after training on Other subsets? And how do we know if training on All subsets would improve prediction accuracy, relative to training on the Same subset? SOAK, Same/Other/All K-fold cross-validation, <doi:10.48550/arXiv.2410.08643> can be used to answer these questions, by fixing a test subset, training models on Same/Other/All subsets, and then comparing test error rates (Same versus Other and Same versus All). Also provides code for estimating how many train samples are required to get accurate predictions on a test set.

This package provides tools for animal movement modelling using hidden Markov models. These include processing of tracking data, fitting hidden Markov models to movement data, visualization of data and fitted model, decoding of the state process, etc. <doi:10.1111/2041-210X.12578>.

This package contains functions for data analysis of Repeated measurement using GEE. Data may contain missing value in response and covariates. For parameter estimation through Fisher Scoring algorithm, Mean Score and Inverse Probability Weighted method combining with Multiple Imputation are used when there is missing value in covariates/response. Reference for mean score method, inverse probability weighted method is Wang et al(2007)<doi:10.1093/biostatistics/kxl024>.

Based on the input data an n-dimensional cube with sub cells of user specified side length is created. The number of sample points which fall in each sub cube is counted, and with the cell volume and overall sample size an empirical probability can be computed. A number of cubes of higher resolution can be superimposed. The basic method stems from J.L. Bentley in "Multidimensional Divide and Conquer". J. L. Bentley (1980) <doi:10.1145/358841.358850>. Furthermore a simple kernel density estimation method is made available, as well as an expansion of Bentleys method, which offers a kernel approach for the grid method.

Fits a Bayesian Regression Model for multivariate count data. This model assumes that the data is distributed according to the Conway-Maxwell-Poisson distribution, and for each response variable it is associate different covariates. This model allows to account for correlations between the counts by using latent effects based on the Chib and Winkelmann (2001) <http://www.jstor.org/stable/1392277> proposal.