This package provides functions to perform all steps of genome-wide association meta-analysis for studying Genotype x Environment interactions, from collecting the data to the manhattan plot. The procedure accounts for the potential correlation between studies. In addition to the Fixed and Random models, one can investigate the relationship between QTL effects and some qualitative or quantitative covariate via the test of contrast and the meta-regression, respectively. The methodology is available from: (De Walsche, A., et al. (2025) \doi10.1371/journal.pgen.1011553).

An efficient implementation of the MCPMod (Multiple Comparisons and Modeling) method to support a simulation-based design and analysis of dose-finding trials with normally distributed, binary and count endpoints (Bretz et al. (2005) <doi:10.1111/j.1541-0420.2005.00344.x>).

This package implements the methods described in Bond S, Farewell V, 2006, Exact Likelihood Estimation for a Negative Binomial Regression Model with Missing Outcomes, Biometrics.

Network meta-analysis and network meta-regression models for aggregate data, individual patient data, and mixtures of both individual and aggregate data using multilevel network meta-regression as described by Phillippo et al. (2020) <doi:10.1111/rssa.12579>. Models are estimated in a Bayesian framework using Stan'.

Unsupervised learning has been widely used in many real-world applications. One of the simplest and most important unsupervised learning models is the Gaussian mixture model (GMM). In this work, we study the multi-task learning problem on GMMs, which aims to leverage potentially similar GMM parameter structures among tasks to obtain improved learning performance compared to single-task learning. We propose a multi-task GMM learning procedure based on the Expectation-Maximization (EM) algorithm that not only can effectively utilize unknown similarity between related tasks but is also robust against a fraction of outlier tasks from arbitrary sources. The proposed procedure is shown to achieve minimax optimal rate of convergence for both parameter estimation error and the excess mis-clustering error, in a wide range of regimes. Moreover, we generalize our approach to tackle the problem of transfer learning for GMMs, where similar theoretical results are derived. Finally, we demonstrate the effectiveness of our methods through simulations and a real data analysis. To the best of our knowledge, this is the first work studying multi-task and transfer learning on GMMs with theoretical guarantees. This package implements the algorithms proposed in Tian, Y., Weng, H., & Feng, Y. (2022) <arXiv:2209.15224>.

Estimates risk as a function of a marker by integrating over other covariates in a conditional risk model.

This package provides a minimalistic but flexible framework that facilitates the creation, management and training of multiple caret models. A model grid consists of two components, (1) a set of settings that is shared by all models by default, and (2) specifications that apply only to the individual models. When the model grid is trained, model and training specifications are first consolidated from the shared and the model specific settings into complete caret model configurations. These models are then trained with the train() function from the caret package.

Parameter estimation and classification for Gaussian Mixture Models (GMMs) in the presence of missing data. This package complements existing implementations by allowing for both missing elements in the input vectors and full (as opposed to strictly diagonal) covariance matrices. Estimation is performed using an expectation conditional maximization algorithm that accounts for missingness of both the cluster assignments and the vector components. The output includes the marginal cluster membership probabilities; the mean and covariance of each cluster; the posterior probabilities of cluster membership; and a completed version of the input data, with missing values imputed to their posterior expectations. For additional details, please see McCaw ZR, Julienne H, Aschard H. "Fitting Gaussian mixture models on incomplete data." <doi:10.1186/s12859-022-04740-9>.

Modern model-based geostatistics for point-referenced data. This package provides a simple interface to run spatial machine learning models and geostatistical models that estimate a continuous (raster) surface from point-referenced outcomes and, optionally, a set of raster covariates. The package also includes functions to summarize raster outcomes by (polygon) region while preserving uncertainty.

Computes the prime implicants or a minimal disjunctive normal form for a logic expression presented by a truth table or a logic tree. Has been particularly developed for logic expressions resulting from a logic regression analysis, i.e. logic expressions typically consisting of up to 16 literals, where the prime implicants are typically composed of a maximum of 4 or 5 literals.

This package provides estimation methods for markets in equilibrium and disequilibrium. Supports the estimation of an equilibrium and four disequilibrium models with both correlated and independent shocks. Also provides post-estimation analysis tools, such as aggregation, marginal effect, and shortage calculations. See Karapanagiotis (2024) <doi:10.18637/jss.v108.i02> for an overview of the functionality and examples. The estimation methods are based on full information maximum likelihood techniques given in Maddala and Nelson (1974) <doi:10.2307/1914215>. They are implemented using the analytic derivative expressions calculated in Karapanagiotis (2020) <doi:10.2139/ssrn.3525622>. Standard errors can be estimated by adjusting for heteroscedasticity or clustering. The equilibrium estimation constitutes a case of a system of linear, simultaneous equations. Instead, the disequilibrium models replace the market-clearing condition with a non-linear, short-side rule and allow for different specifications of price dynamics.

Various functions for random number generation, density estimation, classification, curve fitting, and spatial data analysis.

This package performs causal mediation analysis for count and zero-inflated count data without or with a post-treatment confounder; calculates power to detect prespecified causal mediation effects, direct effects, and total effects; performs sensitivity analysis when there is a treatment- induced mediator-outcome confounder as described by Cheng, J., Cheng, N.F., Guo, Z., Gregorich, S., Ismail, A.I., Gansky, S.A. (2018) <doi:10.1177/0962280216686131>. Implements Instrumental Variable (IV) method to estimate the controlled (natural) direct and mediation effects, and compute the bootstrap Confidence Intervals as described by Guo, Z., Small, D.S., Gansky, S.A., Cheng, J. (2018) <doi:10.1111/rssc.12233>. This software was made possible by Grant R03DE028410 from the National Institute of Dental and Craniofacial Research, a component of the National Institutes of Health.

Dealing with neutrosophic data in single valued form using score, accuracy and certainty functions to calculate ranks of Single Valued Neutrosophic Set (SVNS), also to calculate the Mann-Whitney test, and making a post-hoc test after rejecting the null hypothesis using the Neutrosophic Statistics Kruskal-Wallis test. For more information see Miari, Mahmoud; Anan, Mohamad Taher; Zeina, Mohamed Bisher(2022) <https://digitalrepository.unm.edu/nss_journal/vol51/iss1/60/>.

Implementations of various robust and flexible model-based clustering methods for data sets with missing values at random. Two main models are: Multivariate Contaminated Normal Mixture (MCNM, Tong and Tortora, 2022, <doi:10.1007/s11634-021-00476-1>) and Multivariate Generalized Hyperbolic Mixture (MGHM, Wei et al., 2019, <doi:10.1016/j.csda.2018.08.016>). Mixtures via some special or limiting cases of the multivariate generalized hyperbolic distribution are also included: Normal-Inverse Gaussian, Symmetric Normal-Inverse Gaussian, Skew-Cauchy, Cauchy, Skew-t, Student's t, Normal, Symmetric Generalized Hyperbolic, Hyperbolic Univariate Marginals, Hyperbolic, and Symmetric Hyperbolic. Funding: This work was partially supported by the National Science foundation NSF Grant NO. 2209974.

This package provides functions for fitting various models to capture-recapture data including mixed-effects Cormack-Jolly-Seber(CJS) and multistate models and the multi-variate state model structure for survival estimation and POPAN structured Jolly-Seber models for abundance estimation. There are also Hidden Markov model (HMM) implementations of CJS and multistate models with and without state uncertainty and a simulation capability for HMM models.

Matching with string distance has never been easier! messy.cats contains various functions that employ string distance tools in order to make data management easier for users working with categorical data. Categorical data, especially user inputted categorical data that often tends to be plagued by typos, can be difficult to work with. messy.cats aims to provide functions that make cleaning categorical data simple and easy.

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 simulation modeling framework which significantly extends capabilities from the MGDrivE simulation package via a new mathematical and computational framework based on stochastic Petri nets. For more information about MGDrivE', see our publication: Sánchez et al. (2019) <doi:10.1111/2041-210X.13318> Some of the notable capabilities of MGDrivE2 include: incorporation of human populations, epidemiological dynamics, time-varying parameters, and a continuous-time simulation framework with various sampling algorithms for both deterministic and stochastic interpretations. MGDrivE2 relies on the genetic inheritance structures provided in package MGDrivE', so we suggest installing that package initially.

Fit finite mixture distribution models to grouped data and conditional data by maximum likelihood using a combination of a Newton-type algorithm and the EM algorithm.

Extract textual data from different media channels through its source based on users choice of keywords. These data can be used to perform text analysis to identify patterns in respective media reporting. The media channels used in this package are print media. The data (or news) used are publicly available to consumers.

Fit flexible (excess) hazard regression models with the possibility of including non-proportional effects of covariables and of adding a random effect at the cluster level (corresponding to a shared frailty). A detailed description of the package functionalities is provided in Charvat and Belot (2021) <doi: 10.18637/jss.v098.i14>.

An implementation of the mixed neighbourhood selection (MNS) algorithm. The MNS algorithm can be used to estimate multiple related precision matrices. In particular, the motivation behind this work was driven by the need to understand functional connectivity networks across multiple subjects. This package also contains an implementation of a novel algorithm through which to simulate multiple related precision matrices which exhibit properties frequently reported in neuroimaging analysis.

Simplifies Monte Carlo simulation studies by automatically setting up loops to run over parameter grids and parallelising the Monte Carlo repetitions. It also generates LaTeX tables.