This package provides methods for quality control and exploratory analysis of surface water quality data collected in Massachusetts, USA. Functions are developed to facilitate data formatting for the Water Quality Exchange Network <https://www.epa.gov/waterdata/water-quality-data-upload-wqx> and reporting of data quality objectives to state agencies. Quality control methods are from Massachusetts Department of Environmental Protection (2020) <https://www.mass.gov/orgs/massachusetts-department-of-environmental-protection>.

In breeding experiments, mating environmental (ME) designs are very popular as mating designs are directly implemented in the field environment using block or row-column designs. Here, three functions are given related to three new methods which will generate mating diallel cross designs (Hinkelmann and Kempthorne, 1963<doi:10.2307/2333899>) or mating environmental (ME) designs along with design parameters, C matrix, eigenvalues (EVs), degree of fractionations (DF) and canonical efficiency factor (CEF). Another one function is added to check the properties of a given ME diallel cross design.

Basic Setup for Projects in R for Monterey County Office of Education. It contains functions often used in the analysis of education data in the county office including seeing if an item is not in a list, rounding in the manner the general public expects, including logos for districts, switching between district names and their county-district-school codes, accessing the local SQL table and making thematically consistent graphs.

Simultaneous multiple outcomes prediction based on revised stacking algorithms, which enables the integration of information from predictions of individual models. An implementation of methodologies proposed in our paper: Li Xing, Mary L Lesperance, Xuekui Zhang. (2019) Bioinformatics, "Simultaneous prediction of multiple outcomes using revised stacking algorithms" <doi:10.1093/bioinformatics/btz531>.

This package provides functions for measuring population divergence from genotypic data.

Persistent interface to Macaulay2 <https://www.macaulay2.com> and front-end tools facilitating its use in the R ecosystem. For details see Kahle et. al. (2020) <doi:10.18637/jss.v093.i09>.

Package computes popular and widely used multicollinearity diagnostic measures <doi:10.17576/jsm-2019-4809-26> and <doi:10.32614/RJ-2016-062> . Package also indicates which regressors may be the reason of collinearity among regressors.

Statistical methods to match feature vectors between multiple datasets in a one-to-one fashion. Given a fixed number of classes/distributions, for each unit, exactly one vector of each class is observed without label. The goal is to label the feature vectors using each label exactly once so to produce the best match across datasets, e.g. by minimizing the variability within classes. Statistical solutions based on empirical loss functions and probabilistic modeling are provided. The Gurobi software and its R interface package are required for one of the package functions (match.2x()) and can be obtained at <https://www.gurobi.com/> (free academic license). For more details, refer to Degras (2022) <doi:10.1080/10618600.2022.2074429> "Scalable feature matching for large data collections" and Bandelt, Maas, and Spieksma (2004) <doi:10.1057/palgrave.jors.2601723> "Local search heuristics for multi-index assignment problems with decomposable costs".

Wrapper around the Unix join facility which is more efficient than the built-in R routine merge(). The package enables the joining of multiple files on disk at once. The files can be compressed and various filters can be deployed before joining. Compiles only under Unix.

The mycobacrvR package contains utilities to provide detailed information for B cell and T cell epitopes for predicted adhesins from various servers such as ABCpred, Bcepred, Bimas, Propred, NetMHC and IEDB. Please refer the URL below to download data files (data_mycobacrvR.zip) used in functions of this package.

Measures mobility in a population through transition matrices and mobility indices. Relative, mixed, and absolute transition matrices are supported. The Prais-Bibby, Absolute Movement, Origin Specific, and Weighted Group Mobility indices are supported. Example income and grade data are included.

This package provides a complete and dedicated analytical toolbox for quality control and diagnosis based on subject-related measurements of micro-RNA (miRNA) expressions. The package consists of a set of functions that allow to train, optimize and use a Bayesian classifier that relies on multiplets of measured miRNA expressions. The package also implements the quality control tools required to preprocess input datasets. In addition, the package provides a function to carry out a statistical analysis of miRNA expressions, which can give insights to improve the classifier's performance. The method implemented in the package was first introduced in L. Ricci, V. Del Vescovo, C. Cantaloni, M. Grasso, M. Barbareschi and M. A. Denti, "Statistical analysis of a Bayesian classifier based on the expression of miRNAs", BMC Bioinformatics 16:287, 2015 <doi:10.1186/s12859-015-0715-9>. The package is thoroughly described in M. Castelluzzo, A. Perinelli, S. Detassis, M. A. Denti and L. Ricci, "MiRNA-QC-and-Diagnosis: An R package for diagnosis based on MiRNA expression", SoftwareX 12:100569, 2020 <doi:10.1016/j.softx.2020.100569>. Please cite both these works if you use the package for your analysis. DISCLAIMER: The software in this package is for general research purposes only and is thus provided WITHOUT ANY WARRANTY. It is NOT intended to form the basis of clinical decisions. Please refer to the GNU General Public License 3.0 (GPLv3) for further information.

The estimation of the parameters in mixed Poisson models.

Computing metabolite set enrichment analysis (MSEA) (Yamamoto, H. et al. (2014) <doi:10.1186/1471-2105-15-51>), single sample enrichment analysis (SSEA) (Yamamoto, H. (2023) <doi:10.51094/jxiv.262>) and over-representation analysis (ORA) that accounts for undetected metabolites (Yamamoto, H. (2024) <doi:10.51094/jxiv.954>).

Developed a data-driven estimation framework for the multi-threshold accelerate failure time (MTAFT) model. The MTAFT model features different linear forms in different subdomains, and one of the major challenges is determining the number of threshold effects. The package introduces a data-driven approach that utilizes a Schwarz information criterion, which demonstrates consistency under mild conditions. Additionally, a cross-validation (CV) criterion with an order-preserved sample-splitting scheme is proposed to achieve consistent estimation, without the need for additional parameters. The package establishes the asymptotic properties of the parameter estimates and includes an efficient score-type test to examine the existence of threshold effects. The methodologies are supported by numerical experiments and theoretical results, showcasing their reliable performance in finite-sample cases.

Mixed, low-rank, and sparse multivariate regression ('mixedLSR') provides tools for performing mixture regression when the coefficient matrix is low-rank and sparse. mixedLSR allows subgroup identification by alternating optimization with simulated annealing to encourage global optimum convergence. This method is data-adaptive, automatically performing parameter selection to identify low-rank substructures in the coefficient matrix.

This package provides a comprehensive graphical user interface for analysis of Affymetrix, Agilent, Illumina, Nimblegen and other microarray data. It can perform miscellaneous tasks such as gene set enrichment and test analyses, identifying gene symbols and building co-expression network. It can also estimate sample size for atleast two-fold expression change. The current version is its slenderized form for compatable and flexible implementation.

Combination of either p-values or modified effect sizes from different studies to find differentially expressed genes.

It performs the followings Multivariate Process Capability Indices: Shahriari et al. (1995) Multivariate Capability Vector, Taam et al. (1993) Multivariate Capability Index (MCpm), Pan and Lee (2010) proposal (NMCpm) and the followings based on Principal Component Analysis (PCA):Wang and Chen (1998), Xekalaki and Perakis (2002) and Wang (2005). Two datasets are included.

Micro simulation model to reproduce natural history of cervical cancer and cost-effectiveness evaluation of prevention strategies. See Georgalis L, de Sanjose S, Esnaola M, Bosch F X, Diaz M (2016) <doi:10.1097/CEJ.0000000000000202> for more details.

Framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of multidimensionality and discreteness of the latent traits. The fitting algorithms allow for missing responses and for different item parameterizations and are based on the Expectation-Maximization paradigm. Individual covariates affecting the class weights may be included in the new version (since 2.1).

An implementation of a Bayesian sparse group model using spike and slab priors in a regression context. It is designed for regression with a multivariate response variable, but also provides an implementation for univariate response.

This package provides tools for estimating, measuring, and analyzing migration data. Designed to assist researchers and analysts in working effectively with migration data.

Package for combined miRNA- and mRNA-testing.