Implementation of parametric and semiparametric mixture cure models based on existing R packages. See details of the models in Peng and Yu (2020) <ISBN: 9780367145576>.

The IRLS (Iteratively Reweighted Least Squares) and GMM (Generalized Method of Moments) methods are applied to estimate mixed correlation coefficient matrix (Pearson, Polyseries, Polychoric), which can be estimated in pairs or simultaneously. For more information see Peng Zhang and Ben Liu (2024) <doi:10.1080/10618600.2023.2257251>; Ben Liu and Peng Zhang (2024) <doi:10.48550/arXiv.2404.06781>.

This package provides a causal mediation approach under the counterfactual framework to test the significance of total, direct and indirect effects. In this approach, a group of methylated sites from a predefined region are utilized as the mediator, and the functional transformation is used to reduce the possible high dimension in the region-based methylated sites and account for their location information.

Read a table of fixed width formatted data of different types into a data.frame for each type.

An approach to identify microbiome biomarker for time to event data by discovering microbiome for predicting survival and classifying subjects into risk groups. Classifiers are constructed as a linear combination of important microbiome and treatment effects if necessary. Several methods were implemented to estimate the microbiome risk score such as the LASSO method by Robert Tibshirani (1998) <doi:10.1002/(SICI)1097-0258(19970228)16:4%3C385::AID-SIM380%3E3.0.CO;2-3>, Elastic net approach by Hui Zou and Trevor Hastie (2005) <doi:10.1111/j.1467-9868.2005.00503.x>, supervised principle component analysis of Wold Svante et al. (1987) <doi:10.1016/0169-7439(87)80084-9>, and supervised partial least squares analysis by Inge S. Helland <https://www.jstor.org/stable/4616159>. Sensitivity analysis on the quantile used for the classification can also be accessed to check the deviation of the classification group based on the quantile specified. Large scale cross validation can be performed in order to investigate the mostly selected microbiome and for internal validation. During the evaluation process, validation is accessed using the hazard ratios (HR) distribution of the test set and inference is mainly based on resampling and permutations technique.

Publicly available data from Medicare frequently requires extensive initial effort to extract desired variables and merge them; this package formalizes the techniques I've found work best. More information on the Medicare program, as well as guidance for the publicly available data this package targets, can be found on CMS's website covering publicly available data. See <https://www.cms.gov/Research-Statistics-Data-and-Systems/Research-Statistics-Data-and-Systems.html>.

Fits multivariate Ornstein-Uhlenbeck types of models to continues trait data from species related by a common evolutionary history. See K. Bartoszek, J, Pienaar, P. Mostad, S. Andersson, T. F. Hansen (2012) <doi:10.1016/j.jtbi.2012.08.005> and K. Bartoszek, and J. Tredgett Clarke, J. Fuentes-Gonzalez, V. Mitov, J. Pienaar, M. Piwczynski, R. Puchalka, K. Spalik, K. L. Voje (2024) <doi:10.1111/2041-210X.14376>. The suggested PCMBaseCpp package (which significantly speeds up the likelihood calculations) can be obtained from <https://github.com/venelin/PCMBaseCpp/>.

Implementing a multiple imputation algorithm for multivariate data with missing and censored values under a coarsening at random assumption (Heitjan and Rubin, 1991<doi:10.1214/aos/1176348396>). The multiple imputation algorithm is based on the data augmentation algorithm proposed by Tanner and Wong (1987)<doi:10.1080/01621459.1987.10478458>. The Gibbs sampling algorithm is adopted to to update the model parameters and draw imputations of the coarse data.

Three algorithms for estimating a Markov random field structure.Two of them are an exact version and a simulated annealing version of a penalized maximum conditional likelihood method similar to the Bayesian Information Criterion. These algorithm are described in Frondana (2016) <doi:10.11606/T.45.2018.tde-02022018-151123>.The third one is a greedy algorithm, described in Bresler (2015) <doi:10.1145/2746539.2746631).

Multivariate ARIMA and ARIMA-X estimation using Spliid's algorithm (marima()) and simulation (marima.sim()).

Implement meta-analyses for simultaneously estimating individual means with shrinkage, isotonic regression and pretests. Include our original implementation of the isotonic regression via the pool-adjacent-violators algorithm (PAVA) algorithm. For the pretest estimator, the confidence interval for individual means are provided. Methodologies were published in Taketomi et al. (2021) <doi:10.3390/axioms10040267>, Taketomi et al. (2022) <doi:10.3390/a15010026>, Taketomi et al. (2023-) (under review).

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.

Estimation of models with dependent variable left-censored at zero. Null values may be caused by a selection process Cragg (1971) <doi:10.2307/1909582>, insufficient resources Tobin (1958) <doi:10.2307/1907382>, or infrequency of purchase Deaton and Irish (1984) <doi:10.1016/0047-2727(84)90067-7>.

Extends the mlr3 package with a connector to the package batchtools'. This allows to run large-scale benchmark experiments on scheduled high-performance computing clusters.

Identifying comorbidities, frailty, and multimorbidity in claims and administrative data is often a duplicative process. The functions contained in this package are meant to first prepare the data to a format acceptable by all other packages, then provide a uniform and simple approach to generate comorbidity and multimorbidity metrics based on these claims data. The package is ever evolving to include new metrics, and is always looking for new measures to include. The citations used in this package include the following publications: Anne Elixhauser, Claudia Steiner, D. Robert Harris, Rosanna M. Coffey (1998) <doi:10.1097/00005650-199801000-00004>, Brian J Moore, Susan White, Raynard Washington, et al. (2017) <doi:10.1097/MLR.0000000000000735>, Mary E. Charlson, Peter Pompei, Kathy L. Ales, C. Ronald MacKenzie (1987) <doi:10.1016/0021-9681(87)90171-8>, Richard A. Deyo, Daniel C. Cherkin, Marcia A. Ciol (1992) <doi:10.1016/0895-4356(92)90133-8>, Hude Quan, Vijaya Sundararajan, Patricia Halfon, et al. (2005) <doi:10.1097/01.mlr.0000182534.19832.83>, Dae Hyun Kim, Sebastian Schneeweiss, Robert J Glynn, et al. (2018) <doi:10.1093/gerona/glx229>, Melissa Y Wei, David Ratz, Kenneth J Mukamal (2020) <doi:10.1111/jgs.16310>, Kathryn Nicholson, Amanda L. Terry, Martin Fortin, et al. (2015) <doi:10.15256/joc.2015.5.61>, Martin Fortin, José Almirall, and Kathryn Nicholson (2017)<doi:10.15256/joc.2017.7.122>.

Clustering in metagenomics is the process of grouping of microbial contigs in species specific bins. This package contains functions that extract genomic features from metagenome data, find the number of clusters for that given data and find the best clustering algorithm for binning.

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.

Offers a convenient pipeline to test and compare various missing data imputation algorithms on simulated and real data. These include simpler methods, such as mean and median imputation and random replacement, but also include more sophisticated algorithms already implemented in popular R packages, such as mi', described by Su et al. (2011) <doi:10.18637/jss.v045.i02>; mice', described by van Buuren and Groothuis-Oudshoorn (2011) <doi:10.18637/jss.v045.i03>; missForest', described by Stekhoven and Buhlmann (2012) <doi:10.1093/bioinformatics/btr597>; missMDA', described by Josse and Husson (2016) <doi:10.18637/jss.v070.i01>; and pcaMethods', described by Stacklies et al. (2007) <doi:10.1093/bioinformatics/btm069>. The central assumption behind missCompare is that structurally different datasets (e.g. larger datasets with a large number of correlated variables vs. smaller datasets with non correlated variables) will benefit differently from different missing data imputation algorithms. missCompare takes measurements of your dataset and sets up a sandbox to try a curated list of standard and sophisticated missing data imputation algorithms and compares them assuming custom missingness patterns. missCompare will also impute your real-life dataset for you after the selection of the best performing algorithm in the simulations. The package also provides various post-imputation diagnostics and visualizations to help you assess imputation performance.

Automatically estimate 11 effect size measures from a well-formatted dataset. Various other functions can help, for example, removing dependency between several effect sizes, or identifying differences between two datasets. This package is mainly designed to assist in conducting a systematic review with a meta-analysis but can be useful to any researcher interested in estimating an effect size.

Parametric modeling of M-quantile regression coefficient functions.

This package provides a framework for multiple hypothesis testing based on distribution of p values. It is well known that the p values come from different distribution for null and alternatives, in this package we provide functions to detect that change. We provide a method for using the change in distribution of p values as a way to detect the true signals in the data.

Pipeline for Genome-Wide Association Study using Multi-Locus Mixed Model from Segura V, Vilhjálmsson BJ et al. (2012) <doi:10.1038/ng.2314>. The pipeline include detection of associated SNPs with MLMM, model selection by lowest eBIC and p-value threshold, estimation of the effects of the SNPs in the selected model and graphical functions.

This package provides a PC Algorithm with the Principle of Mendelian Randomization. This package implements the MRPC (PC with the principle of Mendelian randomization) algorithm to infer causal graphs. It also contains functions to simulate data under a certain topology, to visualize a graph in different ways, and to compare graphs and quantify the differences. See Badsha and Fu (2019) <doi:10.3389/fgene.2019.00460>, Badsha, Martin and Fu (2021) <doi:10.3389/fgene.2021.651812>, Kvamme and Badsha, et al. (2025) <doi:10.1093/genetics/iyaf064>.

Data and code for the paper by Ehm, Gneiting, Jordan and Krueger ('Of Quantiles and Expectiles: Consistent Scoring Functions, Choquet Representations, and Forecast Rankings', JRSS-B, 2016 <DOI:10.1111/rssb.12154>).