Multivariate Information-based Inductive Causation, better known by its acronym MIIC, is a causal discovery method, based on information theory principles, which learns a large class of causal or non-causal graphical models from purely observational data, while including the effects of unobserved latent variables. Starting from a complete graph, the method iteratively removes dispensable edges, by uncovering significant information contributions from indirect paths, and assesses edge-specific confidences from randomization of available data. The remaining edges are then oriented based on the signature of causality in observational data. The recent more interpretable MIIC extension (iMIIC) further distinguishes genuine causes from putative and latent causal effects, while scaling to very large datasets (hundreds of thousands of samples). Since the version 2.0, MIIC also includes a temporal mode (tMIIC) to learn temporal causal graphs from stationary time series data. MIIC has been applied to a wide range of biological and biomedical data, such as single cell gene expression data, genomic alterations in tumors, live-cell time-lapse imaging data (CausalXtract), as well as medical records of patients. MIIC brings unique insights based on causal interpretation and could be used in a broad range of other data science domains (technology, climatology, economy, ...). For more information, you can refer to: Simon et al., eLife 2024, <doi:10.1101/2024.02.06.579177>, Ribeiro-Dantas et al., iScience 2024, <doi:10.1016/j.isci.2024.109736>, Cabeli et al., NeurIPS 2021, <https://why21.causalai.net/papers/WHY21_24.pdf>, Cabeli et al., Comput. Biol. 2020, <doi:10.1371/journal.pcbi.1007866>, Li et al., NeurIPS 2019, <https://papers.nips.cc/paper/9573-constraint-based-causal-structure-learning-with-consistent-separating-sets>, Verny et al., PLoS Comput. Biol. 2017, <doi:10.1371/journal.pcbi.1005662>, Affeldt et al., UAI 2015, <https://auai.org/uai2015/proceedings/papers/293.pdf>. Changes from the previous 1.5.3 release on CRAN are available at <https://github.com/miicTeam/miic_R_package/blob/master/NEWS.md>.

Data and examples from a multilevel modelling software review as well as other well-known data sets from the multilevel modelling literature.

This package provides a framework for analyzing broth microdilution assays in various 96-well plate designs, visualizing results and providing descriptive and (simple) inferential statistics (i.e. summary statistics and sign test). The functions are designed to add metadata to 8 x 12 tables of absorption values, creating a tidy data frame. Users can choose between clean-up procedures via function parameters (which covers most cases) or user prompts (in cases with complex experimental designs). Users can also choose between two validation methods, i.e. exclusion of absorbance values above a certain threshold or manual exclusion of samples. A function for visual inspection of samples with their absorption values over time for certain group combinations helps with the decision. In addition, the package includes functions to subtract the background absorption (usually at time T0) and to calculate the growth performance compared to a baseline. Samples can be visually inspected with their absorption values displayed across time points for specific group combinations. Core functions of this package (i.e. background subtraction, sample validation and statistics) were inspired by the manual calculations that were applied in Tewes and Muller (2020) <doi:10.1038/s41598-020-67600-7>.

Penalized regression methods, such as lasso and elastic net, are used in many biomedical applications when simultaneous regression coefficient estimation and variable selection is desired. However, missing data complicates the implementation of these methods, particularly when missingness is handled using multiple imputation. Applying a variable selection algorithm on each imputed dataset will likely lead to different sets of selected predictors, making it difficult to ascertain a final active set without resorting to ad hoc combination rules. miselect presents Stacked Adaptive Elastic Net (saenet) and Grouped Adaptive LASSO (galasso) for continuous and binary outcomes, developed by Du et al (2022) <doi:10.1080/10618600.2022.2035739>. They, by construction, force selection of the same variables across multiply imputed data. miselect also provides cross validated variants of these methods.

Perform a mail merge (mass email) using the message defined in markdown, the recipients in a csv file, and gmail as the mailing engine. With this package you can parse markdown documents as the body of email, and the yaml header to specify the subject line of the email. Any braces in the email will be encoded with glue::glue()'. You can preview the email in the RStudio viewer pane, and send (draft) email using gmailr'.

This package provides a tool to simulate salmon metapopulations and apply financial portfolio optimization concepts. The package accompanies the paper Anderson et al. (2015) <doi:10.1101/2022.03.24.485545>.

Useful functions to analyze proteomic workflows including number of identifications, data completeness, missed cleavages, quantitative and retention time precision etc. Various software outputs are supported such as ProteomeDiscoverer', Spectronaut', DIA-NN and MaxQuant'.

This package performs treatment allocation in two-arm clinical trials by the maximal procedure described by Berger et al. (2003) <doi:10.1002/sim.1538>. To that end, the algorithm provided by Salama et al. (2008) <doi:10.1002/sim.3014> is implemented.

Allows the user to estimate transition probabilities for migratory animals between any two phases of the annual cycle, using a variety of different data types. Also quantifies the strength of migratory connectivity (MC), a standardized metric to quantify the extent to which populations co-occur between two phases of the annual cycle. Includes functions to estimate MC and the more traditional metric of migratory connectivity strength (Mantel correlation) incorporating uncertainty from multiple sources of sampling error. For cross-species comparisons, methods are provided to estimate differences in migratory connectivity strength, incorporating uncertainty. See Cohen et al. (2018) <doi:10.1111/2041-210X.12916>, Cohen et al. (2019) <doi:10.1111/ecog.03974>, Roberts et al. (2023) <doi:10.1002/eap.2788>, and Hostetler et al. (2025) <doi:10.1111/2041-210X.14467> for details on some of these methods.

Straightforward and detailed evaluation of machine learning models. MLeval can produce receiver operating characteristic (ROC) curves, precision-recall (PR) curves, calibration curves, and PR gain curves. MLeval accepts a data frame of class probabilities and ground truth labels, or, it can automatically interpret the Caret train function results from repeated cross validation, then select the best model and analyse the results. MLeval produces a range of evaluation metrics with confidence intervals.

This package provides a tidy workflow for landscape-scale analysis. multilandr offers tools to generate landscapes at multiple spatial scales and compute landscape metrics, primarily using the landscapemetrics package. It also features utility functions for plotting and analyzing multi-scale landscapes, exploring correlations between metrics, filtering landscapes based on specific conditions, generating landscape gradients for a given metric, and preparing datasets for further statistical analysis. Documentation about multilandr is provided in an introductory vignette included in this package and in the paper by Huais (2024) <doi:10.1007/s10980-024-01930-z>; see citation("multilandr") for details.

This package performs maximum a posteriori Bayesian estimation of individual pharmacokinetic parameters from a model defined in mrgsolve', typically for model-based therapeutic drug monitoring. Internally computes an objective function value from model and data, performs optimization and returns predictions in a convenient format. The performance of the package was described by Le Louedec et al (2021) <doi:10.1002/psp4.12689>.

Make all elements of a character vector unique. Differs from make.unique by starting at 1 and allowing users to customise suffix format.

This package provides a tidyverse'-friendly client for the National Statistics Office of Mongolia PXWeb API <https://data.1212.mn/> with helpers to discover tables, variables, and fetch statistical data. Also includes utilities to retrieve Mongolia administrative boundaries (ADM0-ADM2) as sf objects from open sources for mapping and spatial analysis.

Generates multivariate imputations using sequential regression with L2 penalty. For more details see Zahid and Heumann (2018) <doi:10.1177/0962280218755574>.

An implementation of multiple maps t-distributed stochastic neighbor embedding (t-SNE). Multiple maps t-SNE is a method for projecting high-dimensional data into several low-dimensional maps such that non-metric space properties are better preserved than they would be by a single map. Multiple maps t-SNE with only one map is equivalent to standard t-SNE. When projecting onto more than one map, multiple maps t-SNE estimates a set of latent weights that allow each point to contribute to one or more maps depending on similarity relationships in the original data. This implementation is a port of the original Matlab library by Laurens van der Maaten. See Van der Maaten and Hinton (2012) <doi:10.1007/s10994-011-5273-4>. This material is based upon work supported by the United States Air Force and Defense Advanced Research Project Agency (DARPA) under Contract No. FA8750-17-C-0020. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the United States Air Force and Defense Advanced Research Projects Agency. Distribution Statement A: Approved for Public Release; Distribution Unlimited.

The detection of worrying approximate collinearity in a multiple linear regression model is a problem addressed in all existing statistical packages. However, we have detected deficits regarding to the incorrect treatment of qualitative independent variables and the role of the intercept of the model. The objective of this package is to correct these deficits. In this package will be available detection and treatment techniques traditionally used as the recently developed.

Performing multiple-class cluster correspondence analysis(MCCCA). The main functions are create.MCCCAdata() to create a list to be applied to MCCCA, MCCCA() to apply MCCCA, and plot.mccca() for visualizing MCCCA result. Methods used in the package refers to Mariko Takagishi and Michel van de Velden (2022)<doi:10.1080/10618600.2022.2035737>.

In many agricultural, engineering, industrial, post-harvest and processing experiments, the number of factor level changes and hence the total number of changes is of serious concern as such experiments may consists of hard-to-change factors where it is physically very difficult to change levels of some factors or sometime such experiments may require normalization time to obtain adequate operating condition. For this reason, run orders that offer the minimum number of factor level changes and at the same time minimize the possible influence of systematic trend effects on the experimentation have been sought. Factorial designs with minimum changes in factors level may be preferred for such situations as these minimally changed run orders will minimize the cost of the experiments. For method details see, Bhowmik, A.,Varghese, E., Jaggi, S. and Varghese, C. (2017)<doi:10.1080/03610926.2016.1152490>.This package used to construct all possible minimally changed factorial run orders for different experimental set ups along with different statistical criteria to measure the performance of these designs. It consist of the function minFactDesign().

Mahalanobis-Taguchi (MT) system is a collection of multivariate analysis methods developed for the field of quality engineering. MT system consists of two families depending on their purpose. One is a family of Mahalanobis-Taguchi (MT) methods (in the broad sense) for diagnosis (see Woodall, W. H., Koudelik, R., Tsui, K. L., Kim, S. B., Stoumbos, Z. G., and Carvounis, C. P. (2003) <doi:10.1198/004017002188618626>) and the other is a family of Taguchi (T) methods for forecasting (see Kawada, H., and Nagata, Y. (2015) <doi:10.17929/tqs.1.12>). The MT package contains three basic methods for the family of MT methods and one basic method for the family of T methods. The MT method (in the narrow sense), the Mahalanobis-Taguchi Adjoint (MTA) methods, and the Recognition-Taguchi (RT) method are for the MT method and the two-sided Taguchi (T1) method is for the family of T methods. In addition, the Ta and Tb methods, which are the improved versions of the T1 method, are included.

This package provides the users with the ability to quickly create linked micromap plots for a collection of geographic areas. Linked micromap plots are visualizations of geo-referenced data that link statistical graphics to an organized series of small maps or graphic images. The Help description contains examples of how to use the micromapST function. Contained in this package are border group datasets to support creating linked micromap plots for the 50 U.S. states and District of Columbia (51 areas), the U. S. 20 Seer Registries, the 105 counties in the state of Kansas, the 62 counties of New York, the 24 counties of Maryland, the 29 counties of Utah, the 32 administrative areas in China, the 218 administrative areas in the UK and Ireland (for testing only), the 25 districts in the city of Seoul South Korea, and the 52 counties on the Africa continent. A border group dataset contains the boundaries related to the data level areas, a second layer boundaries, a top or third layer boundary, a parameter list of run options, and a cross indexing table between area names, abbreviations, numeric identification and alias matching strings for the specific geographic area. By specifying a border group, the package create linked micromap plots for any geographic region. The user can create and provide their own border group dataset for any area beyond the areas contained within the package with the BuildBorderGroup function. In April of 2022, it was announced that maptools', rgdal', and rgeos R packages would be retired in middle to end of 2023 and removed from the CRAN libraries. The BuildBorderGroup function was dependent on these packages. micromapST functions were not impacted by the retired R packages. Upgrading of BuildBorderGroup function was completed and released with version 3.0.0 on August 10, 2023 using the sf R package. References: Carr and Pickle, Chapman and Hall/CRC, Visualizing Data Patterns with Micromaps, CRC Press, 2010. Pickle, Pearson, and Carr (2015), micromapST: Exploring and Communicating Geospatial Patterns in US State Data., Journal of Statistical Software, 63(3), 1-25., <https://www.jstatsoft.org/v63/i03/>. Copyrighted 2013, 2014, 2015, 2016, 2022, 2023, 2024, and 2025 by Carr, Pearson and Pickle.

Computes the third multivariate cumulant of either the raw, centered or standardized data. Computes the main measures of multivariate skewness, together with their bootstrap distributions. Finally, computes the least skewed linear projections of the data.

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.

Estimation, inference and diagnostics for Univariate Autoregressive Markov Switching Models for Linear and Generalized Models. Distributions for the series include gaussian, Poisson, binomial and gamma cases. The EM algorithm is used for estimation (see Perlin (2012) <doi:10.2139/ssrn.1714016>).