Sixteen tools for bioinformatics processing and analysis of major histocompatibility complex (MHC) data. The functions are tailored for amplicon data sets that have been filtered using the dada2 method (for more information on dada2, visit <https://benjjneb.github.io/dada2/> ), but even other types of data sets can be analyzed. The ReplMatch() function matches replicates in data sets in order to evaluate genotyping success. The GetReplTable() and GetReplStats() functions perform such an evaluation. The CreateFas() function creates a fasta file with all the sequences in the data set. The CreateSamplesFas() function creates individual fasta files for each sample in the data set. The DistCalc() function calculates Grantham, Sandberg, or p-distances from pairwise comparisons of all sequences in a data set, and mean distances of all pairwise comparisons within each sample in a data set. The function additionally outputs five tables with physico-chemical z-descriptor values (based on Sandberg et al. 1998) for each amino acid position in all sequences in the data set. These tables may be useful for further downstream analyses, such as estimation of MHC supertypes. The BootKmeans() function is a wrapper for the kmeans() function of the stats package, which allows for bootstrapping. Bootstrapping k-estimates may be desirable in data sets, where e.g. BIC- vs. k-values do not produce clear inflection points ("elbows"). BootKmeans() performs multiple runs of kmeans() and estimates optimal k-values based on a user-defined threshold of BIC reduction. The method is an automated and bootstrapped version of visually inspecting elbow plots of BIC- vs. k-values. The ClusterMatch() function is a tool for evaluating whether different k-means() clustering models identify similar clusters, and summarize bootstrap model stats as means for different estimated values of k. It is designed to take files produced by the BootKmeans() function as input, but other data can be analyzed if the descriptions of the required data formats are observed carefully. The SynDist() function analyses of synonymous variation among aligned protein-coding DNA sequences, that is, nucleotide substitutions that do not translate to changes in the amino acid sequences due to degeneracy of the genetic code. The SynDist() function calculates synonymous nucleotide changes per base and per codon in pairwise sequence comparisons, as well as mean synonymous variation among all pairwise comparisons of the sequences within each sample in a data set. The PapaDiv() function compares parent pairs in the data set and calculate their joint MHC diversity, taking into account sequence variants that occur in both parents. The HpltFind() function infers putative haplotypes from families in the data set. The GetHpltTable() and GetHpltStats() functions evaluate the accuracy of the haplotype inference. The CreateHpltOccTable() function creates a binary (logical) haplotype-sequence occurrence matrix from the output of HpltFind(), for easy overview of which sequences are present in which haplotypes. The HpltMatch() function compares haplotypes to help identify overlapping and potentially identical types. The NestTablesXL() function translates the output from HpltFind() to an Excel workbook, that provides a convenient overview for evaluation and curating of the inferred putative haplotypes.

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

Multiscale Graph Correlation (MGC) is a framework developed by Vogelstein et al. (2019) <DOI:10.7554/eLife.41690> that extends global correlation procedures to be multiscale; consequently, MGC tests typically require far fewer samples than existing methods for a wide variety of dependence structures and dimensionalities, while maintaining computational efficiency. Moreover, MGC provides a simple and elegant multiscale characterization of the potentially complex latent geometry underlying the relationship.

This package performs monotonic binning of numeric risk factor in credit rating models (PD, LGD, EAD) development. All functions handle both binary and continuous target variable. Functions that use isotonic regression in the first stage of binning process have an additional feature for correction of minimum percentage of observations and minimum target rate per bin. Additionally, monotonic trend can be identified based on raw data or, if known in advance, forced by functions argument. Missing values and other possible special values are treated separately from so-called complete cases.

For a given test market find the best control markets using time series matching and analyze the impact of an intervention. The intervention could be a marketing event or some other local business tactic that is being tested. The workflow implemented in the Market Matching package utilizes dynamic time warping (the dtw package) to do the matching and the CausalImpact package to analyze the causal impact. In fact, this package can be considered a "workflow wrapper" for those two packages. In addition, if you don't have a chosen set of test markets to match, the Market Matching package can provide suggested test/control market pairs and pseudo prospective power analysis (measuring causal impact at fake interventions).

Maximum likelihood Gaussian process modeling for univariate and multi-dimensional outputs with diagnostic plots following Santner et al (2003) <doi:10.1007/978-1-4757-3799-8>. Contact the maintainer for a package version that includes sensitivity analysis.

This package provides functions to compute and visualize movement-based kernel density estimates (MKDEs) for animal utilization distributions in 2 or 3 spatial dimensions.

Provide simple functions to (i) compute a class of multi-functionality measures for a single ecosystem for given function weights, (ii) decompose gamma multi-functionality for pairs of ecosystems and K ecosystems (K can be greater than 2) into a within-ecosystem component (alpha multi-functionality) and an among-ecosystem component (beta multi-functionality). In each case, the correlation between functions can be corrected for. Based on biodiversity and ecosystem function data, this software also facilitates graphics for assessing biodiversity-ecosystem functioning relationships across scales.

Mass measurement corrections and uncertainties using calibration data, as recommended by EURAMET's guideline No. 18 (2015) ISBN:978-3-942992-40-4 . The package provides classes, functions, and methods for storing information contained in calibration certificates and converting balance readings to both conventional mass and real mass. For the latter, the Magnitude of the Air Buoyancy Correction factor employs models (such as the CIMP-2007 formula revised by Picard, Davis, Gläser, and Fujii (2008) <doi:10.1088/0026-1394/45/2/004>) to estimate the local air density using measured environmental conditions.

This package provides pipe-style interface for data.table'. Package preserves all data.table features without significant impact on performance. let and take functions are simplified interfaces for most common data manipulation tasks. For example, you can write take(mtcars, mean(mpg), by = am) for aggregation or let(mtcars, hp_wt = hp/wt, hp_wt_mpg = hp_wt/mpg) for modification. Use take_if/let_if for conditional aggregation/modification. Additionally there are some conveniences such as automatic data.frame conversion to data.table'.

If results from a meta-GWAS are used for validation in one of the cohorts that was included in the meta-analysis, this will yield biased (i.e. too optimistic) results. The validation cohort needs to be independent from the meta-Genome-Wide-Association-Study (meta-GWAS) results. MetaSubtract will subtract the results of the respective cohort from the meta-GWAS results analytically without having to redo the meta-GWAS analysis using the leave-one-out methodology. It can handle different meta-analyses methods and takes into account if single or double genomic control correction was applied to the original meta-analysis. It can also handle different meta-analysis methods. It can be used for whole GWAS, but also for a limited set of genetic markers. See for application: Nolte I.M. et al. (2017); <doi: 10.1038/ejhg.2017.50>.

Multiple imputation using XGBoost', subsampling, and predictive mean matching as described in Deng and Lumley (2024) <doi:10.1080/10618600.2023.2252501>. The package supports various types of variables, offers flexible settings, and enables saving an imputation model to impute new data. Data processing and memory usage have been optimised to speed up the imputation process.

Basic functions for microbial sequence data analysis. The idea is to use generic R data structures as much as possible, making R data wrangling possible also for sequence data.

Multivariate distribution derived from a Bernoulli mixed model under a marginal approach, incorporating a non-normal random intercept whose distribution is assumed to follow a generalized log-gamma (GLG) specification under a particular parameter setting. Estimation is performed by maximizing the log-likelihood using numerical optimization techniques (Lizandra C. Fabio, Vanessa Barros, Cristian Lobos, Jalmar M. F. Carrasco, Marginal multivariate approach: A novel strategy for handling correlated binary outcomes, 2025, under submission).

Constructs mixed-level and regular fractional factorial designs using coordinate-exchange optimization and automatic generator search. Design quality is evaluated with J2 and balance (H-hat) criteria, alias structures are computed via correlation-based chaining, and deterministic trend-free run orders can be produced following Coster (1993) <doi:10.1214/aos/1176349410>. Mixed-level design construction follows the NONBPA approach of Pantoja-Pacheco et al. (2021) <doi:10.3390/math9131455>. Regular fraction identification follows Guo, Simpson and Pignatiello (2007) <doi:10.1080/00224065.2007.11917691>. Alias structure computation follows Rios-Lira et al.(2021) <doi:10.3390/math9233053>.

This package implements Main Path Analysis (MPA) as introduced by Hummon and Doreian (1989) <doi:10.1016/0378-8733(89)90017-8>. Given a directed acyclic graph (DAG) representing a citation or precedence network, the package computes traversal weights (SPC, SPLC, SPNP) for each edge and extracts the global, local, and key-route main paths. Also provides tools for DAG validation, node role classification (source/terminal/user), per-component path extraction for disconnected networks, and scale-free network testing. Accepts igraph objects or edge-list data frames as input. Includes readers for Pajek (.net) and Gephi export (.gexf, .graphml) files.

Native R implementation of many-facet ordered-response measurement models with arbitrary facet counts, rating-scale and partial-credit parameterizations, a bounded generalized partial-credit extension, and both marginal and joint maximum likelihood estimation. The package provides a fit / diagnose / report pipeline covering anchoring, linking, bias and differential-functioning screening, and publication-oriented reporting summaries, with reproducibility manifests for replay. See Andrich (1978) <doi:10.1007/BF02293814>, Masters (1982) <doi:10.1007/BF02296272>, and Muraki (1992) <doi:10.1177/014662169201600206> for the underlying ordered-response models.

Frequentist and Bayesian linear regression for large data sets. Useful when the data does not fit into memory (for both frequentist and Bayesian regression), to make running time manageable (mainly for Bayesian regression), and to reduce the total running time because of reduced or less severe memory-spillover into the virtual memory. This is an implementation of Merge & Reduce for linear regression as described in Geppert, L.N., Ickstadt, K., Munteanu, A., & Sohler, C. (2020). Streaming statistical models via Merge & Reduce'. International Journal of Data Science and Analytics, 1-17, <doi:10.1007/s41060-020-00226-0>.

This package provides functions for row-reducing and inverting matrices with entries in many of the finite fields (those with a prime number of elements). With this package, users will be able to find the reduced row echelon form (RREF) of a matrix and calculate the inverse of a (square, invertible) matrix.

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.

An implementation of the additive (Gurevitch et al., 2000 <doi:10.1086/303337>) and multiplicative (Lajeunesse, 2011 <doi:10.1890/11-0423.1>) factorial null models for multiple stressor data (Burgess et al., 2021 <doi:10.1101/2021.07.21.453207>). Effect sizes are able to be calculated for either null model, and subsequently classified into one of four different interaction classifications (e.g., antagonistic or synergistic interactions). Analyses can be conducted on data for single experiments through to large meta-analytical datasets. Minimal input (or statistical knowledge) is required, with any output easily understood. Summary figures are also able to be easily generated.

The Mutual Information Index (M) introduced to social science literature by Theil and Finizza (1971) <doi:10.1080/0022250X.1971.9989795> is a multigroup segregation measure that is highly decomposable and that according to Frankel and Volij (2011) <doi:10.1016/j.jet.2010.10.008> and Mora and Ruiz-Castillo (2011) <doi:10.1111/j.1467-9531.2011.01237.x> satisfies the Strong Unit Decomposability and Strong Group Decomposability properties. This package allows computing and decomposing the total index value into its "between" and "within" terms. These last terms can also be decomposed into their contributions, either by group or unit characteristics. The factors that produce each "within" term can also be displayed at the user's request. The results can be computed considering a variable or sets of variables that define separate clusters.

Function multiroc() can be used for computing and visualizing Receiver Operating Characteristics (ROC) and Area Under the Curve (AUC) for multi-class classification problems. It supports both One-vs-One approach by M.Bishop, C. (2006, ISBN:978-0-387-31073-2) and One-vs-All approach by Murphy P., K. (2012, ISBN:9780262018029).

This package provides tools for training, selecting, and evaluating maximum entropy (and standard logistic regression) distribution models. This package provides tools for user-controlled transformation of explanatory variables, selection of variables by nested model comparison, and flexible model evaluation and projection. It follows principles based on the maximum- likelihood interpretation of maximum entropy modeling, and uses infinitely- weighted logistic regression for model fitting. The package is described in Vollering et al. (2019; <doi:10.1002/ece3.5654>).