This package provides functions and classes to store, manipulate and summarise Monte Carlo Markov Chain (MCMC) samples. For more information see Brooks et al. (2011) <isbn:978-1-4200-7941-8>.

Determines single or multiple modes (most frequent values). Checks if missing values make this impossible, and returns NA in this case. Dependency-free source code. See Franzese and Iuliano (2019) <doi:10.1016/B978-0-12-809633-8.20354-3>.

Supports morphological analysis for Japanese by using MeCab <https://taku910.github.io/mecab/>, Sudachi <https://github.com/WorksApplications/Sudachi>, Chamame <https://chamame.ninjal.ac.jp/>, or Ginza <https://github.com/megagonlabs/ginza>. Can input a data.frame and obtain all results of MeCab and the row number of the original data.frame as a text id.

Implementation of a next-generation, multi-stock age-structured fisheries assessment model. multiSA is intended for use in mixed fisheries where stock composition can not be readily identified in fishery data alone, e.g., from catch and age/length composition. Models can be fitted to genetic data, e.g., stock composition of catches and close-kin pairs, with seasonal stock availability and movement.

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>.

Simulation results detailed in Esarey and Menger (2019) <doi:10.1017/psrm.2017.42> demonstrate that cluster adjusted t statistics (CATs) are an effective method for correcting standard errors in scenarios with a small number of clusters. The mmiCATs package offers a suite of tools for working with CATs. The mmiCATs() function initiates a shiny web application, facilitating the analysis of data utilizing CATs, as implemented in the cluster.im.glm() function from the clusterSEs package. Additionally, the pwr_func_lmer() function is designed to simplify the process of conducting simulations to compare mixed effects models with CATs models. For educational purposes, the CloseCATs() function launches a shiny application card game, aimed at enhancing users understanding of the conditions under which CATs should be preferred over random intercept models.

Simulate, manage, visualize, and analyze spatially and temporally explicit datasets of mating potential. Implements methods to calculate synchrony, proximity, and compatibility.Synchrony calculations are based on methods described in Augspurger (1983) <doi:10.2307/2387650>, Kempenaers (1993) <doi:10.2307/3676415>, Ison et al. (2014) <doi:10.3732/ajb.1300065>, and variations on these, as described.

Description: Functions and S4 classes to create, manage and analyse discrete time Markov chains. Includes probabilistic analysis of chain structure (state classification, hitting times, stationary distributions, spectral and mixing diagnostics), statistical inference (maximum likelihood and Bayesian estimation, tests of the Markov property, homogeneity, stationarity and order, simulation), higher-order and multivariate chains, state aggregation, and import/export to JSON, YAML and CSV. Basic support for continuous time Markov chains is also provided; some of these functions depend on the suggested ctmcd package. See Spedicato (2017) <doi:10.32614/RJ-2017-036>.

This package provides access to coded election programmes from the Manifesto Corpus and to the Manifesto Project's Main Dataset and routines to analyse this data. The Manifesto Project <https://manifesto-project.wzb.eu> collects and analyses election programmes across time and space to measure the political preferences of parties. The Manifesto Corpus contains the collected and annotated election programmes in the Corpus format of the package tm to enable easy use of text processing and text mining functionality. Specific functions for scaling of coded political texts are included.

This package provides high-performance tools for macroeconomic trend extraction and filtering, specifically designed to solve the end-point problem in real-time. Implements the MacroBoost Hybrid (MBH) filter using penalized P-splines and gradient boosting. Unlike the standard Hodrick-Prescott filter, MacroFilters utilizes component-wise L2-boosting with robust loss functions (Huber) to handle extreme transient shocks (e.g., COVID-19) without inducing spurious trend shifts. The algorithm includes an automated two-layer diagnostic stage for unit roots and structural breaks, optimized via corrected AICc for computational efficiency. Methodology detailed in Kinel (2026) <doi:10.2139/ssrn.6371138>.

Deploy file changes across multiple GitHub repositories using the GitHub Web API <https://docs.github.com/en/rest>. Allows synchronizing common files, Continuous Integration ('CI') workflows, or configurations across many repositories with a single command.

Advanced methods for a valuable quantitative environmental risk assessment using Bayesian inference of survival and reproduction Data. Among others, it facilitates Bayesian inference of the general unified threshold model of survival (GUTS). See our companion paper Baudrot and Charles (2021) <doi:10.21105/joss.03200>, as well as complementary details in Baudrot et al. (2018) <doi:10.1021/acs.est.7b05464> and Delignette-Muller et al. (2017) <doi:10.1021/acs.est.6b05326>.

There are two functions-meta2d and meta3d for detecting rhythmic signals from time-series datasets. For analyzing time-series datasets without individual information, meta2d is suggested, which could incorporates multiple methods from ARSER, JTK_CYCLE and Lomb-Scargle in the detection of interested rhythms. For analyzing time-series datasets with individual information, meta3d is suggested, which takes use of any one of these three methods to analyze time-series data individual by individual and gives out integrated values based on analysis result of each individual.

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

High-performance MongoDB client based on mongo-c-driver and jsonlite'. Includes support for aggregation, indexing, map-reduce, streaming, encryption, enterprise authentication, and GridFS. The online user manual provides an overview of the available methods in the package: <https://jeroen.github.io/mongolite/>.

This package provides an interactive shiny application for teaching and applied analysis of geostatistical data. Users can explore spatial data, assess spatial correlation through the empirical variogram, fit model-based geostatistical models for continuous, prevalence and count outcomes, produce spatial predictions, and download reports. The methodology follows the model-based geostatistics framework of Diggle and Giorgi (2019, ISBN:9781138732353).

Mouse-tracking, the analysis of mouse movements in computerized experiments, is a method that is becoming increasingly popular in the cognitive sciences. The mousetrap package offers functions for importing, preprocessing, analyzing, aggregating, and visualizing mouse-tracking data. An introduction into mouse-tracking analyses using mousetrap can be found in Wulff, Kieslich, Henninger, Haslbeck, & Schulte-Mecklenbeck (2023) <doi:10.31234/osf.io/v685r> (preprint: <https://osf.io/preprints/psyarxiv/v685r>).

Allows the user to generate a list of features (gene, pseudo, RNA, CDS, and/or UTR) directly from NCBI database for any species with a current build available. Option to save downloaded and formatted files is available, and the user can prioritize the feature list based on type and assembly builds present in the current build used. The user can then use the list of features generated or provide a list to map a set of markers (designed for SNP markers with a single base pair position available) to the closest feature based on the map build. This function does require map positions of the markers to be provided and the positions should be based on the build being queried through NCBI.

This package provides a session-based IMAP client that implements the full command sets of the IMAP4rev2 (RFC 9051) and IMAP4rev1 (RFC 3501) protocols, along with the optional extensions registered with the Internet Assigned Numbers Authority, allowing virtually all e-mail operations to be performed from within R, paving the way for e-mail data analysis.

An R interface to the MinIO Client. The MinIO Client ('mc') provides a modern alternative to UNIX commands like ls', cat', cp', mirror', diff', find etc. It supports filesystems and Amazon "S3" compatible cloud storage service ("AWS" Signature v2 and v4). This package provides convenience functions for installing the MinIO client and running any operations, as described in the official documentation, <https://docs.min.io/aistor/reference/cli/>. This package provides a flexible and high-performance alternative to aws.s3'.

Generate interactive html reports that enable quick visual review of multiple related time series stored in a data frame. For static datasets, this can help to identify any temporal artefacts that may affect the validity of subsequent analyses. For live data feeds, regularly scheduled reports can help to pro-actively identify data feed problems or unexpected trends that may require action. The reports are self-contained and shareable without a web server.

This package provides tools to analysis of experiments having two or more quantitative explanatory variables and one quantitative dependent variable. Experiments can be without repetitions or with a statistical design (Hair JF, 2016) <ISBN: 13: 978-0138132637>. Pacote para uma analise de experimentos havendo duas ou mais variaveis explicativas quantitativas e uma variavel dependente quantitativa. Os experimentos podem ser sem repeticoes ou com delineamento estatistico (Hair JF, 2016) <ISBN: 13: 978-0138132637>.

Addressing a central challenge encountered in Mendelian randomization (MR) studies, where MR primarily focuses on discerning the effects of individual exposures on specific outcomes and establishes causal links between them. Using a network-based methodology, the intricacy involving interdependent outcomes due to numerous factors has been tackled through this routine. Based on Ni et al. (2018) <doi:10.1214/17-BA1087>, MR.RGM extends to a broader exploration of the causal landscape by leveraging on network structures and involves the construction of causal graphs that capture interactions between response variables and consequently between responses and instrument variables. The resulting Graph visually represents these causal connections, showing directed edges with effect sizes labeled. MR.RGM facilitates the navigation of various data availability scenarios effectively by accommodating three input formats, i.e., individual-level data and two types of summary-level data. The method also optionally incorporates measured covariates (when available) and allows flexible modeling of the error variance structure, including correlated errors that may reflect unmeasured confounding among responses. In the process, causal effects, adjacency matrices, and other essential parameters of the complex biological networks, are estimated. Besides, MR.RGM provides uncertainty quantification for specific network structures among response variables. Parts of the Inverse Wishart sampler are adapted from the econ722 repository by DiTraglia (GPL-2.0).

This package provides R6 objects to perform parallelized hyperparameter optimization and cross-validation. Hyperparameter optimization can be performed with Bayesian optimization (via rBayesianOptimization <https://cran.r-project.org/package=rBayesianOptimization>) and grid search. The optimized hyperparameters can be validated using k-fold cross-validation. Alternatively, hyperparameter optimization and validation can be performed with nested cross-validation. While mlexperiments focuses on core wrappers for machine learning experiments, additional learner algorithms can be supplemented by inheriting from the provided learner base class.