This package provides tools for calculating I-Scores, a simple way to measure how successful minor political parties are at influencing the major parties in their environment. I-Scores are designed to be a more comprehensive measurement of minor party success than vote share and legislative seats won, the current standard measurements, which do not reflect the strategies that most minor parties employ. The procedure leverages the Manifesto Project's NLP model to identify the issue areas that sentences discuss, see Burst et al. (2024) <doi:10.25522/manifesto.manifestoberta.56topics.context.2024.1.1>, and the Wordfish algorithm to estimate the relative positions that platforms take on those issue areas, see Slapin and Proksch (2008) <doi:10.1111/j.1540-5907.2008.00338.x>.

Calculation of signed root deviance profiles for linear combinations of parameters in a generalized linear model. Multiple tests and simultaneous confidence intervals are provided.

Compute the coefficient of determination for outcomes in n-dimensions. May be useful for multidimensional predictions (such as a multinomial model) or calculating goodness of fit from latent variable models such as probabilistic topic models like latent Dirichlet allocation or deterministic topic models like latent semantic analysis. Based on Jones (2019) <arXiv:1911.11061>.

Converts results from the manymome package, presented in Cheung and Cheung (2023) <doi:10.3758/s13428-023-02224-z>, to publication-ready tables.

This package creates a spectroscopy guideline with a highly accurate prediction model for soil properties using machine learning or deep learning algorithms such as LASSO, Random Forest, Cubist, etc., and decide which algorithm generates the best model for different soil types.

Calculate the maximal fat oxidation, the exercise intensity that elicits the maximal fat oxidation and the SIN model to represent the fat oxidation kinetics. Three variables can be obtained from the SIN model: dilatation, symmetry and translation. Examples of these methods can be found in Montes de Oca et al (2021) <doi:10.1080/17461391.2020.1788650> and Chenevière et al. (2009) <doi:10.1249/MSS.0b013e31819e2f91>.

This package provides a lightweight framework for model selection and hyperparameter tuning in R. The package offers intuitive tools for grid search, cross-validation, and combined grid search with cross-validation that work seamlessly with virtually any modeling package. Designed for flexibility and ease of use, it standardizes tuning workflows while remaining fully compatible with a wide range of model interfaces and estimation functions.

Age-specific mortality rates are estimated and projected using the Kannisto, Lee-Carter and related methods as described in Sevcikova et al. (2016) <doi:10.1007/978-3-319-26603-9_15>.

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.

The ultimate goal is to support 2-2-1, 2-1-1, and 1-1-1 models for multilevel mediation, the option of a moderating variable for either the a, b, or both paths, and covariates. Currently the 1-1-1 model is supported and several options of random effects; the initial code for bootstrapping was evaluated in simulations by Falk, Vogel, Hammami, and MioÄ eviÄ (2024) <doi:10.3758/s13428-023-02079-4>. Support for Bayesian estimation using brms comprises ongoing work. Currently only continuous mediators and outcomes are supported. Factors for any predictors must be numerically represented.

Implementation of two p-value combination techniques (inverse normal and Fisher methods). A vignette is provided to explain how to perform a meta-analysis from two independent RNA-seq experiments.

This package provides a container for data used by the mapindia package. The data used by mapindia has been extracted into this package so that the file size of the mapindia package can be reduced considerably. The data in this package will be updated when latest data is available.

This package creates and manages a PostgreSQL database suitable for storing fisheries data and aggregating ready for use within a Gadget <https://gadget-framework.github.io/gadget2/> model. See <https://mareframe.github.io/mfdb/> for more information.

The algorithms implemented here are used to detect the community structure of a network. These algorithms follow different approaches, but are all based on the concept of modularity maximization.

This package contains the MultiFractal Detrended Fluctuation Analysis (MFDFA), MultiFractal Detrended Cross-Correlation Analysis (MFXDFA), and the Multiscale Multifractal Analysis (MMA). The MFDFA() function proposed in this package was used in Laib et al. (<doi:10.1016/j.chaos.2018.02.024> and <doi:10.1063/1.5022737>). See references for more information. Interested users can find a parallel version of the MFDFA() function on GitHub.

This package contains the function mice.impute.midastouch(). Technically this function is to be run from within the mice package (van Buuren et al. 2011), type ??mice. It substitutes the method pmm within mice by midastouch'. The authors have shown that midastouch is superior to default pmm'. Many ideas are based on Siddique / Belin 2008's MIDAS.

This package provides a set of utility functions for analysing and modelling data from continuous report short-term memory experiments using either the 2-component mixture model of Zhang and Luck (2008) <doi:10.1038/nature06860> or the 3-component mixture model of Bays et al. (2009) <doi:10.1167/9.10.7>. Users are also able to simulate from these models.

Fitting multivariate response models with random effects on one or two levels; whereby the (one-dimensional) random effect represents a latent variable approximating the multivariate space of outcomes, after possible adjustment for covariates. The method is particularly useful for multivariate, highly correlated outcome variables with unobserved heterogeneities. Applications include regression with multivariate responses, as well as multivariate clustering or ranking problems. See Zhang and Einbeck (2024) <doi:10.1007/s42519-023-00357-0>.

This package provides a number of functions to facilitate the handling and production of reports using time series data. The package was developed to be understandable for beginners, so some functions aim to transform processes that would be complex into functions with a few lines. The main advantage of using the metools package is the ease of producing reports and working with time series using a few lines of code, so the code is clean and easy to understand/maintain. Learn more about the metools at <https://metoolsr.wordpress.com>.

This package provides functionality to produce graphs of sampling distributions of test statistics from a variety of common statistical tests. With only a few keystrokes, the user can conduct a hypothesis test and visualize the test statistic and corresponding p-value through the shading of its sampling distribution. Initially created for statistics at Middlebury College.

Lattice functions for drawing folded empirical cumulative distribution plots, or mountain plots. A mountain plot is similar to an empirical CDF plot, except that the curve increases from 0 to 0.5, then decreases from 0.5 to 1 using an inverted scale at the right side. See Monti (1995) <doi:10.1080/00031305.1995.10476179>.

Perform multivariate modeling of evolved traits, with special attention to understanding the interplay of the multi-factorial determinants of their origins in complex ecological settings (Stephens, 2007 <doi:10.1016/j.tree.2006.12.003>). This software primarily concentrates on phylogenetic regression analysis, enabling implementation of tree transformation averaging and visualization functionality. Functions additionally support information theoretic approaches (Grueber, 2011 <doi:10.1111/j.1420-9101.2010.02210.x>; Garamszegi, 2011 <doi:10.1007/s00265-010-1028-7>) such as model averaging and selection of phylogenetic models. Accessory functions are also implemented for coef standardization (Cade 2015), selection uncertainty, and variable importance (Burnham & Anderson 2000). There are other numerous functions for visualizing confounded variables, plotting phylogenetic trees, as well as reporting and exporting modeling results. Lastly, as challenges to ecology are inherently multifarious, and therefore often multi-dataset, this package features several functions to support the identification, interpolation, merging, and updating of missing data and outdated nomenclature.

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.

Efficiently estimates single- and multilevel latent class models with covariates, allowing for output visualization in all specifications. For more technical details, see Lyrvall et al. (2025) <doi:10.1080/00273171.2025.2473935>.