This package provides a set of functions for computing expected permutation matrices given a matrix of likelihoods for each individual assignment. It has been written to accompany the forthcoming paper Computing expectations and marginal likelihoods for permutations'. Publication details will be updated as soon as they are finalized.

This package implements the formulae required to calculate freedom from disease according to Cameron and Baldock (1998) <doi:10.1016/S0167-5877(97)00081-0>. These are the methods used at the Swedish national veterinary institute (SVA) to evaluate the performance of our nation animal disease surveillance programmes.

This package creates a scatter plot after residualizing using a set of covariates. The residuals are calculated using the fixest package which allows very fast estimation that scales. Details of the (Yule-)Frisch-Waugh-Lovell theorem is given in Basu (2023) <doi:10.48550/arXiv.2307.00369>.

This package performs geographically weighted Lasso regressions. Find optimal bandwidth, fit a geographically weighted lasso or ridge regression, and make predictions. These methods are specially well suited for ecological inferences. Bandwidth selection algorithm is from A. Comber and P. Harris (2018) <doi:10.1007/s10109-018-0280-7>.

Geostatistical interpolation has traditionally been done by manually fitting a variogram and then interpolating. Here, we introduce classes and methods that can do this interpolation automatically. Pebesma et al (2010) gives an overview of the methods behind and possible usage <doi:10.1016/j.cageo.2010.03.019>.

Estimates the intraclass correlation coefficient for trajectory data using a matrix of distances between trajectories. The distances implemented are the extended Hausdorff distances (Min et al. 2007) <doi:10.1080/13658810601073315> and the discrete Fréchet distance (Magdy et al. 2015) <doi:10.1109/IntelCIS.2015.7397286>.

It allows to cluster communication networks using the Stochastic Topic Block Model <doi:10.1007/s11222-016-9713-7> by posting jobs through the API of the linkage.fr server, which implements the clustering method. The package also allows to visualize the clustering results returned by the server.

Run flexible mediation analyses using natural effect models as described in Lange, Vansteelandt and Bekaert (2012) <DOI:10.1093/aje/kwr525>, Vansteelandt, Bekaert and Lange (2012) <DOI:10.1515/2161-962X.1014> and Loeys, Moerkerke, De Smet, Buysse, Steen and Vansteelandt (2013) <DOI:10.1080/00273171.2013.832132>.

Calculate the precision in mean differences (raw or Cohen's D) and correlation coefficients for different sample sizes. Uses permutations of the collected functional magnetic resonance imaging (fMRI) region of interest data. Method described in Klapwijk, Jongerling, Hoijtink and Crone (2024) <doi:10.31234/osf.io/cz32t>.

This package provides methods to detect genetic markers involved in biological adaptation. pcadapt provides statistical tools for outlier detection based on Principal Component Analysis. Implements the method described in (Luu, 2016) <DOI:10.1111/1755-0998.12592> and later revised in (Privé, 2020) <DOI:10.1093/molbev/msaa053>.

Based on (but not identical to) the no-longer-maintained package phyext', provides enhancements to phylobase classes, specifically for use by package SigTree'; provides classes and methods which help users manipulate branch-annotated trees (as in SigTree'); also provides support for a few other extra features.

This package provides tools for downloading, reading and analyzing the National Survey of Health - PNS, a household survey from Brazilian Institute of Geography and Statistics - IBGE. The data must be downloaded from the official website <https://www.ibge.gov.br/>. Further analysis must be made using package survey'.

Provide regularized maximum covariance analysis incorporating smoothness, sparseness and orthogonality of couple patterns by using the alternating direction method of multipliers algorithm. The method can be applied to either regularly or irregularly spaced data, including 1D, 2D, and 3D (Wang and Huang, 2017 <doi:10.1002/env.2481>).

Implementation of hybrid STL decomposition based time delay neural network model for univariate time series forecasting. For method details see Jha G K, Sinha, K (2014). <doi:10.1007/s00521-012-1264-z>, Xiong T, Li C, Bao Y (2018). <doi:10.1016/j.neucom.2017.11.053>.

Generates random values from a univariate and multivariate continuous distribution by using kernel density estimation based on a sample. Duong (2017) <doi:10.18637/jss.v021.i07>, Christian P. Robert and George Casella (2010 ISBN:978-1-4419-1575-7) <doi:10.1007/978-1-4419-1576-4>.

Programmatic interface to the SNOTEL snow data (<https://www.nrcs.usda.gov/programs-initiatives/sswsf-snow-survey-and-water-supply-forecasting-program>). Provides easy downloads of snow data into your R work space or a local directory. Additional post-processing routines to extract snow season indexes are provided.

Fit linear and logistic regression models penalised with group-adaptive elastic net penalties. The group penalties correspond to groups of covariates defined by a co-data group set. The method accommodates inclusion of unpenalised covariates and overlapping groups. See Van Nee et al. (2021) <arXiv:2101.03875>.

It can be useful to temporarily hide some text or other HTML elements in Shiny applications. Building on Spoiler-Alert.js', it is possible to select the elements to hide at startup, to partially reveal them by hovering them, and to completely show them when clicking on them.

Facilitate the movement between data frames to xts'. Particularly useful when moving from tidyverse to the widely used xts package, which is the input format of choice to various other packages. It also allows the user to use a spread_by argument for a character column xts conversion.

Create rich and fully interactive timeline visualizations. Timelines can be included in Shiny apps or R markdown documents. timevis includes an extensive API to manipulate a timeline after creation, and supports getting data out of the visualization into R. Based on the vis.js Timeline JavaScript library.

This package provides functions that compute predictions from Generalised Additive Models (GAMs) fitted with mgcv and return them as a tibble. These can be plotted with a generic plot()-method that uses ggplot2 or plotted as any other data frame. The main function is predict_gam().

Feasible Multivariate Generalized Autoregressive Conditional Heteroscedasticity (GARCH) models including Dynamic Conditional Correlation (DCC), Copula GARCH and Generalized Orthogonal GARCH with Generalized Hyperbolic distribution. A review of some of these models can be found in Boudt, Galanos, Payseur and Zivot (2019) <doi:10.1016/bs.host.2019.01.001>.

The BPRMeth package is a probabilistic method to quantify explicit features of methylation profiles, in a way that would make it easier to formally use such profiles in downstream modelling efforts, such as predicting gene expression levels or clustering genomic regions or cells according to their methylation profiles.

This package Copynumber KAryotyping of Tumors infers genomic copy number and subclonal structure of human tumors using integrative Bayesian approaches to identify genome-wide aneuploidy at 5MB resolution in single cells data. It separates tumor cells and tumor subclones from normal cells using high-throughput sc-RNAseq data.