This package provides a collection of functions for preparing data and fitting Bayesian count spatial regression models, with a specific focus on the Gamma-Count (GC) model. The GC model is well-suited for modeling dispersed count data, including under-dispersed or over-dispersed counts, or counts with equivalent dispersion, using Integrated Nested Laplace Approximations (INLA). The package includes functions for generating data from the GC model, as well as spatially correlated versions of the model. See Nadifar, Baghishani, Fallah (2023) <doi:10.1007/s13253-023-00550-5>.

Construct subtests from a pool of items by using ant-colony-optimization, genetic algorithms, brute force, or random sampling. Schultze (2017) <doi:10.17169/refubium-622>.

This package provides functionality for analytically calculating parameters (via the InteractionPoweR package) useful for simulation of moderated multiple regression, based on the correlations among the predictors and outcome and the reliability of predictors.

This package implements the routines and algorithms developed and analysed in "Multiple Systems Estimation for Sparse Capture Data: Inferential Challenges when there are Non-Overlapping Lists" Chan, L, Silverman, B. W., Vincent, K (2019) <arXiv:1902.05156>. This package explicitly handles situations where there are pairs of lists which have no observed individuals in common. It deals correctly with parameters whose estimated values can be considered as being negative infinity. It also addresses other possible issues of non-existence and non-identifiability of maximum likelihood estimates.

This package provides functions for obtaining p-values (for hypothesis tests), confidence intervals, and multivariate confidence sets. In particular, the method is compatible with differentially private dataset, as long as the privacy mechanism is known. For more details, see Awan and Wang (2024), "Simulation-based, Finite-sample Inference for Privatized Data", <doi:10.48550/arXiv.2303.05328>.

Calculates the slope (longitudinal gradient or steepness) of linear geographic features such as roads (for more details, see Ariza-López et al. (2019) <doi:10.1038/s41597-019-0147-x>) and rivers (for more details, see Cohen et al. (2018) <doi:10.1016/j.jhydrol.2018.06.066>). It can use local Digital Elevation Model (DEM) data or download DEM data via the ceramic package. The package also provides functions to add elevation data to linestrings and visualize elevation profiles.

This package provides user friendly methods for the identification of sequence patterns that are statistically significantly associated with a property of the sequence. For instance, SeqFeatR allows to identify viral immune escape mutations for hosts of given HLA types. The underlying statistical method is Fisher's exact test, with appropriate corrections for multiple testing, or Bayes. Patterns may be point mutations or n-tuple of mutations. SeqFeatR offers several ways to visualize the results of the statistical analyses, see Budeus (2016) <doi:10.1371/journal.pone.0146409>.

This package provides a collection of sample datasets on various fields such as automotive performance and safety data to historical demographics and socioeconomic indicators, as well as recreational data. It serves as a resource for researchers and analysts seeking to perform analyses and derive insights from classic data sets in R.

This package provides movies to help students to understand statistical concepts. The rpanel package <https://cran.r-project.org/package=rpanel> is used to create interactive plots that move to illustrate key statistical ideas and methods. There are movies to: visualise probability distributions (including user-supplied ones); illustrate sampling distributions of the sample mean (central limit theorem), the median, the sample maximum (extremal types theorem) and (the Fisher transformation of the) product moment correlation coefficient; examine the influence of an individual observation in simple linear regression; illustrate key concepts in statistical hypothesis testing. Also provided are dpqr functions for the distribution of the Fisher transformation of the correlation coefficient under sampling from a bivariate normal distribution.

This package provides methods for analysis of energy consumption data (electricity, gas, water) at different data measurement intervals. The package provides feature extraction methods and algorithms to prepare data for data mining and machine learning applications. Deatiled descriptions of the methods and their application can be found in Hopf (2019, ISBN:978-3-86309-669-4) "Predictive Analytics for Energy Efficiency and Energy Retailing" <doi:10.20378/irbo-54833> and Hopf et al. (2016) <doi:10.1007/s12525-018-0290-9> "Enhancing energy efficiency in the residential sector with smart meter data analytics".

This package implements the sparse clustering methods of Witten and Tibshirani (2010): "A framework for feature selection in clustering"; published in Journal of the American Statistical Association 105(490): 713-726.

Linear mixed models for complex survey data, by pairwise composite likelihood, as described in Lumley & Huang (2023) <arXiv:2311.13048>. Supports nested and crossed random effects, and correlated random effects as in genetic models. Allows for multistage sampling and for other designs where pairwise sampling probabilities are specified or can be calculated.

Identifies the name of the current script in a variety of contexts, e.g. interactively or when sourced. Attempts to support RStudio environment. Based on <https://stackoverflow.com/a/32016824/2292993> and <https://stackoverflow.com/a/35842176/2292993>.

This package provides functions that facilitate and speed up the analysis of data produced by a Syntech servosphere <http://www.ockenfels-syntech.com/products/locomotion-compensation/>, which is equipment for studying the movement behavior of arthropods. This package is designed to make working with data produced from a servosphere easy for someone new to or unfamiliar with R. The functions provided in this package fall into three broad-use categories: functions for cleaning raw data produced by the servosphere software, functions for deriving movement variables based on position data, and functions for summarizing movement variables for easier analysis. These functions are built with functions from the tidyverse package to work efficiently, as a single servosphere file may consist of hundreds of thousands of rows of data and a user may wish to analyze hundreds of files at a time. Many of the movement variables derivable through this package are described in the following papers: Otálora-Luna, Fernando; Dickens, Joseph C. (2011) <doi:10.1371/journal.pone.0020990> Party, Virginie; Hanot, Christophe; Busser, Daniela Schmidt; Rochat, Didier; Renou, Michel (2013) <doi:10.1371/journal.pone.0052897> Bell, William J.; Kramer, Ernest (1980) <doi:10.1007/BF01402908> Becher, Paul G; Guerin, Patrick M. (2009) <doi:10.1016/j.jinsphys.2009.01.006>.

This package contains an R Markdown template for a clinical trial protocol adhering to the SPIRIT statement. The SPIRIT (Standard Protocol Items for Interventional Trials) statement outlines recommendations for a minimum set of elements to be addressed in a clinical trial protocol. Also contains functions to create a xml document from the template and upload it to clinicaltrials.gov<https://www.clinicaltrials.gov/> for trial registration.

Fast Multiplication and Marginalization of Sparse Tables <doi:10.18637/jss.v111.i02>.

An efficient tool for fitting the nested common and shared atoms models using variational Bayes approximate inference for fast computation. Specifically, the package implements the common atoms model (Denti et al., 2023), its finite version (D'Angelo et al., 2023), and a hybrid finite-infinite model. All models use Gaussian mixtures with a normal-inverse-gamma prior distribution on the parameters. Additional functions are provided to help analyze the results of the fitting procedure. References: Denti, Camerlenghi, Guindani, Mira (2023) <doi:10.1080/01621459.2021.1933499>, Dâ Angelo, Canale, Yu, Guindani (2023) <doi:10.1111/biom.13626>.

Hail is an open-source, general-purpose, python based data analysis tool with additional data types and methods for working with genomic data, see <https://hail.is/>. Hail is built to scale and has first-class support for multi-dimensional structured data, like the genomic data in a genome-wide association study (GWAS). Hail is exposed as a python library, using primitives for distributed queries and linear algebra implemented in scala', spark', and increasingly C++'. The sparkhail is an R extension using sparklyr package. The idea is to help R users to use hail functionalities with the well-know tidyverse syntax, see <https://www.tidyverse.org/>.

We provide functionality to implement penalized PCA with an option to smooth the objective function using Nesterov smoothing. Two functions are available to compute a user-specified number of eigenvectors. The function unsmoothed_penalized_EV() computes a penalized PCA without smoothing and has three parameters (the input matrix, the Lasso penalty, and the number of desired eigenvectors). The function smoothed_penalized_EV() computes a smoothed penalized PCA using the same parameters and additionally requires the specification of a smoothing parameter. Both functions return a matrix having the desired eigenvectors as columns.

Computes the effective range of a smoothing matrix, which is a measure of the distance to which smoothing occurs. This is motivated by the application of spatial splines for adjusting for unmeasured spatial confounding in regression models, but the calculation of effective range can be applied to smoothing matrices in other contexts. For algorithmic details, see Rainey and Keller (2024) "spconfShiny: an R Shiny application..." <doi:10.1371/journal.pone.0311440> and Keller and Szpiro (2020) "Selecting a Scale for Spatial Confounding Adjustment" <doi:10.1111/rssa.12556>.

Semiparametric and parametric estimation of INAR models including a finite sample refinement (Faymonville et al. (2022) <doi:10.1007/s10260-022-00655-0>) for the semiparametric setting introduced in Drost et al. (2009) <doi:10.1111/j.1467-9868.2008.00687.x>, different procedures to bootstrap INAR data (Jentsch, C. and Weià , C.H. (2017) <doi:10.3150/18-BEJ1057>) and flexible simulation of INAR data.

Evaluating the biasing impact of geographic features such as airports, cities, roads, rivers in datasets of coordinates based biological collection datasets, by Bayesian estimation of the parameters of a Poisson process. Enables also spatial visualization of sampling bias and includes a set of convenience functions for publication level plotting. Also available as shiny app. The reference for the methodology is: Zizka et al. (2020) <doi:10.1111/ecog.05102>.

This package contains functions for estimating the STARTS model of Kenny and Zautra (1995, 2001) <DOI:10.1037/0022-006X.63.1.52>, <DOI:10.1037/10409-008>. Penalized maximum likelihood estimation and Markov Chain Monte Carlo estimation are also provided, see Luedtke, Robitzsch and Wagner (2018) <DOI:10.1037/met0000155>.

Assessment of the distributions of baseline continuous and categorical variables in randomised trials. This method is based on the Carlisle-Stouffer method with Monte Carlo simulations. It calculates p-values for each trial baseline variable, as well as combined p-values for each trial - these p-values measure how compatible are distributions of trials baseline variables with random sampling. This package also allows for graphically plotting the cumulative frequencies of computed p-values. Please note that code was partly adapted from Carlisle JB, Loadsman JA. (2017) <doi:10.1111/anae.13650>.