ARFIMA, in-mean, external regressors and various GARCH flavors, with methods for fit, forecast, simulation, inference and plotting.

Robust generalized linear models (GLM) using a mixture method, as described in Beath (2018) <doi:10.1080/02664763.2017.1414164>. This assumes that the data are a mixture of standard observations, being a generalised linear model, and outlier observations from an overdispersed generalized linear model. The overdispersed linear model is obtained by including a normally distributed random effect in the linear predictor of the generalized linear model.

The basic algorithm to perform the folding test of unimodality. Given a dataset X (d dimensional, n samples), the test checks whether the distribution of the data are rather unimodal or rather multimodal. This package stems from the following research publication: Siffer Alban, Pierre-Alain Fouque, Alexandre Termier, and Christine Largouët. "Are your data gathered?" In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery Data Mining, pp. 2210-2218. ACM, 2018. <doi:10.1145/3219819.3219994>.

This package provides a flexible and streamlined pipeline for formatting, analyzing, and visualizing omics data, regardless of omics type (e.g. transcriptomics, proteomics, metabolomics). The package includes tools for shaping input data into analysis-ready structures, fitting linear or mixed-effect models, extracting key contrasts, and generating a rich variety of ready-to-use publication-quality plots. Designed for transparency and reproducibility across a wide range of study designs, with customizable components for statistical modeling.

Download up-to-date data from the Reserve Bank of Australia in a tidy data frame. Package includes functions to download current and historical statistical tables (<https://www.rba.gov.au/statistics/tables/>) and forecasts (<https://www.rba.gov.au/publications/smp/forecasts-archive.html>). Data includes a broad range of Australian macroeconomic and financial time series.

Detects copy number alteration events in targeted exon sequencing data for tumor samples without matched normal controls. The advantage of this method is that it can be applied to smaller sequencing panels including evaluations of exon, transcript, gene, or even user specified genetic regions of interest. Functions in the package include steps for GC-content correction, calculation of quantile based normal karyotype ranges, and calculation of feature score. Cutoffs for "normal" quantile and score are user-adjustable.

This package provides a comprehensive set of tools designed for optimizing likelihood within a tie-oriented (Butts, C., 2008, <doi:10.1111/j.1467-9531.2008.00203.x>) or an actor-oriented modelling framework (Stadtfeld, C., & Block, P., 2017, <doi:10.15195/v4.a14>) in relational event networks. The package accommodates both frequentist and Bayesian approaches. The frequentist approaches that the package incorporates are the Maximum Likelihood Optimization (MLE) and the Gradient-based Optimization (GDADAMAX). The Bayesian methodologies included in the package are the Bayesian Sampling Importance Resampling (BSIR) and the Hamiltonian Monte Carlo (HMC). The flexibility of choosing between frequentist and Bayesian optimization approaches allows researchers to select the estimation approach which aligns the most with their analytical preferences.

This package provides a simple approach to configuring R projects with different parameter values. Configurations are specified using a reduced subset of base R and parsed accordingly.

This package contains tools for reading and writing data from or to files in the formats: akterm, dmna, Scintec Format-1, and Campbell Scientific TOA5.

We provide a variety of algorithms for manifold-valued data, including Fréchet summaries, hypothesis testing, clustering, visualization, and other learning tasks. See Bhattacharya and Bhattacharya (2012) <doi:10.1017/CBO9781139094764> for general exposition to statistics on manifolds.

Makes documents containing plots and tables from a table of R codes. Can make "HTML", "pdf('LaTex')", "docx('MS Word')" and "pptx('MS Powerpoint')" documents with or without R code. In the package, modularized shiny app codes are provided. These modules are intended for reuse across applications.

This package provides a set of functions to see and interactively adjust a distribution of lessons by day, aiming at homogenizing individual distributions (for each class and teacher).

This package performs both classical and robust panel clustering by applying Principal Component Analysis (PCA) for dimensionality reduction and clustering via standard K-Means or Trimmed K-Means. The method is designed to ensure stable and reliable clustering, even in the presence of outliers. Suitable for analyzing panel data in domains such as economic research, financial time-series, healthcare analytics, and social sciences. The package allows users to choose between classical K-Means for standard clustering and Trimmed K-Means for robust clustering, making it a flexible tool for various applications. For this package, we have benefited from the studies Rencher (2003), Wang and Lu (2021) <DOI:10.25236/AJBM.2021.031018>, Cuesta-Albertos et al. (1997) <https://www.jstor.org/stable/2242558?seq=1>.

Linear model calculations are made for many random versions of data. Using residual randomization in a permutation procedure, sums of squares are calculated over many permutations to generate empirical probability distributions for evaluating model effects. Additionally, coefficients, statistics, fitted values, and residuals generated over many permutations can be used for various procedures including pairwise tests, prediction, classification, and model comparison. This package should provide most tools one could need for the analysis of high-dimensional data, especially in ecology and evolutionary biology, but certainly other fields, as well.

Estimation of Bayes and local Bayes false discovery rates for replicability analysis (Heller & Yekutieli, 2014 <doi:10.1214/13-AOAS697> ; Heller at al., 2015 <doi: 10.1093/bioinformatics/btu434>).

An interface between the GRASS geographical information system ('GIS') and R', based on starting R from within the GRASS GIS environment, or running a free-standing R session in a temporary GRASS location; the package provides facilities for using all GRASS commands from the R command line. The original interface package for GRASS 5 (2000-2010) is described in Bivand (2000) <doi:10.1016/S0098-3004(00)00057-1> and Bivand (2001) <https://www.r-project.org/conferences/DSC-2001/Proceedings/Bivand.pdf>. This was succeeded by spgrass6 for GRASS 6 (2006-2016) and rgrass7 for GRASS 7 (2015-present). The rgrass package modernizes the interface for GRASS 8 while still permitting the use of GRASS 7'.

This package contains miscellaneous functions useful in biostatistics, mostly univariate and multivariate testing procedures with a special emphasis on permutation tests. Many functions intend to simplify user's life by shortening existing procedures or by implementing plotting functions that can be used with as many methods from different packages as possible.

Interface of MIXMOD software for supervised, unsupervised and semi-supervised classification with mixture modelling <doi: 10.18637/jss.v067.i06>.

In repeated measures studies with extreme large or small values it is common that the subjects measurements on average are closer to the mean of the basic population. Interpreting possible changes in the mean in such situations can lead to biased results since the values were not randomly selected, they come from truncated sampling. This method allows to estimate the range of means where treatment effects are likely to occur when regression toward the mean is present. Ostermann, T., Willich, Stefan N. & Luedtke, Rainer. (2008). Regression toward the mean - a detection method for unknown population mean based on Mee and Chua's algorithm. BMC Medical Research Methodology.<doi:10.1186/1471-2288-8-52>. Acknowledgments: We would like to acknowledge "Lena Roth" and "Nico Steckhan" for the package's initial updates (Q3 2024) and continued supervision and guidance. Both have contributed to discussing and integrating these methods into the package, ensuring they are up-to-date and contextually relevant.

R implementation of the common parsing tools lex and yacc'.

Implementation of the algorithms (with minor modifications) to correct bias in quantitative DNA methylation analyses as described by Moskalev et al. (2011) <doi:10.1093/nar/gkr213>. Publication: Kapsner et al. (2021) <doi:10.1002/ijc.33681>.

This package provides methods for downloading and processing data and metadata from Kolada', the official Swedish regions and municipalities database <https://www.kolada.se/>.

Drift-Diffusion Model (DDM) has been widely used to model binary decision-making tasks, and many research studies the relationship between DDM parameters and other characteristics of the subject. This package uses RStan to perform generalized liner regression analysis over DDM parameters via a single Bayesian Hierarchical model. Compared to estimating DDM parameters followed by a separate regression model, RegDDM reduces bias and improves statistical power.

This package implements the RecMap MP2 construction heuristic <doi:10.1109/INFVIS.2004.57>. This algorithm draws maps according to a given statistical value, e.g., election results, population, or epidemiological data. The basic idea of the RecMap algorithm is that each map region, e.g., different countries, is represented by a rectangle. The area of each rectangle represents the statistical value provided as input to maintain zero cartographic error. Computationally intensive tasks are implemented in C++. The included vignette documents recmap algorithm usage.