This package provides functions to calculate estimates of intrinsic and extrinsic noise from the two-reporter single-cell experiment, as in Elowitz, M. B., A. J. Levine, E. D. Siggia, and P. S. Swain (2002) Stochastic gene expression in a single cell. Science, 297, 1183-1186. Functions implement multiple estimators developed for unbiasedness or min Mean Squared Error (MSE) in Fu, A. Q. and Pachter, L. (2016). Estimating intrinsic and extrinsic noise from single-cell gene expression measurements. Statistical Applications in Genetics and Molecular Biology, 15(6), 447-471.

This package provides a near drop-in replacement for base::Sys.sleep() that allows more types of input to produce delays in the execution of code and can silence/prevent typical sources of error.

This package provides utility functions and objects for Extreme Value Analysis. These include probability functions with their exact derivatives w.r.t. the parameters that can be used for estimation and inference, even with censored observations. The transformations exchanging the two parameterizations of Peaks Over Threshold (POT) models: Poisson-GP and Point-Process are also provided with their derivatives.

This package performs network-based source estimation. Different approaches are available: effective distance median, recursive backtracking, and centrality-based source estimation. Additionally, we provide public transportation network data as well as methods for data preparation, source estimation performance analysis and visualization.

This package provides a set of techniques that can be used to develop, validate, and implement automated classifiers. A powerful tool for transforming raw data into meaningful information, ncodeR (Shaffer, D. W. (2017) Quantitative Ethnography. ISBN: 0578191687) is designed specifically for working with big data: large document collections, logfiles, and other text data.

R functions for (non)linear time series analysis with an emphasis on nonparametric autoregression and order estimation, and tests for linearity / additivity.

Nonparametric methods for analysis of covariance (ANCOVA) are distribution-free and provide a flexible statistical framework for situations where the assumptions of parametric ANCOVA are violated or when the response variable is ordinal. This package implements several well-known nonparametric ANCOVA procedures, including Quade, Puri and Sen, McSweeney and Porter, Burnett and Barr, Hettmansperger and McKean, Shirley, and Puri-Sen-Harwell-Serlin. The package provides user-friendly functions to apply these methods in practice. These methods are described in Olejnik et al. (1985) <doi:10.1177/0193841X8500900104> and Harwell et al. (1988) <doi:10.1037/0033-2909.104.2.268>.

Partial informational correlation (PIC) is used to identify the meaningful predictors to the response from a large set of potential predictors. Details of methodologies used in the package can be found in Sharma, A., Mehrotra, R. (2014). <doi:10.1002/2013WR013845>, Sharma, A., Mehrotra, R., Li, J., & Jha, S. (2016). <doi:10.1016/j.envsoft.2016.05.021>, and Mehrotra, R., & Sharma, A. (2006). <doi:10.1016/j.advwatres.2005.08.007>.

This package provides utility functions and custom probability distribution for Bayesian analyses of radiocarbon dates within the nimble modelling framework. It includes various population growth models, nimbleFunction objects, as well as a suite of functions for prior and posterior predictive checks for demographic inference (Crema and Shoda (2021) <doi:10.1371/journal.pone.0251695>) and other analyses.

An R interface to the Julia package NeuralEstimators.jl'. The package facilitates the user-friendly development of neural Bayes estimators, which are neural networks that map data to a point summary of the posterior distribution (Sainsbury-Dale et al., 2024, <doi:10.1080/00031305.2023.2249522>). These estimators are likelihood-free and amortised, in the sense that, once the neural networks are trained on simulated data, inference from observed data can be made in a fraction of the time required by conventional approaches. The package also supports amortised Bayesian or frequentist inference using neural networks that approximate the posterior or likelihood-to-evidence ratio (Zammit-Mangion et al., 2025, Sec. 3.2, 5.2, <doi:10.48550/arXiv.2404.12484>). The package accommodates any model for which simulation is feasible by allowing users to define models implicitly through simulated data.

Features tools for the network data analysis and community detection. Provides multiple methods for fitting, model selection and goodness-of-fit testing in degree-corrected stochastic blocks models. Most of the computations are fast and scalable for sparse networks, esp. for Poisson versions of the models. Implements the following: Amini, Chen, Bickel and Levina (2013) <doi:10.1214/13-AOS1138> Bickel and Sarkar (2015) <doi:10.1111/rssb.12117> Lei (2016) <doi:10.1214/15-AOS1370> Wang and Bickel (2017) <doi:10.1214/16-AOS1457> Zhang and Amini (2020) <arXiv:2012.15047> Le and Levina (2022) <doi:10.1214/21-EJS1971>.

This package provides a set of functions to estimate outcomes of fourth down plays in the National Football League and obtain fourth down plays from <https://www.nfl.com/> and <https://www.espn.com/>.

This package provides a variety of Network Scale-up Models for researchers to analyze Aggregated Relational Data, through the use of Stan and glmmTMB'. Also provides tools for model checking In this version, the package implements models from Laga, I., Bao, L., and Niu, X (2023) <doi:10.1080/01621459.2023.2165929>, Zheng, T., Salganik, M. J., and Gelman, A. (2006) <doi:10.1198/016214505000001168>, Killworth, P. D., Johnsen, E. C., McCarty, C., Shelley, G. A., and Bernard, H. R. (1998) <doi:10.1016/S0378-8733(96)00305-X>, and Killworth, P. D., McCarty, C., Bernard, H. R., Shelley, G. A., and Johnsen, E. C. (1998) <doi:10.1177/0193841X9802200205>.

In shotgun proteomics, shared peptides (i.e., peptides that might originate from different proteins sharing homology, from different proteoforms due to alternative mRNA splicing, post-translational modifications, proteolytic cleavages, and/or allelic variants) represent a major source of ambiguity in protein identifications. The net4pg package allows to assess and handle ambiguity of protein identifications. It implements methods for two main applications. First, it allows to represent and quantify ambiguity of protein identifications by means of graph connected components (CCs). In graph theory, CCs are defined as the largest subgraphs in which any two vertices are connected to each other by a path and not connected to any other of the vertices in the supergraph. Here, proteins sharing one or more peptides are thus gathered in the same CC (multi-protein CC), while unambiguous protein identifications constitute CCs with a single protein vertex (single-protein CCs). Therefore, the proportion of single-protein CCs and the size of multi-protein CCs can be used to measure the level of ambiguity of protein identifications. The package implements a strategy to efficiently calculate graph connected components on large datasets and allows to visually inspect them. Secondly, the net4pg package allows to exploit the increasing availability of matched transcriptomic and proteomic datasets to reduce ambiguity of protein identifications. More precisely, it implement a transcriptome-based filtering strategy fundamentally consisting in the removal of those proteins whose corresponding transcript is not expressed in the sample-matched transcriptome. The underlying assumption is that, according to the central dogma of biology, there can be no proteins without the corresponding transcript. Most importantly, the package allows to visually inspect the effect of the filtering on protein identifications and quantify ambiguity before and after filtering by means of graph connected components. As such, it constitutes a reproducible and transparent method to exploit transcriptome information to enhance protein identifications. All methods implemented in the net4pg package are fully described in Fancello and Burger (2022) <doi:10.1186/s13059-022-02701-2>.

Enables correction for technical variance in raw quantitative reverse transcription polymerase chain reaction (qRT-PCR) data using the least squares-based NORMAgene data-driven normalization algorithm originally described by Heckmann et al. (2011) <doi:10.1186/1471-2105-12-250>. Performs normalization of raw crossing threshold values (CT) and also calculates relative variability metrics that can be used to assess the impact of normalization on variance.

Miscellaneous R functions developed as collateral damage over the course of work in statistical and scientific computing for research. These include, for example, utilities that supplement existing idiosyncrasies of the R language, extend existing plotting functionality and aesthetics, help prepare data objects for imputation, and extend access to command line tools and systems-level information.

It provides a framework and a fast and simple way for researchers to evaluate methods (particularly some data-driven methods or their own methods) and then select a best one for data normalization in the gene expression analysis, based on the consistency of metrics and the consistency of datasets. Zhenfeng Wu, Weixiang Liu, Xiufeng Jin, Deshui Yu, Hua Wang, Gustavo Glusman, Max Robinson, Lin Liu, Jishou Ruan and Shan Gao (2018) <doi:10.1101/251140>.

This package provides a compact variation of the usual syntax of function declaration, in order to support tidyverse-style quasiquotation of a function's arguments and body.

Estimators and variance estimators tailored to the NILS hierarchical design (Adler et al. 2020, <https://res.slu.se/id/publ/105630>; Grafström et al. 2023, <https://res.slu.se/id/publ/128235>). The National Inventories of Landscapes in Sweden (NILS) is a long-term national monitoring program that collects, analyses and presents data on Swedish nature, covering both common and rare habitats <https://www.slu.se/om-slu/organisation/institutioner/skoglig-resurshushallning/miljoanalys/nils/>.

It names the R Markdown chunks of files based on the filename.

Implementation of forward selection based on cross-validated linear and logistic regression.

Novel responsive tools for developing R based Shiny dashboards and applications. The scripts and style sheets are based on jQuery <https://jquery.com/> and Bootstrap <https://getbootstrap.com/>.

Implement and enhance the performance of spatial fuzzy clustering using Fuzzy Geographically Weighted Clustering with various optimization algorithms, mainly from Xin She Yang (2014) <ISBN:9780124167438> with book entitled Nature-Inspired Optimization Algorithms. The optimization algorithm is useful to tackle the disadvantages of clustering inconsistency when using the traditional approach. The distance measurements option is also provided in order to increase the quality of clustering results. The Fuzzy Geographically Weighted Clustering with nature inspired optimisation algorithm was firstly developed by Arie Wahyu Wijayanto and Ayu Purwarianti (2014) <doi:10.1109/CITSM.2014.7042178> using Artificial Bee Colony algorithm.

Network Pre-Processing and normalization. Methods for normalizing graphs, including Chua normalization, Laplacian normalization, Binary magnification, min-max normalization and others. Methods to sparsify adjacency matrices. Methods for graph pre-processing and for filtering edges of the graph.