This package provides a self-contained set of methods to aid clinical trial safety investigators, statisticians and researchers, in the early detection of adverse events using groupings by body-system or system organ class. This work was supported by the Engineering and Physical Sciences Research Council (UK) (EPSRC) [award reference 1521741] and Frontier Science (Scotland) Ltd. The package title c212 is in reference to the original Engineering and Physical Sciences Research Council (UK) funded project which was named CASE 2/12.

Neural network has potential in forestry modelling. This package is designed to create and assess Artificial Intelligence based Neural Networks with varying architectures for prediction of volume of forest trees using two input features: height and diameter at breast height, as they are the key factors in predicting volume, therefore development and validation of efficient volume prediction neural network model is necessary. This package has been developed using the algorithm of Tabassum et al. (2022) <doi:10.18805/ag.D-5555>.

Ing and Lai (2011) <doi:10.5705/ss.2010.081> proposed a high-dimensional model selection procedure that comprises three steps: orthogonal greedy algorithm (OGA), high-dimensional information criterion (HDIC), and Trim. The first two steps, OGA and HDIC, are used to sequentially select input variables and determine stopping rules, respectively. The third step, Trim, is used to delete irrelevant variables remaining in the second step. This package aims at fitting a high-dimensional linear regression model via OGA+HDIC+Trim.

Fits the regularization path of regression models (linear and logistic) with additively combined penalty terms. All possible combinations with Least Absolute Shrinkage and Selection Operator (LASSO), Smoothly Clipped Absolute Deviation (SCAD), Minimax Concave Penalty (MCP) and Exponential Penalty (EP) are supported. This includes Sparse Group LASSO (SGL), Sparse Group SCAD (SGS), Sparse Group MCP (SGM) and Sparse Group EP (SGE). For more information, see Buch, G., Schulz, A., Schmidtmann, I., Strauch, K., & Wild, P. S. (2024) <doi:10.1002/bimj.202200334>.

This package provides a set of functions is provided for 1) the stratum lengths analysis along a chosen direction, 2) fast estimation of continuous lag spatial Markov chains model parameters and probability computing (also for large data sets), 3) transition probability maps and transiograms drawing, 4) simulation methods for categorical random fields. More details on the methodology are discussed in Sartore (2013) <doi:10.32614/RJ-2013-022> and Sartore et al. (2016) <doi:10.1016/j.cageo.2016.06.001>.

Fit Bayesian hierarchical models of animal abundance and occurrence via the rstan package, the R interface to the Stan C++ library. Supported models include single-season occupancy, dynamic occupancy, and N-mixture abundance models. Covariates on model parameters are specified using a formula-based interface similar to package unmarked', while also allowing for estimation of random slope and intercept terms. References: Carpenter et al. (2017) <doi:10.18637/jss.v076.i01>; Fiske and Chandler (2011) <doi:10.18637/jss.v043.i10>.

This package provides support for transformations of numeric aggregates between statistical classifications (e.g. occupation or industry categorisations) using the Crossmaps framework. Implements classes for representing transformations between a source and target classification as graph structures, and methods for validating and applying crossmaps to transform data collected under the source classification into data indexed using the target classification codes. Documentation about the Crossmaps framework is provided in the included vignettes and in Huang (2024, <doi:10.48550/arXiv.2406.14163>).

DNA methylation contains information about the regulatory state of the cell. MIRA aggregates genome-scale DNA methylation data into a DNA methylation profile for a given region set with shared biological annotation. Using this profile, MIRA infers and scores the collective regulatory activity for the region set. MIRA facilitates regulatory analysis in situations where classical regulatory assays would be difficult and allows public sources of region sets to be leveraged for novel insight into the regulatory state of DNA methylation datasets.

This package provides a scale based normalization (SCBN) method to identify genes with differential expression between different species. It takes into account the available knowledge of conserved orthologous genes and the hypothesis testing framework to detect differentially expressed orthologous genes. The method on this package are described in the article A statistical normalization method and differential expression analysis for RNA-seq data between different species by Yan Zhou, Jiadi Zhu, Tiejun Tong, Junhui Wang, Bingqing Lin, Jun Zhang (2018, pending publication).

This package provides an object-oriented modeling language for disciplined convex programming (DCP) as described in Fu, Narasimhan, and Boyd (2020, <doi:10.18637/jss.v094.i14>). It allows the user to formulate convex optimization problems in a natural way following mathematical convention and DCP rules. The system analyzes the problem, verifies its convexity, converts it into a canonical form, and hands it off to an appropriate solver to obtain the solution. Interfaces to solvers on CRAN and elsewhere are provided.

This package provides the ASUS procedure for estimating a high dimensional sparse parameter in the presence of auxiliary data that encode side information on sparsity. It is a robust data combination procedure in the sense that even when pooling non-informative auxiliary data ASUS would be at least as efficient as competing soft thresholding based methods that do not use auxiliary data. For more information, please see the paper Adaptive Sparse Estimation with Side Information by Banerjee, Mukherjee and Sun (JASA 2020).

This package provides a comprehensive approach for identifying and estimating change points in multivariate time series through various statistical methods. Implements the multiple change point detection methodology from Ryan & Killick (2023) <doi:10.1080/00401706.2023.2183261> and a novel estimation methodology from Fotopoulos et al. (2023) <doi:10.1007/s00362-023-01495-0> generalized to fit the detection methodologies. Performs both detection and estimation of change points, providing visualization and summary information of the estimation process for each detected change point.

This package provides a general, flexible framework for estimating parameters and empirical sandwich variance estimator from a set of unbiased estimating equations (i.e., M-estimation in the vein of Stefanski & Boos (2002) <doi:10.1198/000313002753631330>). All examples from Stefanski & Boos (2002) are published in the corresponding Journal of Statistical Software paper "The Calculus of M-Estimation in R with geex" by Saul & Hudgens (2020) <doi:10.18637/jss.v092.i02>. Also provides an API to compute finite-sample variance corrections.

The heterogeneous multi-task feature learning is a data integration method to conduct joint feature selection across multiple related data sets with different distributions. The algorithm can combine different types of learning tasks, including linear regression, Huber regression, adaptive Huber, and logistic regression. The modified version of Bayesian Information Criterion (BIC) is produced to measure the model performance. Package is based on Yuan Zhong, Wei Xu, and Xin Gao (2022) <https://www.fields.utoronto.ca/talk-media/1/53/65/slides.pdf>.

We provide an R tool for computation and nonparametric plug-in estimation of Highest Density Regions (HDRs) and general level sets in the directional setting. Concretely, circular and spherical HDRs can be reconstructed from a data sample following Saavedra-Nieves and Crujeiras (2021) <doi:10.1007/s11634-021-00457-4>. This library also contains two real datasets in the circular and spherical settings. The first one concerns a problem from animal orientation studies and the second one is related to earthquakes occurrences.

Rapid satellite data streams in operational applications have clear benefits for monitoring land cover, especially when information can be delivered as fast as changing surface conditions. Over the past decade, remote sensing has become a key tool for monitoring and predicting environmental variables by using satellite data. This package presents the main applications in remote sensing for land surface monitoring and land cover mapping (soil, vegetation, water...). Tomlinson, C.J., Chapman, L., Thornes, E., Baker, C (2011) <doi:10.1002/met.287>.

Modified functions of the package pcalg and some additional functions to run the PC and the FCI (Fast Causal Inference) algorithm for constraint-based causal discovery in incomplete and multiply imputed datasets. Foraita R, Friemel J, Günther K, Behrens T, Bullerdiek J, Nimzyk R, Ahrens W, Didelez V (2020) <doi:10.1111/rssa.12565>; Andrews RM, Bang CW, Didelez V, Witte J, Foraita R (2021) <doi:10.1093/ije/dyae113>; Witte J, Foraita R, Didelez V (2022) <doi:10.1002/sim.9535>.

Advanced statistical library offering a method to encapsulate and query the probability space of a dataset effortlessly using Probability Boxes (p-boxes). Its distinctive feature lies in the ease with which users can navigate and analyze marginal, joint, and conditional probabilities while taking into account the underlying correlation structure inherent in the data using copula theory and models. A comprehensive explanation is available in the paper "pbox: Exploring Multivariate Spaces with Probability Boxes" to be published in the Journal of Statistical Software.

Computes pseudo-realizations from the posterior distribution of a Gaussian Process (GP) with the method described in Azzimonti et al. (2016) <doi:10.1137/141000749>. The realizations are obtained from simulations of the field at few well chosen points that minimize the expected distance in measure between the true excursion set of the field and the approximate one. Also implements a R interface for (the main function of) Distance Transform of sampled Functions (<https://cs.brown.edu/people/pfelzens/dt/index.html>).

Two stage curvature identification with machine learning for causal inference in settings when instrumental variable regression is not suitable because of potentially invalid instrumental variables. Based on Guo and Buehlmann (2022) "Two Stage Curvature Identification with Machine Learning: Causal Inference with Possibly Invalid Instrumental Variables" <doi:10.48550/arXiv.2203.12808>. The vignette is available in Carl, Emmenegger, Bühlmann and Guo (2025) "TSCI: Two Stage Curvature Identification for Causal Inference with Invalid Instruments in R" <doi:10.18637/jss.v114.i07>.

The SEQC/MAQC-III Consortium has produced benchmark RNA-seq data for the assessment of RNA sequencing technologies and data analysis methods (Nat Biotechnol, 2014). Billions of sequence reads have been generated from ten different sequencing sites. This package contains the summarized read count data for ~2000 sequencing libraries. It also includes all the exon-exon junctions discovered from the study. TaqMan RT-PCR data for ~1000 genes and ERCC spike-in sequence data are included in this package as well.

This package contains extensions to ggplot2.

1. Geomas: geom_table, geom_plot and geom_grob add insets to plots using native data coordinates, while geom_table_npc, geom_plot_npc and geom_grob_npc do the same using npc coordinates through new aesthetics npcx and npcy.

2. Statistics: select observations based on 2D density.

3. Positions: radial nudging away from a center point and nudging away from a line or curve.

rebar3 is an Erlang build tool that makes it easy to compile and test Erlang applications, port drivers and releases.

rebar3 is a self-contained Erlang script, so it's easy to distribute or even embed directly in a project. Where possible, rebar uses standard Erlang/OTP conventions for project structures, thus minimizing the amount of build configuration work. rebar3 also provides dependency management, enabling application writers to easily re-use common libraries from a variety of locations (git, hg, etc).

This package provides a method for quantifying resilience after a stress event. A set of functions calculate the area of resilience that is created by the departure of baseline y (i.e., robustness) and the time taken x to return to baseline (i.e., rapidity) after a stress event using the Cartesian coordinates of the data. This package has the capability to calculate areas of resilience, growth, and cases in which resilience is not achieved (e.g., diminished performance without return to baseline).