Software to aid in modeling and analyzing mass-spectrometry-based proteome melting data. Quantitative data is imported and normalized and thermal behavior is modeled at the protein level. Methods exist for normalization, modeling, visualization, and export of results. For a general introduction to MS-based thermal profiling, see Savitski et al. (2014) <doi:10.1126/science.1255784>.

This package performs network meta-analysis using integrated nested Laplace approximations ('INLA') which is described in Guenhan, Held, and Friede (2018) <doi:10.1002/jrsm.1285>. Includes methods to assess the heterogeneity and inconsistency in the network. Contains more than ten different network meta-analysis dataset. INLA package can be obtained from <https://www.r-inla.org>.

An interface to the mclust package to easily carry out latent profile analysis ("LPA"). Provides functionality to estimate commonly-specified models. Follows a tidy approach, in that output is in the form of a data frame that can subsequently be computed on. Also has functions to interface to the commercial MPlus software via the MplusAutomation package.

Plots ternary diagrams (simplex plots / Gibbs triangles) and Holdridge life zone plots <doi:10.1126/science.105.2727.367> using the standard graphics functions. Allows custom annotation, interpolating, contouring and scaling of plotting region. Includes a Shiny user interface for point-and-click ternary plotting. An alternative to ggtern', which uses the ggplot2 family of plotting functions.

Uniform random samples from simple manifolds, sometimes with noise, are commonly used to test topological data analytic (TDA) tools. This package includes samplers powered by two techniques: analytic volume-preserving parameterizations, as employed by Arvo (1995) <doi:10.1145/218380.218500>, and rejection sampling, as employed by Diaconis, Holmes, and Shahshahani (2013) <doi:10.1214/12-IMSCOLL1006>.

This package translates microarray expression data into metadata of reduced dimension. It provides various sample-centered and group-centered visualizations, sample similarity analyses and functional enrichment analyses. The underlying SOM algorithm combines feature clustering, multidimensional scaling and dimension reduction, along with strong visualization capabilities. It enables extraction and description of functional expression modules inherent in the data.

This package provides a single sample pathway perturbation testing method for RNA-seq data. The method propagates changes in gene expression down gene-set topologies to compute single-sample directional pathway perturbation scores that reflect potential direction of change. Perturbation scores can be used to test significance of pathway perturbation at both individual-sample and treatment levels.

This package provides a data-driven test for the assumptions of quantile normalization using raw data such as objects that inherit eSets (e.g. ExpressionSet, MethylSet). Group level information about each sample (such as Tumor / Normal status) must also be provided because the test assesses if there are global differences in the distributions between the user-defined groups.

Sending functions to remote processes can be wasteful of resources because they carry their environments with them. With this package, it is easy to create functions that are isolated from their environment. These isolated functions, also called crates, print to the console with their total size and can be easily tested locally before being sent to a remote.

This package provides functions for Arps decline-curve analysis on oil and gas data. Includes exponential, hyperbolic, harmonic, and hyperbolic-to-exponential models as well as the preceding with initial curtailment or a period of linear rate buildup. Functions included for computing rate, cumulative production, instantaneous decline, EUR, time to economic limit, and performing least-squares best fits.

This package provides a Bayesian model for examining the association between environmental mixtures and all Taxa measured in a hierarchical microbiome dataset in a single integrated analysis. Compared with analyzing the associations of environmental mixtures with each Taxa individually, BaHZING controls Type 1 error rates and provides more stable effect estimates when dealing with small sample sizes.

This package provides a framework for the replicable removal of personally identifiable data (PID) in data sets. The package implements a suite of methods to suit different data types based on the suggestions of Garfinkel (2015) <doi:10.6028/NIST.IR.8053> and the ICO "Guidelines on Anonymization" (2012) <https://ico.org.uk/media/1061/anonymisation-code.pdf>.

Designing experimental plans that involve both discrete and continuous factors with general parametric statistical models using the ForLion algorithm and EW ForLion algorithm. The algorithms will search for locally optimal designs and EW optimal designs under the D-criterion. Reference: Huang, Y., Li, K., Mandal, A., & Yang, J., (2024)<doi:10.1007/s11222-024-10465-x>.

This package provides tools and methods to apply the model Geospatial Regression Equation for European Nutrient losses (GREEN); Grizzetti et al. (2005) <doi:10.1016/j.jhydrol.2004.07.036>; Grizzetti et al. (2008); Grizzetti et al. (2012) <doi:10.1111/j.1365-2486.2011.02576.x>; Grizzetti et al. (2021) <doi:10.1016/j.gloenvcha.2021.102281>.

Develops a General Equilibrium (GE) Model, which estimates key variables such as wages, the number of residents and workers, the prices of the floor space, and its distribution between commercial and residential use, as in Ahlfeldt et al., (2015) <doi:10.3982/ECTA10876>. By doing so, the model allows understanding the economic influence of different urban policies.

This package provides a comprehensive R interface to access data from the Kraken cryptocurrency exchange REST API <https://docs.kraken.com/api/>. It allows users to retrieve various market data, such as asset information, trading pairs, and price data. The package is designed to facilitate efficient data access for analysis, strategy development, and monitoring of cryptocurrency market trends.

Four measures of linkage disequilibrium are provided: the usual r^2 measure, the r^2_S measure (r^2 corrected by the structure sample), the r^2_V (r^2 corrected by the relatedness of genotyped individuals), the r^2_VS measure (r^2 corrected by both the relatedness of genotyped individuals and the structure of the sample).

This package provides functions to fit finite mixture of scale mixture of skew-normal (FM-SMSN) distributions, details in Prates, Lachos and Cabral (2013) <doi: 10.18637/jss.v054.i12>, Cabral, Lachos and Prates (2012) <doi:10.1016/j.csda.2011.06.026> and Basso, Lachos, Cabral and Ghosh (2010) <doi:10.1016/j.csda.2009.09.031>.

An R wrapper for pulling data from the National Public Transport Access Nodes ('NaPTAN') API (<https://www.api.gov.uk/dft/national-public-transport-access-nodes-naptan-api/#national-public-transport-access-nodes-naptan-api>). This allows users to download NaPTAN transport information, for the full dataset, by ATCO region code, or by name of region.

An implementation of Simultaneous Truth and Performance Level Estimation (STAPLE) <doi:10.1109/TMI.2004.828354>. This method is used when there are multiple raters for an object, typically an image, and this method fuses these ratings into one rating. It uses an expectation-maximization method to estimate this rating and the individual specificity/sensitivity for each rater.

This dataset was collected using a new four-arm within-study comparison design. The study aimed to examine the impact of a mathematics training intervention and a vocabulary study session on post-test scores in mathematics and vocabulary, respectively. The innovative four-arm within-study comparison design facilitates both experimental and quasi-experimental identification of average causal effects.

Crumblr enables analysis of count ratio data using precision weighted linear (mixed) models. It uses an asymptotic normal approximation of the variance following the centered log ration transform (CLR) that is widely used in compositional data analysis. Crumblr provides a fast, flexible alternative to GLMs and GLMM's while retaining high power and controlling the false positive rate.

COSMOS (Causal Oriented Search of Multi-Omic Space) is a method that integrates phosphoproteomics, transcriptomics, and metabolomics data sets based on prior knowledge of signaling, metabolic, and gene regulatory networks. It estimated the activities of transcrption factors and kinases and finds a network-level causal reasoning. Thereby, COSMOS provides mechanistic hypotheses for experimental observations across mulit-omics datasets.

High-throughput sequencing experiments followed by differential expression analysis is a widely used approach to detect genomic biomarkers. A fundamental step in differential expression analysis is to model the association between gene counts and covariates of interest. NBAMSeq a flexible statistical model based on the generalized additive model and allows for information sharing across genes in variance estimation.