This package provides functions are provided to calculate and display ridge TRACE Diagnostics for a variety of alternative Shrinkage Paths. While all methods focus on Maximum Likelihood estimation of unknown true effects under normal distribution-theory, some estimates are modified to be Unbiased or to have "Correct Range" when estimating either [1] the noncentrality of the F-ratio for testing that true Beta coefficients are Zeros or [2] the "relative" MSE Risk (i.e. MSE divided by true sigma-square, where the "relative" variance of OLS is known.) The eff.ridge() function implements the "Efficient Shrinkage Path" introduced in Obenchain (2022) <Open Statistics>. This "p-Parameter" Shrinkage-Path always passes through the vector of regression coefficient estimates Most-Likely to achieve the overall Optimal Variance-Bias Trade-Off and is the shortest Path with this property. Functions eff.aug() and eff.biv() augment the calculations made by eff.ridge() to provide plots of the bivariate confidence ellipses corresponding to any of the p*(p-1) possible ordered pairs of shrunken regression coefficients. Functions for plotting TRACE Diagnostics now have more options.

This package provides JAR to perform Markov chain Monte Carlo (MCMC) inference using the popular Bayesian Evolutionary Analysis by Sampling Trees BEAST software library of Suchard et al (2018) <doi:10.1093/ve/vey016>. BEAST supports auto-tuning Metropolis-Hastings, slice, Hamiltonian Monte Carlo and Sequential Monte Carlo sampling for a large variety of composable standard and phylogenetic statistical models using high performance computing. By placing the BEAST JAR in this package, we offer an efficient distribution system for BEAST use by other R packages using CRAN.

Computationally efficient tools for comparing all pairs of profiles in a DNA database. The expectation and covariance of the summary statistic is implemented for fast computing. Routines for estimating proportions of close related individuals are available. The use of wildcards (also called F- designation) is implemented. Dedicated functions ease plotting the results. See Tvedebrink et al. (2012) <doi:10.1016/j.fsigen.2011.08.001>. Compute the distribution of the numbers of alleles in DNA mixtures. See Tvedebrink (2013) <doi:10.1016/j.fsigss.2013.10.142>.

Simulates demic diffusion building on models previously developed for the expansion of Neolithic and other food-producing economies during the Holocene (Fort et al. (2012) <doi:10.7183/0002-7316.77.2.203>, Souza et al. (2021) <doi:10.1098/rsif.2021.0499>). Growth and emigration are modelled as density-dependent processes using logistic growth and an asymptotic threshold model. Environmental and terrain layers, which can change over time, affect carrying capacity, growth and mobility. Multiple centres of origin with their respective starting times can be specified.

This package provides a Haar-Fisz algorithm for Poisson intensity estimation. Will denoise Poisson distributed sequences where underlying intensity is not constant. Uses the multiscale variance-stabilization method called the Haar-Fisz transform. Contains functions to carry out the forward and inverse Haar-Fisz transform and denoising on near-Gaussian sequences. Can also carry out cycle-spinning. Main reference: Fryzlewicz, P. and Nason, G.P. (2004) "A Haar-Fisz algorithm for Poisson intensity estimation." Journal of Computational and Graphical Statistics, 13, 621-638. <doi:10.1198/106186004X2697>.

Classification performance metrics that are derived from the ROC curve of a classifier. The package includes the H-measure performance metric as described in <http://link.springer.com/article/10.1007/s10994-009-5119-5>, which computes the minimum total misclassification cost, integrating over any uncertainty about the relative misclassification costs, as per a user-defined prior. It also offers a one-stop-shop for other scalar metrics of performance, including sensitivity, specificity and many others, and also offers plotting tools for ROC curves and related statistics.

R binding for NNG (Nanomsg Next Gen), a successor to ZeroMQ. NNG is a socket library for reliable, high-performance messaging over in-process, IPC, TCP, WebSocket and secure TLS transports. Implements Scalability Protocols', a standard for common communications patterns including publish/subscribe, request/reply and service discovery. As its own threaded concurrency framework, provides a toolkit for asynchronous programming and distributed computing. Intuitive aio objects resolve automatically when asynchronous operations complete, and synchronisation primitives allow R to wait upon events signalled by concurrent threads.

This package provides a modified version of alternating logistic regressions (ALR) with estimation based on orthogonalized residuals (ORTH) is implemented, which use paired estimating equations to jointly estimate parameters in marginal mean and within-association models. The within-cluster association between ordinal responses is modeled by global pairwise odds ratios (POR). A finite-sample bias correction is provided to POR parameter estimates based on matrix multiplicative adjusted orthogonalized residuals (MMORTH) for correcting estimating equations, and different bias-corrected variance estimators such as BC1, BC2, and BC3.

Using the Bayesian state-space approach, we developed a continuous development model to quantify dynamic incremental changes in the response variable. While the model was originally developed for daily changes in forest green-up, the model can be used to predict any similar process. The CDM can capture both timing and rate of nonlinear processes. Unlike statics methods, which aggregate variations into a single metric, our dynamic model tracks the changing impacts over time. The CDM accommodates nonlinear responses to variation in predictors, which changes throughout development.

This package infers the trends of one or several animal populations over time from series of counts. It does so by accounting for count precision (provided or inferred based on expert knowledge, e.g. guesstimates), smoothing the population rate of increase over time, and accounting for the maximum demographic potential of species. Inference is carried out in a Bayesian framework. This work is part of the FRB-CESAB working group AfroBioDrivers <https://www.fondationbiodiversite.fr/en/the-frb-in-action/programs-and-projects/le-cesab/afrobiodrivers/>.

Examines the characteristics of a data frame and a formula to automatically choose the most suitable type of plot out of the following supported options: scatter, violin, box, bar, density, hexagon bin, spine plot, and heat map. The aim of the package is to let the user focus on what to plot, rather than on the "how" during exploratory data analysis. It also automates handling of observation weights, logarithmic axis scaling, reordering of factor levels, and overlaying smoothing curves and median lines. Plots are drawn using ggplot2'.

Assists in the TOPSIS analysis process, designed to return at the end of the answer of the TOPSIS multicriteria analysis, a ranking table with the best option as the analysis proposes. TOPSIS is basically a technique developed by Hwang and Yoon in 1981, starting from the point that the best alternative should be closest to the positive ideal solution and farthest from the negative one, based on several criteria to result in the best benefit. (LIU, H. et al., 2019) <doi:10.1016/j.agwat.2019.105787>.

Easy install and load key packages from the tesselle suite in a single step. The tesselle suite is a collection of packages for research and teaching in archaeology. These packages focus on quantitative analysis methods developed for archaeology. The tesselle packages are designed to work seamlessly together and to complement general-purpose and other specialized statistical packages. These packages can be used to explore and analyze common data types in archaeology: count data, compositional data and chronological data. Learn more about tesselle at <https://www.tesselle.org>.

This package stores the data employed in the vignette of the GSVA package. These data belong to the following publications: Armstrong et al. Nat Genet 30:41-47, 2002; Cahoy et al. J Neurosci 28:264-278, 2008; Carrel and Willard, Nature, 434:400-404, 2005; Huang et al. PNAS, 104:9758-9763, 2007; Pickrell et al. Nature, 464:768-722, 2010; Skaletsky et al. Nature, 423:825-837; Verhaak et al. Cancer Cell 17:98-110, 2010; Costa et al. FEBS J, 288:2311-2331, 2021.

This package provides a comprehensive toolbox for analysing Spatial Point Patterns. It is focused mainly on two-dimensional point patterns, including multitype/marked points, in any spatial region. It also supports three-dimensional point patterns, space-time point patterns in any number of dimensions, point patterns on a linear network, and patterns of other geometrical objects. It supports spatial covariate data such as pixel images and contains over 2000 functions for plotting spatial data, exploratory data analysis, model-fitting, simulation, spatial sampling, model diagnostics, and formal inference.

This package provides a toolset for the exploration of genetic and genomic data. Adegenet provides formal (S4) classes for storing and handling various genetic data, including genetic markers with varying ploidy and hierarchical population structure (genind class), alleles counts by populations (genpop), and genome-wide SNP data (genlight). It also implements original multivariate methods (DAPC, sPCA), graphics, statistical tests, simulation tools, distance and similarity measures, and several spatial methods. A range of both empirical and simulated datasets is also provided to illustrate various methods.

Estimate the lower and upper bound of asymptomatic cases in an epidemic using the capture/recapture methods from Böhning et al. (2020) <doi:10.1016/j.ijid.2020.06.009> and Rocchetti et al. (2020) <doi:10.1101/2020.07.14.20153445>. Note there is currently some discussion about the validity of the methods implemented in this package. You should read carefully the original articles, alongside this answer from Li et al. (2022) <doi:10.48550/arXiv.2209.11334> before using this package in your project.

The Large Language Model (LLM) represents a groundbreaking advancement in data science and programming, and also allows us to extend the world of R. A seamless interface for integrating the OpenAI Web APIs into R is provided in this package. This package leverages LLM-based AI techniques, enabling efficient knowledge discovery and data analysis (see OpenAI Web APIs details <https://openai.com/blog/openai-api>). The previous functions such as seamless translation and image generation have been moved to other packages deepRstudio and stableDiffusion4R'.

Linear or nonlinear cross-lagged panel model can be built from input data. Users can choose the appropriate method from three methods for constructing nonlinear cross lagged models. These three methods include polynomial regression, generalized additive model and generalized linear mixed model.In addition, a function for determining linear relationships is provided. Relevant knowledge of cross lagged models can be learned through the paper by Fredrik Falkenström (2024) <doi:10.1016/j.cpr.2024.102435> and the paper by A Gasparrini (2010) <doi:10.1002/sim.3940>.

The Australian Regulatory Guidelines for Prescription Medicines (ARGPM), guidance on "Stability testing for prescription medicines", recommends to predict the shelf life of chemically derived medicines from stability data by taking the worst case situation at batch release into account. Consequently, if a change over time is observed, a release limit needs to be specified. Finding a release limit and the associated shelf life is supported, as well as the standard approach that is recommended by guidance Q1E "Evaluation of stability data" from the International Council for Harmonisation (ICH).

This package provides a straightforward interface for accessing the IMF (International Monetary Fund) data JSON API, available at <https://data.imf.org/>. This package offers direct access to the primary API endpoints: Dataflow, DataStructure, and CompactData. And, it provides an intuitive interface for exploring available dimensions and attributes, as well as querying individual time-series datasets. Additionally, the package implements a rate limit on API calls to reduce the chances of exceeding service limits (limited to 10 calls every 5 seconds) and encountering response errors.

This package provides tools for reading and writing NIfTI-1.1 (NII) files, including optimized voxelwise read/write operations and a simplified method to write dataframes to NII. Specification of the NIfTI-1.1 format can be found here <https://nifti.nimh.nih.gov/nifti-1>. Scientific publication first using these tools Koscik TR, Man V, Jahn A, Lee CH, Cunningham WA (2020) <doi:10.1016/j.neuroimage.2020.116764> "Decomposing the neural pathways in a simple, value-based choice." Neuroimage, 214, 116764.

This package provides a robust approach for omics data integration and disease subtyping. PINSPlus is fast and supports the analysis of large datasets with hundreds of thousands of samples and features. The software automatically determines the optimal number of clusters and then partitions the samples in a way such that the results are robust against noise and data perturbation (Nguyen et al. (2019) <DOI: 10.1093/bioinformatics/bty1049>, Nguyen et al. (2017)<DOI: 10.1101/gr.215129.116>, Nguyen et al. (2021)<DOI: 10.3389/fonc.2021.725133>).

Parametric survival regression models under the maximum likelihood approach via Stan'. Implemented regression models include accelerated failure time models, proportional hazards models, proportional odds models, accelerated hazard models, Yang and Prentice models, and extended hazard models. Available baseline survival distributions include exponential, Weibull, log-normal, log-logistic, gamma, generalized gamma, rayleigh, Gompertz and fatigue (Birnbaum-Saunders) distributions. References: Lawless (2002) <ISBN:9780471372158>; Bennett (1982) <doi:10.1002/sim.4780020223>; Chen and Wang(2000) <doi:10.1080/01621459.2000.10474236>; Demarqui and Mayrink (2021) <doi:10.1214/20-BJPS471>.