This package provides a comprehensive suite of tools for analyzing omics data. It includes functionalities for alpha diversity analysis, beta diversity analysis, differential abundance analysis, community assembly analysis, visualization of phylogenetic tree, and functional enrichment analysis. With a progressive approach, the package offers a range of analysis methods to explore and understand the complex communities. It is designed to support researchers and practitioners in conducting in-depth and professional omics data analysis.

Automate pharmacokinetic/pharmacodynamic bioanalytical procedures based on best practices and regulatory recommendations. The package impose regulatory constrains and sanity checking for common bioanalytical procedures. Additionally, PKbioanalysis provides a relational infrastructure for plate management and injection sequence.

This package provides additional functions for evaluating predictive models, including plotting calibration curves and model-based Receiver Operating Characteristic (mROC) based on Sadatsafavi et al (2021) <arXiv:2003.00316>.

Large-scale gene expression studies allow gene network construction to uncover associations among genes. This package is developed for estimating and testing partial correlation graphs with prior information incorporated.

Smoothing splines with penalties on order m derivatives.

Generation of multiple count, binary and continuous variables simultaneously given the marginal characteristics and association structure. Throughout the package, the word Poisson is used to imply count data under the assumption of Poisson distribution. The details of the method are explained in Amatya et al. (2015) <DOI:10.1080/00949655.2014.953534>.

Shiny app to interactively visualize hierarchical clustering with prototypes. For details on hierarchical clustering with prototypes, see Bien and Tibshirani (2011) <doi:10.1198/jasa.2011.tm10183>. This package currently launches the application.

Determine the chlorophyll a (Chl a) concentrations of different phytoplankton groups based on their pigment biomarkers. The method uses non-negative matrix factorisation and simulated annealing to minimise error between the observed and estimated values of pigment concentrations (Hayward et al. (2023) <doi:10.1002/lom3.10541>). The approach is similar to the widely used CHEMTAX program (Mackey et al. 1996) <doi:10.3354/meps144265>, but is more straightforward, accurate, and not reliant on initial guesses for the pigment to Chl a ratios for phytoplankton groups.

This package implements an extension of the Chacko chi-square test for ordered vectors (Chacko, 1966, <https://www.jstor.org/stable/25051572>). Our extension brings the Chacko test to the computer age by implementing a permutation test to offer a numeric estimate of the p-value, which is particularly useful when the analytic solution is not available.

PACTA (Paris Agreement Capital Transition Assessment) for Banks is a tool that allows banks to calculate the climate alignment of their corporate lending portfolios. This package is designed to make it easy to install and load multiple PACTA for Banks packages in a single step. It also provides thorough documentation - the PACTA for Banks cookbook at <https://rmi-pacta.github.io/pacta.loanbook/articles/cookbook_overview.html> - on how to run a PACTA for Banks analysis. This covers prerequisites for the analysis, the separate steps of running the analysis, the interpretation of PACTA for Banks results, and advanced use cases.

This package implements partial transfer learning (PTL) for causal effect estimation using source and target data, with bootstrap-based source detection. Provides data generating processes and nuisance functions for simulation.

The introduction of the broom package has made converting model objects into data frames as simple as a single function. While the broom package focuses on providing tidy data frames that can be used in advanced analysis, it deliberately stops short of providing functionality for reporting models in publication-ready tables. pixiedust provides this functionality with a programming interface intended to be similar to ggplot2's system of layers with fine tuned control over each cell of the table. Options for output include printing to the console and to the common markdown formats (markdown, HTML, and LaTeX). With a little pixiedust (and happy thoughts) tables can really fly.

The 2017 American College of Cardiology and American Heart Association blood pressure guideline recommends using 10-year predicted atherosclerotic cardiovascular disease risk to guide the decision to initiate or intensify antihypertensive medication. The guideline recommends using the Pooled Cohort risk prediction equations to predict 10-year atherosclerotic cardiovascular disease risk. This package implements the original Pooled Cohort risk prediction equations and also incorporates updated versions based on more contemporary data and statistical methods.

This package provides data set and function for exploration of Multiple Indicator Cluster Survey 2014 Women (age 15-49 years) questionnaire data for Punjab, Pakistan.

This package provides the infrastructure to define and analyze the solutions of Partially Observable Markov Decision Process (POMDP) models. Interfaces for various exact and approximate solution algorithms are available including value iteration, point-based value iteration and SARSOP. Hahsler and Cassandra <doi:10.32614/RJ-2024-021>.

Create the density contour plot for bivariate inverse Gaussian distribution for given non negative random variables.

This package provides a simple implementation of the Predictive Information Index ('PII').

Conduct permutation One-Way or Two-Way Analysis of Variance in R. Use different permutation types for two-way designs.

This package provides a progression model for repeated measures (PMRM) is a continuous-time nonlinear mixed-effects model for longitudinal clinical trials in progressive diseases. Unlike mixed models for repeated measures (MMRMs), which estimate treatment effects as linear combinations of additive effects on the outcome scale, PMRMs characterize treatment effects in terms of the underlying disease trajectory. This framing yields clinically interpretable quantities such as average time saved and percent reduction in decline due to treatment. This package implements frequentist PMRMs by Raket (2022) <doi:10.1002/sim.9581> using RTMB by Kristensen (2016) <doi:10.18637/jss.v070.i05>.

This package implements the primePCA algorithm, developed and analysed in Zhu, Z., Wang, T. and Samworth, R. J. (2019) High-dimensional principal component analysis with heterogeneous missingness. <arXiv:1906.12125>.

Levels and changes of productivity and profitability are measured with various indices. The package contains the multiplicatively complete Färe-Primont, Fisher, Hicks-Moorsteen, Laspeyres, Lowe, and Paasche indices, as well as the classic Malmquist productivity index. Färe-Primont and Lowe indices verify the transitivity property and can therefore be used for multilateral or multitemporal comparison. Fisher, Hicks-Moorsteen, Laspeyres, Malmquist, and Paasche indices are not transitive and are only to be used for binary comparison. All indices can also be decomposed into different components, providing insightful information on the sources of productivity and profitability changes. In the use of Malmquist productivity index, the technological change index can be further decomposed into bias technological change components. The package also allows to prohibit technological regression (negative technological change). In the case of the Fisher, Hicks-Moorsteen, Laspeyres, Paasche and the transitive Färe-Primont and Lowe indices, it is furthermore possible to rule out technological change. Deflated shadow prices can also be obtained. Besides, the package allows parallel computing as an option, depending on the user's computer configuration. All computations are carried out with the nonparametric Data Envelopment Analysis (DEA), and several assumptions regarding returns to scale are available. All DEA linear programs are implemented using lp_solve'.

The name of the package is derived from the French, pour ridge, and provides functionality for ridge-type estimation of a potpourri of models. Currently, this estimation concerns that of various Gaussian graphical models from different study designs. Among others it considers the regular Gaussian graphical model and a mixture of such models. The porridge-package implements the estimation of the former either from i) data with replicated observations by penalized loglikelihood maximization using the regular ridge penalty on the parameters (van Wieringen, Chen, 2021) or ii) from non-replicated data by means of either a ridge estimator with multiple shrinkage targets (as presented in van Wieringen et al. 2020, <doi:10.1016/j.jmva.2020.104621>) or the generalized ridge estimator that allows for both the inclusion of quantitative and qualitative prior information on the precision matrix via element-wise penalization and shrinkage (van Wieringen, 2019, <doi:10.1080/10618600.2019.1604374>). Additionally, the porridge-package facilitates the ridge penalized estimation of a mixture of Gaussian graphical models (Aflakparast et al., 2018). On another note, the package also includes functionality for ridge-type estimation of the generalized linear model (as presented in van Wieringen, Binder, 2022, <doi:10.1080/10618600.2022.2035231>).

This package provides functions for bootstrapping the power of ANOVA designs based on estimated means and standard deviations of the conditions. Please refer to the documentation of the boot.power.anova() function for further details.

Estimation and inference of spatial and spatio-temporal semiparametric models including spatial or spatio-temporal non-parametric trends, parametric and non-parametric covariates and, possibly, a spatial lag for the dependent variable and temporal correlation in the noise. The spatio-temporal trend can be decomposed in ANOVA way including main and interaction functional terms. Use of SAP algorithm to estimate the spatial or spatio-temporal trend and non-parametric covariates. The methodology of these models can be found in next references Basile, R. et al. (2014), <doi:10.1016/j.jedc.2014.06.011>; Rodriguez-Alvarez, M.X. et al. (2015) <doi:10.1007/s11222-014-9464-2> and, particularly referred to the focus of the package, Minguez, R., Basile, R. and Durban, M. (2020) <doi:10.1007/s10260-019-00492-8>.