Set of functions to estimate kidney function and other traits of interest in nephrology.

Vector AutoRegressive (VAR) type models with tailored regularisation structures are provided to uncover network type structures in the data, such as influential time series (influencers). Currently the package implements the LISAR model from Zhang and Trimborn (2023) <doi:10.2139/ssrn.4619531>. The package automatically derives the required regularisation sequences and refines it during the estimation to provide the optimal model. The package allows for model optimisation under various loss functions such as Mean Squared Forecasting Error (MSFE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). It provides a dedicated class, allowing for summary prints of the optimal model and a plotting function to conveniently analyse the optimal model via heatmaps.

Adding updates (version or bullet points) to the NEWS.md file.

Package to select best model among several linear and nonlinear models. The main function uses the gnls() function from the nlme package to fit the data to nine regression models, named: "linear", "quadratic", "cubic", "logistic", "exponential", "power", "monod", "haldane", "logit".

Non linear dot plots are diagrams that allow dots of varying size to be constructed, so that columns with a large number of samples are reduced in height. Implementation of algorithm described in: Nils Rodrigues and Daniel Weiskopf, "Nonlinear Dot Plots", IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 1, pp. 616-625, 2018. <doi:10.1109/TVCG.2017.2744018>.

Offers a rich and diverse collection of datasets focused on the brain, nervous system, and related disorders. The package includes clinical, experimental, neuroimaging, behavioral, cognitive, and simulated data on conditions such as Parkinson's disease, Alzheimer's disease, dementia, epilepsy, schizophrenia, autism spectrum disorder, attention deficit, hyperactivity disorder, Tourette's syndrome, traumatic brain injury, gliomas, migraines, headaches, sleep disorders, concussions, encephalitis, subarachnoid hemorrhage, and mental health conditions. Datasets cover structural and functional brain data, cross-sectional and longitudinal MRI imaging studies, neurotransmission, gene expression, cognitive performance, intelligence metrics, sleep deprivation effects, treatment outcomes, brain-body relationships across species, neurological injury patterns, and acupuncture interventions. Data sources include peer-reviewed studies, clinical trials, military health records, sports injury databases, and international comparative studies. Designed for researchers, neuroscientists, clinicians, psychologists, data scientists, and students, this package facilitates exploratory data analysis, statistical modeling, and hypothesis testing in neuroscience and neuroepidemiology.

Datasets for testing nonlinear regression routines.

This package provides a system for writing hierarchical statistical models largely compatible with BUGS and JAGS', writing nimbleFunctions to operate models and do basic R-style math, and compiling both models and nimbleFunctions via custom-generated C++. NIMBLE includes default methods for MCMC, Laplace Approximation, deterministic nested approximations, Monte Carlo Expectation Maximization, and some other tools. The nimbleFunction system makes it easy to do things like implement new MCMC samplers from R, customize the assignment of samplers to different parts of a model from R, and compile the new samplers automatically via C++ alongside the samplers NIMBLE provides. NIMBLE extends the BUGS'/'JAGS language by making it extensible: New distributions and functions can be added, including as calls to external compiled code. Although most people think of MCMC as the main goal of the BUGS'/'JAGS language for writing models, one can use NIMBLE for writing arbitrary other kinds of model-generic algorithms as well. A full User Manual is available at <https://r-nimble.org>.

Dealing with neutrosophic data of the form N=D+I(where N is a Neutrosophic number ,D is the determinant part of the number and I is the indeterminacy part) using the neutrosophic two way anova test keeps the type I error low. This algorithm calculates the fisher statistics when we have a neutrosophic data, also tests two hypothesizes, first is to test differences between treatments, and second is to test differences between sectors. For more information see Miari, Mahmoud; Anan, Mohamad Taher; Zeina, Mohamed Bisher(2022) <https://www.americaspg.com/articleinfo/21/show/1058>.

Nonparametric test of independence between a pair of spatial objects (random fields, point processes) based on random shifts with torus or variance correction. See MrkviÄ ka et al. (2021) <doi:10.1016/j.spasta.2020.100430>, DvoŠák et al. (2022) <doi:10.1111/insr.12503>, DvoŠák and MrkviÄ ka (2024) <doi:10.1080/10618600.2024.2357626>.

Cross-Entropy optimisation of unconstrained deterministic and noisy functions illustrated in Rubinstein and Kroese (2004, ISBN: 978-1-4419-1940-3) through a highly flexible and customisable function which allows user to define custom variable domains, sampling distributions, updating and smoothing rules, and stopping criteria. Several built-in methods and settings make the package very easy-to-use under standard optimisation problems.

Assists actuaries and other insurance modellers in pricing, reserving and capital modelling for non-life insurance and reinsurance modelling. Provides functions that help model excess levels, capping and pure Incurred but not reported claims (pure IBNR). Includes capped mean, exposure curves and increased limit factor curves (ILFs) for LogNormal, Gamma, Pareto, Sliced LogNormal-Pareto and Sliced Gamma-Pareto distributions. Includes mean, probability density function (pdf), cumulative probability function (cdf) and inverse cumulative probability function for Sliced LogNormal-Pareto and Sliced Gamma-Pareto distributions. Includes calculating pure IBNR exposure with LogNormal and Gamma distribution for reporting delay. Includes three shiny tools, one to simulate insurance claims applying reinsurance structures, fit generalised linear models and fit claims frequency or severity distributions. Methods used in the package refer to Free for All by Yiannis Parizas (2023) <https://www.theactuary.com/2023/03/02/free-all>; Escaping the triangle by Yiannis Parizas (2019) <https://www.theactuary.com/features/2019/06/2019/06/05/escaping-triangle>; Take to excess by Yiannis Parizas (2019) <https://www.theactuary.com/features/2019/03/2019/03/06/taken-excess>.

Inference and dependence measure for the non-central squared Gaussian, Student, Clayton, Gumbel, and Frank copula models.The description of the methodology is taken from Section 3 of Nasri, Remillard and Bouezmarni (2019) <doi:10.1016/j.jmva.2019.03.007>.

Simulate DNA sequences for the node substitution model. In the node substitution model, substitutions accumulate additionally during a speciation event, providing a potential mechanistic explanation for substitution rate variation. This package provides tools to simulate such a process, simulate a reference process with only substitutions along the branches, and provides tools to infer phylogenies from alignments. More information can be found in Janzen (2021) <doi:10.1093/sysbio/syab085>.

Robust nonparametric bootstrap and permutation tests for location, correlation, and regression problems, as described in Helwig (2019a) <doi:10.1002/wics.1457> and Helwig (2019b) <doi:10.1016/j.neuroimage.2019.116030>. Univariate and multivariate tests are supported. For each problem, exact tests and Monte Carlo approximations are available. Five different nonparametric bootstrap confidence intervals are implemented. Parallel computing is implemented via the parallel package.

Sample sizes are often small due to hard to reach target populations, rare target events, time constraints, limited budgets, or ethical considerations. Two statistical methods with promising performance in small samples are the nonparametric bootstrap test with pooled resampling method, which is the focus of Dwivedi, Mallawaarachchi, and Alvarado (2017) <doi:10.1002/sim.7263>, and informative hypothesis testing, which is implemented in the restriktor package. The npboottprmFBar package uses the nonparametric bootstrap test with pooled resampling method to implement informative hypothesis testing. The bootFbar() function can be used to analyze data with this method and the persimon() function can be used to conduct performance simulations on type-one error and statistical power.

The Needleman-Wunsch global alignment algorithm can be used to find approximate matches between sample names in different data sets. See Wang et al. (2010) <doi:10.4137/CIN.S5613>.

The National Ecological Observatory Network (NEON) provides access to its numerous data products through its REST API, <https://data.neonscience.org/data-api/>. This package provides a high-level user interface for downloading and storing NEON data products. Unlike neonUtilities', this package will avoid repeated downloading, provides persistent storage, and improves performance. neonstore can also construct a local duckdb database of stacked tables, making it possible to work with tables that are far to big to fit into memory.

Fit and compare nonlinear mixed-effects models in differential equations with flexible dosing information commonly seen in pharmacokinetics and pharmacodynamics (Almquist, Leander, and Jirstrand 2015 <doi:10.1007/s10928-015-9409-1>). Differential equation solving is by compiled C code provided in the rxode2 package (Wang, Hallow, and James 2015 <doi:10.1002/psp4.12052>).

Training of neural networks using backpropagation, resilient backpropagation with (Riedmiller, 1994) or without weight backtracking (Riedmiller and Braun, 1993) or the modified globally convergent version by Anastasiadis et al. (2005). The package allows flexible settings through custom-choice of error and activation function. Furthermore, the calculation of generalized weights (Intrator O & Intrator N, 1993) is implemented.

Annotated neuroblastoma copy number profiles, a benchmark data set for change-point detection algorithms, as described by Hocking et al. <doi:10.1186/1471-2105-14-164>.

This package provides nearest-neighbors matching and analysis of case-control data. Cui, Z., Marder, E. P., Click, E. S., Hoekstra, R. M., & Bruce, B. B. (2022) <doi:10.1097/EDE.0000000000001504>.

Nonparametric Tests for Main Effects, Simple Effects and Interaction Effect with Censored Data and Two Factorial Influencing Variables.

Enables users to retrieve data, meta-data, and codebooks from <https://nettskjema.no/>. The data from the API is richer than from the online data portal. This package is not developed by the University of Oslo IT. Mowinckel (2021) <doi:10.5281/zenodo.4745481>.