Common ecological distributions for nimble models in the form of nimbleFunction objects. Includes Cormack-Jolly-Seber, occupancy, dynamic occupancy, hidden Markov, dynamic hidden Markov, and N-mixture models. (Jolly (1965) <DOI: 10.2307/2333826>, Seber (1965) <DOI: 10.2307/2333827>, Turek et al. (2016) <doi:10.1007/s10651-016-0353-z>).

This package implements the online Bayesian inference framework for joint state and parameter estimation in a stochastic Susceptible-Exposed-Infectious-Recovered (SEIR) epidemic model with a time-varying transmission rate. The log-transmission rate is modelled as a latent Ornstein-Uhlenbeck (OU) process with exact Gaussian discrete-time transitions. Inference is performed via the nested particle filter (NPF) of Crisan and Miguez (2018) <doi:10.3150/17-BEJ954>, which maintains an outer particle layer over the OU hyperparameters and, for each outer particle, an inner bootstrap filter over epidemic states. The Cori-style renewal-equation estimator follows Cori et al. (2013) <doi:10.1093/aje/kwt133>. The package also provides utilities for simulation, posterior summarisation, and forecasting.

This package provides utility functions and custom probability distribution for Bayesian analyses of radiocarbon dates within the nimble modelling framework. It includes various population growth models, nimbleFunction objects, as well as a suite of functions for prior and posterior predictive checks for demographic inference (Crema and Shoda (2021) <doi:10.1371/journal.pone.0251695>) and other analyses.

Represent network or igraph objects whose vertices can be represented by features in an sf object as a network graph surmising a sf plot. Fits into ggplot2 grammar.

The Bayesian hierarchical model named antigen-T cell interaction estimation is to estimate the history of the immune pressure on the evolution of the tumor clones.The model is based on the estimation result from Andrew Roth (2014) <doi:10.1038/nmeth.2883>.

Fits regularization paths for linear regression, GLM, and Cox regression models using lasso or nonconvex penalties, in particular the minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) penalty, with options for additional L2 penalties (the "elastic net" idea). Utilities for carrying out cross-validation as well as post-fitting visualization, summarization, inference, and prediction are also provided. For more information, see Breheny and Huang (2011) <doi:10.1214/10-AOAS388> or visit the ncvreg homepage <https://pbreheny.github.io/ncvreg/>.

An interactive document on the topic of naive Bayes classification analysis using rmarkdown and shiny packages. Runtime examples are provided in the package function as well as at <https://kartikeyab.shinyapps.io/NBShiny/>.

This package provides functions for normalizing psychometric test scores. The normalization aims at correcting the metrological properties of the psychometric tests such as the ceiling and floor effects and the curvilinearity (unequal interval scaling). Functions to compute and plot predictions in the natural scale of the psychometric test from the estimates of a linear mixed model estimated on the normalized scores are also provided. See Philipps et al (2014) <doi:10.1159/000365637> for details.

In semi-structured interviews that use the framework method, it is not always clear how refinements to interview questions affect the decision of when to stop interviews. The trend of novel and duplicate interview codes (novel codes are information that other interviewees have not previously mentioned) provides insight into the richness of qualitative information. This package provides tools to visualise when refinements occur and how that affects the trends of novel and duplicate codes. These visualisations, when used progressively as new interviews are finished, can help the researcher to decide on a stopping point for their interviews. For context, see Wong et al., (2023) <doi:10.1177/16094069231220773>.

This package implements the framework presented in Cucci, D. A., Voirol, L., Khaghani, M. and Guerrier, S. (2023) <doi:10.1109/TIM.2023.3267360> which allows to analyze the impact of sensor error modeling on the performance of integrated navigation (sensor fusion) based on inertial measurement unit (IMU), Global Positioning System (GPS), and barometer data. The framework relies on Monte Carlo simulations in which a Vanilla Extended Kalman filter is coupled with realistic and user-configurable noise generation mechanisms to recover a reference trajectory from noisy measurements. The evaluation of several statistical metrics of the solution, aggregated over hundreds of simulated realizations, provides reasonable estimates of the expected performances of the system in real-world conditions.

This package provides tools for traversing and working with National Hydrography Dataset Plus (NHDPlus) data. All methods implemented in nhdplusTools are available in the NHDPlus documentation available from the US Environmental Protection Agency <https://www.epa.gov/waterdata/basic-information>.

Datasets for nlmixr2 and rxode2'. nlmixr2 is used for fitting and comparing 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>).

Enable programmatic interaction with Notion pages, databases, blocks, comments, and users through the Notion API <https://developers.notion.com/>. Provides both synchronous and asynchronous client interfaces for building workflows and automations that integrate with Notion workspaces. Supports all Notion API endpoints including content creation, data retrieval, and workspace management.

NONMEM has been a tool for running nonlinear mixed effects models since the 80s and is still used today (Bauer 2019 <doi:10.1002/psp4.12404>). This tool allows you to convert NONMEM models to rxode2 (Wang, Hallow and James (2016) <doi:10.1002/psp4.12052>) and with simple models nlmixr2 syntax (Fidler et al (2019) <doi:10.1002/psp4.12445>). The nlmixr2 syntax requires the residual specification to be included and it is not always translated. If available, the rxode2 model will read in the NONMEM data and compare the simulation for the population model ('PRED') individual model ('IPRED') and residual model ('IWRES') to immediately show how well the translation is performing. This saves the model development time for people who are creating an rxode2 model manually. Additionally, this package reads in all the information to allow simulation with uncertainty (that is the number of observations, the number of subjects, and the covariance matrix) with a rxode2 model. This is complementary to the babelmixr2 package that translates nlmixr2 models to NONMEM and can convert the objects converted from nonmem2rx to a full nlmixr2 fit.

This package provides tools to support research on vowel covariation. Methods are provided to support Principal Component Analysis workflows (as in Brand et al. (2021) <doi:10.1016/j.wocn.2021.101096> and Wilson Black et al. (2023) <doi:10.1515/lingvan-2022-0086>).

This package provides a model library for nlmixr2'. The models include (and plan to include) pharmacokinetic, pharmacodynamic, and disease models used in pharmacometrics. Where applicable, references for each model are included in the meta-data for each individual model. The package also includes model composition and modification functions to make model updates easier.

Designed to be compatible with the R package DBI (Database Interface) when connecting to Amazon Web Service ('AWS') Athena <https://aws.amazon.com/athena/>. To do this the R AWS Software Development Kit ('SDK') paws <https://github.com/paws-r/paws> is used as a driver.

Wald Test for nonlinear restrictions on model parameters and confidence intervals for nonlinear functions of parameters using delta-method. Applicable after ANY model, provided parameters estimates and their covariance matrix are available.

Nonparametric (and semiparametric) kernel methods that seamlessly handle a mix of continuous, unordered, and ordered factor data types. This package is a parallel implementation of the np package based on the MPI specification that incorporates the Rmpi package (Hao Yu <hyu@stats.uwo.ca>) with minor modifications and we are extremely grateful to Hao Yu for his contributions to the R community. We would like to gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada (NSERC, <https://www.nserc-crsng.gc.ca/>), the Social Sciences and Humanities Research Council of Canada (SSHRC, <https://www.sshrc-crsh.gc.ca/>), and the Shared Hierarchical Academic Research Computing Network (SHARCNET, <https://sharcnet.ca/>). We would also like to acknowledge the contributions of the GNU GSL authors. In particular, we adapt the GNU GSL B-spline routine gsl_bspline.c adding automated support for quantile knots (in addition to uniform knots), providing missing functionality for derivatives, and for extending the splines beyond their endpoints.

Derives the most frequent hierarchies along with their probability of occurrence. One can also define complex hierarchy criteria and calculate their probability. Methodology based on Papakonstantinou et al. (2021) <DOI:10.21203/rs.3.rs-858140/v1>.

Search, preview, and download datasets from the National Health and Nutrition Examination Survey (NHANES) across survey cycles. The package provides functions to identify relevant datasets by keyword, inspect available .XPT files before downloading, and organize retrieved data locally. Data are retrieved from the NHANES web services available at <https://wwwn.cdc.gov/nchs/nhanes/> .

Many tools for calculating network, node, or tie marks, measures, motifs and memberships of many different types of networks. Marks identify structural positions, measures quantify network properties, memberships classify nodes into groups, and motifs tabulate substructure participation. All functions operate with all classes of network data covered in manynet', and on directed, undirected, multiplex, multimodal, signed, and other networks.

Framework is devoted to mining numerical association rules through the utilization of nature-inspired algorithms for optimization. Drawing inspiration from the NiaARM Python and the NiaARM Julia packages, this repository introduces the capability to perform numerical association rule mining in the R programming language. Fister Jr., Iglesias, Galvez, Del Ser, Osaba and Fister (2018) <doi:10.1007/978-3-030-03493-1_9>.

Variational Expectation-Maximization algorithm to fit the noisy stochastic block model to an observed dense graph and to perform a node clustering. Moreover, a graph inference procedure to recover the underlying binary graph. This procedure comes with a control of the false discovery rate. The method is described in the article "Powerful graph inference with false discovery rate control" by T. Rebafka, E. Roquain, F. Villers (2020) <arXiv:1907.10176>.