This package provides a framework for simulating spatially explicit genomic data which leverages real cartographic information for programmatic and visual encoding of spatiotemporal population dynamics on real geographic landscapes. Population genetic models are then automatically executed by the SLiM software by Haller et al. (2019) <doi:10.1093/molbev/msy228> behind the scenes, using a custom built-in simulation SLiM script. Additionally, fully abstract spatial models not tied to a specific geographic location are supported, and users can also simulate data from standard, non-spatial, random-mating models. These can be simulated either with the SLiM built-in back-end script, or using an efficient coalescent population genetics simulator msprime by Baumdicker et al. (2022) <doi:10.1093/genetics/iyab229> with a custom-built Python script bundled with the R package. Simulated genomic data is saved in a tree-sequence format and can be loaded, manipulated, and summarised using tree-sequence functionality via an R interface to the Python module tskit by Kelleher et al. (2019) <doi:10.1038/s41588-019-0483-y>. Complete model configuration, simulation and analysis pipelines can be therefore constructed without a need to leave the R environment, eliminating friction between disparate tools for population genetic simulations and data analysis.

Calculates and plots the SiZer map for scatterplot data. A SiZer map is a way of examining when the p-th derivative of a scatterplot-smoother is significantly negative, possibly zero or significantly positive across a range of smoothing bandwidths.

Statistical methods for analyzing case-control point data. Methods include the ratio of kernel densities, the difference in K Functions, the spatial scan statistic, and q nearest neighbors of cases.

Code for describing and manipulating scuba diving profiles (depth-time curves) and decompression models, for calculating the predictions of decompression models, for calculating maximum no-decompression time and decompression tables, and for performing mixed gas calculations.

Fits (excess) hazard, relative mortality ratio or marginal intensity models with multidimensional penalized splines allowing for time-dependent effects, non-linear effects and interactions between several continuous covariates. In survival and net survival analysis, in addition to modelling the effect of time (via the baseline hazard), one has often to deal with several continuous covariates and model their functional forms, their time-dependent effects, and their interactions. Model specification becomes therefore a complex problem and penalized regression splines represent an appealing solution to that problem as splines offer the required flexibility while penalization limits overfitting issues. Current implementations of penalized survival models can be slow or unstable and sometimes lack some key features like taking into account expected mortality to provide net survival and excess hazard estimates. In contrast, survPen provides an automated, fast, and stable implementation (thanks to explicit calculation of the derivatives of the likelihood) and offers a unified framework for multidimensional penalized hazard and excess hazard models. Later versions (>2.0.0) include penalized models for relative mortality ratio, and marginal intensity in recurrent event setting. survPen may be of interest to those who 1) analyse any kind of time-to-event data: mortality, disease relapse, machinery breakdown, unemployment, etc 2) wish to describe the associated hazard and to understand which predictors impact its dynamics, 3) wish to model the relative mortality ratio between a cohort and a reference population, 4) wish to describe the marginal intensity for recurrent event data. See Fauvernier et al. (2019a) <doi:10.21105/joss.01434> for an overview of the package and Fauvernier et al. (2019b) <doi:10.1111/rssc.12368> for the method.

Calculate and compare lower confidence bounds for binomial series system reliability. The R shiny application, launched by the function launch_app(), weaves together a workflow of customized simulations and delta coverage calculations to output recommended lower confidence bound methods.

This package provides a flexible tool for simulating complex longitudinal data using structural equations, with emphasis on problems in causal inference. Specify interventions and simulate from intervened data generating distributions. Define and evaluate treatment-specific means, the average treatment effects and coefficients from working marginal structural models. User interface designed to facilitate the conduct of transparent and reproducible simulation studies, and allows concise expression of complex functional dependencies for a large number of time-varying nodes. See the package vignette for more information, documentation and examples.

This package provides a facility to generate sliced (orthogonal) Latin hypercube designs with four and five slices. For details about sliced and orthogonal Latin hypercube designs, see Yang, J. F., Lin, C. D., Qian, P. Z., and Lin, D. K. (2013). "Construction of sliced orthogonal Latin hypercube designs". Statistica Sinica, 1117-1130, <doi:10.5705/ss.2012.037>.

This package implements the S-type estimators, novel robust estimators for general linear regression models, addressing challenges such as outlier contamination and leverage points. This package introduces robust regression techniques to provide a robust alternative to classical methods and includes diagnostic tools for assessing model fit and performance. The methodology is based on the study, "Comparison of the Robust Methods in the General Linear Regression Model" by Sazak and Mutlu (2023). This package is designed for statisticians and applied researchers seeking advanced tools for robust regression analysis.

This package provides functions for fitting semiparametric regression models for panel count survival data. An overview of the package can be found in Wang and Yan (2011) <doi:10.1016/j.cmpb.2010.10.005> and Chiou et al. (2018) <doi:10.1111/insr.12271>.

Efficient R package for latent class analysis of recurrent events, based on the semiparametric multiplicative intensity model by Zhao et al. (2022) <doi:10.1111/rssb.12499>. SLCARE returns estimates for non-functional model parameters along with the associated variance estimates and p-values. Visualization tools are provided to depict the estimated functional model parameters and related functional quantities of interest. SLCARE also delivers a model checking plot to help assess the adequacy of the fitted model.

This package provides tools for simulating spatially dependent predictors (continuous or binary), which are used to generate scalar outcomes in a (generalized) linear model framework. Continuous predictors are generated using traditional multivariate normal distributions or Gauss Markov random fields with several correlation function approaches (e.g., see Rue (2001) <doi:10.1111/1467-9868.00288> and Furrer and Sain (2010) <doi:10.18637/jss.v036.i10>), while binary predictors are generated using a Boolean model (see Cressie and Wikle (2011, ISBN: 978-0-471-69274-4)). Parameter vectors exhibiting spatial clustering can also be easily specified by the user.

This package provides a simple to use summary function that can be used with pipes and displays nicely in the console. The default summary statistics may be modified by the user as can the default formatting. Support for data frames and vectors is included, and users can implement their own skim methods for specific object types as described in a vignette. Default summaries include support for inline spark graphs. Instructions for managing these on specific operating systems are given in the "Using skimr" vignette and the README.

This package contains a suite of functions for survival analysis in health economics. These can be used to run survival models under a frequentist (based on maximum likelihood) or a Bayesian approach (both based on Integrated Nested Laplace Approximation or Hamiltonian Monte Carlo). To run the Bayesian models, the user needs to install additional modules (packages), i.e. survHEinla and survHEhmc'. These can be installed from <https://giabaio.r-universe.dev/> using install.packages("survHEhmc", repos = c("https://giabaio.r-universe.dev", "https://cloud.r-project.org")) and install.packages("survHEinla", repos = c("https://giabaio.r-universe.dev", "https://cloud.r-project.org")) respectively. survHEinla is based on the package INLA, which is available for download at <https://inla.r-inla-download.org/R/stable/>. The user can specify a set of parametric models using a common notation and select the preferred mode of inference. The results can also be post-processed to produce probabilistic sensitivity analysis and can be used to export the output to an Excel file (e.g. for a Markov model, as often done by modellers and practitioners). <doi:10.18637/jss.v095.i14>.

This package provides elastic net penalized maximum likelihood estimator for structural equation models (SEM). The package implements `lasso` and `elastic net` (l1/l2) penalized SEM and estimates the model parameters with an efficient block coordinate ascent algorithm that maximizes the penalized likelihood of the SEM. Hyperparameters are inferred from cross-validation (CV). A Stability Selection (STS) function is also available to provide accurate causal effect selection. The software achieves high accuracy performance through a `Network Generative Pre-trained Transformer` (Network GPT) Framework with two steps: 1) pre-trains the model to generate a complete (fully connected) graph; and 2) uses the complete graph as the initial state to fit the `elastic net` penalized SEM.

Stepwise models for the optimal linear combination of continuous variables in binary classification problems under Youden Index optimisation. Information on the models implemented can be found at Aznar-Gimeno et al. (2021) <doi:10.3390/math9192497>.

This package provides a set of user interface components to create outstanding shiny apps <https://shiny.posit.co/>, with the power of React JavaScript <https://react.dev/>. Seamlessly support dark and light themes, customize CSS with tailwind <https://tailwindcss.com/>.

Secure handling of API keys can be difficult. This package provides secure convenience functions for entering / handling API keys and opening connections via inversion of control on those keys. Works seamlessly between production and developer environments.

This package provides interface to the Spectator Earth API <https://api.spectator.earth/>, mainly for obtaining the acquisition plans and satellite overpasses for Sentinel-1, Sentinel-2, Landsat-8 and Landsat-9 satellites. Current position and trajectory can also be obtained for a much larger set of satellites. It is also possible to search the archive for available images over the area of interest for a given (past) period, get the URL links to download the whole image tiles, or alternatively to download the image for just the area of interest based on selected spectral bands.

Compare performance between different versions of a shiny application based on git references.

Download, navigate and analyse the Student-Life dataset. The Student-Life dataset contains passive and automatic sensing data from the phones of a class of 48 Dartmouth college students. It was collected over a 10 week term. Additionally, the dataset contains ecological momentary assessment results along with pre-study and post-study mental health surveys. The intended use is to assess mental health, academic performance and behavioral trends. The raw dataset and additional information is available at <https://studentlife.cs.dartmouth.edu/>.

The estimation method proposed by Chen and Yi (2021) <doi:10.1111/biom.13331> is extended to the analysis of survival data, accommodating commonly used survival models while accounting for measurement error and network structures among covariates.

Create sampling designs using the surface reconstruction algorithm. Original method by: Olsson, D. 2002. A method to optimize soil sampling from ancillary data. Poster presenterad at: NJF seminar no. 336, Implementation of Precision Farming in Practical Agriculture, 10-12 June 2002, Skara, Sweden.

Apache Drill is a low-latency distributed query engine designed to enable data exploration and analysis on both relational and non-relational data stores, scaling to petabytes of data. Methods are provided that enable working with Apache Drill instances via the REST API, DBI methods and using dplyr'/'dbplyr idioms. Helper functions are included to facilitate using official Drill Docker images/containers.