This package provides functions for bipartite network rewiring through N consecutive switching steps and for the computation of the minimal number of switching steps to be performed in order to maximise the dissimilarity with respect to the original network. It includes functions for the analysis of the introduced randomness across the switching steps and several other routines to analyse the resulting networks and their natural projections.

This package provides a a transcriptomic-based framework to dissect cell communication in a global manner. It integrates an original expert-curated database of ligand-receptor interactions taking into account multiple subunits expression. Based on transcriptomic profiles (gene expression), this package computes communication scores between cells and provides several visualization modes that can be helpful to dig into cell-cell interaction mechanism and extend biological knowledge.

Fast and automatic gradient tree boosting designed to avoid manual tuning and cross-validation by utilizing an information theoretic approach. This makes the algorithm adaptive to the dataset at hand; it is completely automatic, and with minimal worries of overfitting. Consequently, the speed-ups relative to state-of-the-art implementations can be in the thousands while mathematical and technical knowledge required on the user are minimized.

Fits linear or generalized linear regression models using Bayesian global-local shrinkage prior hierarchies as described in Polson and Scott (2010) <doi:10.1093/acprof:oso/9780199694587.003.0017>. Provides an efficient implementation of ridge, lasso, horseshoe and horseshoe+ regression with logistic, Gaussian, Laplace, Student-t, Poisson or geometric distributed targets using the algorithms summarized in Makalic and Schmidt (2016) <doi:10.48550/arXiv.1611.06649>.

This package implements methods for Bayesian analysis of State Space Models. Includes implementations of the Particle Marginal Metropolis-Hastings algorithm described in Andrieu et al. (2010) <doi:10.1111/j.1467-9868.2009.00736.x> and automatic tuning inspired by Pitt et al. (2012) <doi:10.1016/j.jeconom.2012.06.004> and J. Dahlin and T. B. Schön (2019) <doi:10.18637/jss.v088.c02>.

This package provides tools for penalized estimation of flexible hidden Markov models for time series of counts w/o the need to specify a (parametric) family of distributions. These include functions for model fitting, model checking, and state decoding. For details, see Adam, T., Langrock, R., and Weià , C.H. (2019): Penalized Estimation of Flexible Hidden Markov Models for Time Series of Counts. <arXiv:1901.03275>.

Calculate the confidence interval and p value for change in C-statistic. The adjusted C-statistic is calculated by using formula as "Somers Dxy rank correlation"/2+0.5. The confidence interval was calculated by using the bootstrap method. The p value was calculated by using the Z testing method. Please refer to the article of Peter Ganz et al. (2016) <doi:10.1001/jama.2016.5951>.

This package provides a suite of routines for Clifford algebras, using the Map class of the Standard Template Library. Canonical reference: Hestenes (1987, ISBN 90-277-1673-0, "Clifford algebra to geometric calculus"). Special cases including Lorentz transforms, quaternion multiplication, and Grassmann algebra, are discussed. Vignettes presenting conformal geometric algebra, quaternions and split quaternions, dual numbers, and Lorentz transforms are included. The package follows disordR discipline.

This creates code names that a user can consider for their organizations, their projects, themselves, people in their organizations or projects, or whatever else. The user can also supply a numeric seed (and even a character seed) for maximum reproducibility. Use is simple and the code names produced come in various types too, contingent on what the user may be desiring as a code name or nickname.

Fits a variety of cure models using excess hazard modeling methodology such as the mixture model proposed by Phillips et al. (2002) <doi:10.1002/sim.1101> The Weibull distribution is used to represent the survival function of the uncured patients; Fits also non-mixture cure model such as the time-to-null excess hazard model proposed by Boussari et al. (2020) <doi:10.1111/biom.13361>.

This package provides a series of functions which aid in both simulating and determining the properties of finite, discrete-time, discrete state markov chains. Two functions (DTMC, MultDTMC) produce n iterations of a Markov Chain(s) based on transition probabilities and an initial distribution. The function FPTime determines the first passage time into each state. The function statdistr determines the stationary distribution of a Markov Chain.

Motifs within biological sequences show a significant role. This package utilizes a user-defined threshold value (window size and similarity) to create consensus segments or motifs through local alignment of dynamic programming with gap and it calculates the frequency of each identified motif, offering a detailed view of their prevalence within the dataset. It allows for thorough exploration and understanding of sequence patterns and their biological importance.

Calculates the (approximate) effective number of clusters for a regression model, as described in Carter, Schnepel, and Steigerwald (2017) <doi:10.1162/REST_a_00639>. The effective number of clusters is a statistic to assess the reliability of asymptotic inference when sampling or treatment assignment is clustered. Methods are implemented for stats::lm(), plm::plm(), and fixest::feols(). There is also a formula method.

This package contains elementary tools for analysis of common epidemiological problems, ranging from sample size estimation, through 2x2 contingency table analysis and basic measures of agreement (kappa, sensitivity/specificity). Appropriate print and summary statements are also written to facilitate interpretation wherever possible. Source code is commented throughout to facilitate modification. The target audience includes advanced undergraduate and graduate students in epidemiology or biostatistics courses, and clinical researchers.

It is important to ensure that sensitive data is protected. This straightforward package is aimed at the end-user. Strong RSA encryption using a public/private key pair is used to encrypt data frame or tibble columns. A public key can be shared to allow others to encrypt data to be sent to you. This is particularly aimed a healthcare settings so patient data can be pseudonymised.

This package provides tools for fitting statistical network models to dynamic network data. Can be used for fitting both dynamic network actor models ('DyNAMs') and relational event models ('REMs'). Stadtfeld, Hollway, and Block (2017a) <doi:10.1177/0081175017709295>, Stadtfeld, Hollway, and Block (2017b) <doi:10.1177/0081175017733457>, Stadtfeld and Block (2017) <doi:10.15195/v4.a14>, Hoffman et al. (2020) <doi:10.1017/nws.2020.3>.

Vapor pressure, relative humidity, absolute humidity, specific humidity, and mixing ratio are commonly used water vapor measures in meteorology. This R package provides functions for calculating saturation vapor pressure (hPa), partial water vapor pressure (Pa), relative humidity (%), absolute humidity (kg/m^3), specific humidity (kg/kg), and mixing ratio (kg/kg) from temperature (K) and dew point (K). Conversion functions between humidity measures are also provided.

This package provides a system for identifying diseases or events from healthcare databases and preparing data for epidemiological studies. It includes capabilities not supported by SQL', such as matching strings by stringr style regular expressions, and can compute comorbidity scores (Quan et al. (2005) <doi:10.1097/01.mlr.0000182534.19832.83>) directly on a database server. The implementation is based on dbplyr with full tidyverse compatibility.

Two pipelines are provided to study microbial turnover along a gradient, including the beta diversity and microbial abundance change. The betaturn class consists of the steps of community dissimilarity matrix generation, matrix conversion, differential test and visualization. The workflow of taxaturn class includes the taxonomic abundance calculation, abundance transformation, abundance change summary, statistical analysis and visualization. Multiple statistical approaches can contribute to the analysis of microbial turnover.

Shiny web application to run meta-analyses. Essentially a graphical front-end to package meta for R. Can be useful as an educational tool, and for quickly analyzing and sharing meta-analyses. Provides output to quickly fill in GRADE (Grading of Recommendations, Assessment, Development and Evaluations) Summary-of-Findings tables. Importantly, it allows further processing of the results inside R, in case more specific analyses are needed.

Package for processing downloaded MODIS Surface reflectance Product HDF files. Specifically, MOD09 surface reflectance product files, and the associated MOD03 geolocation files (for MODIS-TERRA). The package will be most effective if the user installs MRTSwath (MODIS Reprojection Tool for swath products; <https://lpdaac.usgs.gov/tools/modis_reprojection_tool_swath>, and adds the directory with the MRTSwath executable to the default R PATH by editing ~/.Rprofile.

This package provides an interface to connect R with the <https://github.com/IDEMSInternational/open-app-builder> OpenAppBuilder platform, enabling users to retrieve and work with user and notification data for analysis and processing. It is designed for developers and analysts to seamlessly integrate data from OpenAppBuilder into R workflows via a Postgres database connection, allowing direct querying and import of app data into R.

Toolkit for fitting point process models with sequences of LASSO penalties ("regularisation paths"), as described in Renner, I.W. and Warton, D.I. (2013) <doi:10.1111/j.1541-0420.2012.01824.x>. Regularisation paths of Poisson point process models or area-interaction models can be fitted with LASSO, adaptive LASSO or elastic net penalties. A number of criteria are available to judge the bias-variance tradeoff.

This package provides predictive accuracy tools to evaluate time-to-event survival models. This includes calculating the concordance probability estimate that incorporates the follow-up time for a particular study developed by Devlin, Gonen, Heller (2020)<doi:10.1007/s10985-020-09503-3>. It also evaluates the concordance probability estimate for nested Cox proportional hazards models using a projection-based approach by Heller and Devlin (under review).