Emulate MATLAB code using R'.

The maybe type represents the possibility of some value or nothing. It is often used instead of throwing an error or returning `NULL`. The advantage of using a maybe type over `NULL` is that it is both composable and requires the developer to explicitly acknowledge the potential absence of a value, helping to avoid the existence of unexpected behaviour.

This algorithm provides a numerical solution to the problem of unconstrained local minimization (or maximization). It is particularly suited for complex problems and more efficient than the Gauss-Newton-like algorithm when starting from points very far from the final minimum (or maximum). Each iteration is parallelized and convergence relies on a stringent stopping criterion based on the first and second derivatives. See Philipps et al, 2021 <doi:10.32614/RJ-2021-089>.

Local recombination rates are graphically estimated across a genome using Marey maps.

The iterative procedure estimates structural changes in the success probability of Bernoulli variables. It estimates the number and location of the breakpoints as well as the success probability of the different sequences between the breakpoints. In addition, it provides a graphical illustration of the result.

Model fitting, sampling and visualization for the (Hidden) Markov Random Field model with pairwise interactions and general interaction structure from Freguglia, Garcia & Bicas (2020) <doi:10.1002/env.2613>, which has many popular models used in 2-dimensional lattices as particular cases, like the Ising Model and Potts Model. A complete manuscript describing the package is available in Freguglia & Garcia (2022) <doi:10.18637/jss.v101.i08>.

The Molecular Signatures Database ('MSigDB') is one of the most widely used and comprehensive databases of gene sets for performing gene set enrichment analysis <doi:10.1016/j.cels.2015.12.004>. The msig package provides you with powerful, easy-to-use and flexible query functions for the MsigDB database. There are 2 query modes in the msig package: online query and local query. Both queries contain 2 steps: gene set name and gene. The online search is divided into 2 modes: registered search and non-registered browse. For registered search, email that you registered should be provided. Local queries can be made from local database, which can be updated by msig_update() function.

Bayesian inference analysis for bivariate meta-analysis of diagnostic test studies using integrated nested Laplace approximation with INLA. A purpose built graphic user interface is available. The installation of R package INLA is compulsory for successful usage. The INLA package can be obtained from <https://www.r-inla.org>. We recommend the testing version, which can be downloaded by running: install.packages("INLA", repos=c(getOption("repos"), INLA="https://inla.r-inla-download.org/R/testing"), dep=TRUE).

Find common entities detected in both positive and negative ionization mode, delete this entity in the less sensible mode and combine both matrices.

Model stability and variable inclusion plots [Mueller and Welsh (2010, <doi:10.1111/j.1751-5823.2010.00108.x>); Murray, Heritier and Mueller (2013, <doi:10.1002/sim.5855>)] as well as the adaptive fence [Jiang et al. (2008, <doi:10.1214/07-AOS517>); Jiang et al. (2009, <doi:10.1016/j.spl.2008.10.014>)] for linear and generalised linear models.

When choosing proper variable selection methods, it is important to consider the uncertainty of a certain method. The model confidence bound for variable selection identifies two nested models (upper and lower confidence bound models) containing the true model at a given confidence level. A good variable selection method is the one of which the model confidence bound under a certain confidence level has the shortest width. When visualizing the variability of model selection and comparing different model selection procedures, model uncertainty curve is a good graphical tool. A good variable selection method is the one of whose model uncertainty curve will tend to arch towards the upper left corner. This function aims to obtain the model confidence bound and draw the model uncertainty curve of certain single model selection method under a coverage rate equal or little higher than user-given confidential level. About what model confidence bound is and how it work please see Li,Y., Luo,Y., Ferrari,D., Hu,X. and Qin,Y. (2019) Model Confidence Bounds for Variable Selection. Biometrics, 75:392-403. <DOI:10.1111/biom.13024>. Besides, flare is needed only you apply the SQRT or LAD method ('mcb totally has 8 methods). Although flare has been archived by CRAN, you can still get it in <https://CRAN.R-project.org/package=flare> and the latest version is useful for mcb'.

This package provides tools necessary to reconstruct clonal affiliations from temporally and/or spatially separated measurements of viral integration sites. For this means it utilizes correlations present in the relative readouts of the integration sites. Furthermore, facilities for filtering of the data and visualization of different steps in the pipeline are provided with the package.

Basic functions for microbial sequence data analysis. The idea is to use generic R data structures as much as possible, making R data wrangling possible also for sequence data.

Interface to Apache Commons Email to send emails from R.

An ensemble meta-prediction framework to integrate multiple regression models into a current study. Gu, T., Taylor, J.M.G. and Mukherjee, B. (2020) <arXiv:2010.09971>. A meta-analysis framework along with two weighted estimators as the ensemble of empirical Bayes estimators, which combines the estimates from the different external models. The proposed framework is flexible and robust in the ways that (i) it is capable of incorporating external models that use a slightly different set of covariates; (ii) it is able to identify the most relevant external information and diminish the influence of information that is less compatible with the internal data; and (iii) it nicely balances the bias-variance trade-off while preserving the most efficiency gain. The proposed estimators are more efficient than the naive analysis of the internal data and other naive combinations of external estimators.

This package provides the facility to calculate non-isotropic accumulated cost surface, least-cost paths, least-cost corridors, least-cost networks using a number of human-movement-related cost functions that can be selected by the user. It just requires a Digital Terrain Model, a start location and (optionally) destination locations. See Alberti (2019) <doi:10.1016/j.softx.2019.100331>.

MedDRA data is used for defining adverse events in clinical studies. You can load and merge the data for use in categorizing the adverse events using this package. The package requires the data licensed from MedDRA <https://www.meddra.org/>.

These guidelines are meant to provide a pragmatic, yet rigorous, help to drug developers and decision makers, since they are shaped by three fundamental ingredients: the clinically determined margin of detriment on OS that is unacceptably high (delta null); the benefit on OS that is plausible given the mechanism of action of the novel intervention (delta alt); and the quantity of information (i.e. survival events) it is feasible to accrue given the clinical and drug development setting. The proposed guidelines facilitate transparent discussions between stakeholders focusing on the risks of erroneous decisions and what might be an acceptable trade-off between power and the false positive error rate.

Monolix is a tool for running mixed effects model using saem'. This tool allows you to convert Monolix models to rxode2 (Wang, Hallow and James (2016) <doi:10.1002/psp4.12052>) using the form compatible with nlmixr2 (Fidler et al (2019) <doi:10.1002/psp4.12445>). If available, the rxode2 model will read in the Monolix data and compare the simulation for the population model individual model and residual model 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 Monolix and can convert the objects converted from monolix2rx to a full nlmixr2 fit. While not required, you can get/install the lixoftConnectors package in the Monolix installation, as described at the following url <https://monolixsuite.slp-software.com/r-functions/2024R1/installation-and-initialization>. When lixoftConnectors is available, Monolix can be used to load its model library instead manually setting up text files (which only works with old versions of Monolix').

Integrates fairness auditing and bias mitigation methods for the mlr3 ecosystem. This includes fairness metrics, reporting tools, visualizations and bias mitigation techniques such as "Reweighing" described in Kamiran, Calders (2012) <doi:10.1007/s10115-011-0463-8> and "Equalized Odds" described in Hardt et al. (2016) <https://papers.nips.cc/paper/2016/file/9d2682367c3935defcb1f9e247a97c0d-Paper.pdf>. Integration with mlr3 allows for auditing of ML models as well as convenient joint tuning of machine learning algorithms and debiasing methods.

This package provides an R wrapper for the MD4C (Markdown for C') library. Functions exist for parsing markdown ('CommonMark compliant) along with support for other common markdown extensions (e.g. GitHub flavored markdown, LaTeX equation support, etc.). The package also provides a number of higher level functions for exploring and manipulating markdown abstract syntax trees as well as translating and displaying the documents.

Simulate a (bivariate) multivariate renewal Hawkes (MRHawkes) self-exciting process, with given immigrant hazard rate functions and offspring density function. Calculate the likelihood of a MRHawkes process with given hazard rate functions and offspring density function for an (increasing) sequence of event times. Calculate the Rosenblatt residuals of the event times. Predict future event times based on observed event times up to a given time. For details see Stindl and Chen (2018) <doi:10.1016/j.csda.2018.01.021>.

Clean the MS/MS spectrum, calculate spectral entropy, unweighted entropy similarity, and entropy similarity for mass spectrometry data. The entropy similarity is a novel similarity measure for MS/MS spectra which outperform the widely used dot product similarity in compound identification. For more details, please refer to the paper: Yuanyue Li et al. (2021) "Spectral entropy outperforms MS/MS dot product similarity for small-molecule compound identification" <doi:10.1038/s41592-021-01331-z>.

This package provides one function, which is a wrapper around purrr::map() with some extras on top, including parallel computation, progress bars, error handling, and result caching.