This package creates nomogram visualizations for penalized Cox regression models, with the support of reproducible survival model building, validation, calibration, and comparison for high-dimensional data.

The log4r package is meant to provide a fast, lightweight, object-oriented approach to logging in R based on the widely-emulated log4j system and etymology.

This package provides functions to estimate the intensity function and its derivative of a given order of a multiplicative counting process using the local polynomial method.

Visualize the relationship between linear regression variables and causes of multi-collinearity. Implements the method in Lin et. al. (2020) <doi:10.1080/10618600.2020.1779729>.

The Mass Transportation Distance rank histogram was developed to assess the reliability of scenarios with equal or different probabilities of occurrence <doi:10.1002/we.1872>.

Improved methods to construct prediction intervals for network meta-analysis. The parametric bootstrap and Kenward-Roger-type adjustment by Noma et al. (2022) <forthcoming> are implementable.

This package provides a toolbox for writing knitr', Sweave or other LaTeX'- or markdown'-based reports and to prettify the output of various estimated models.

This package provides a seamless design that combines phase I dose escalation based on toxicity with phase II dose expansion and dose comparison based on efficacy.

This package provides function to apply "Subgroup Identification based on Differential Effect Search" (SIDES) method proposed by Lipkovich et al. (2011) <doi:10.1002/sim.4289>.

This is a simple addin to RStudio that finds all TODO', FIX ME', CHANGED etc. comments in your project and shows them as a markers list.

This package provides tools for voice analysis, speaker recognition and mood inference. Gathers R and Python tools to solve problems concerning voice and audio in general.

This package provides functions for the mass-univariate voxelwise analysis of medical imaging data that follows the NIfTI <http://nifti.nimh.nih.gov> format.

This package provides computational support for flow over weirs, such as sharp-crested, broad-crested, and embankments. Initially, the package supports broad- and sharp-crested weirs.

This package provides functions for calculating the fetch (length of open water distance along given directions) and estimating wave energy from wind and wave monitoring data.

This package calculates probabilistic pathway scores using gene expression data. Gene expression values are aggregated into pathway-based scores using Bayesian network representations of biological pathways.

This package ofers functions for importation, normalization, visualization, and quality control to correct identified sources of variability in array of CGH experiments.

This package implements synchronization between R processes (spawned by using the parallel package for instance) using file locks. It supports both exclusive and shared locking.

This package provides implementations of a family of Lasso variants including Dantzig Selector, LAD Lasso, SQRT Lasso, Lq Lasso for estimating high dimensional sparse linear models.

It is sometimes useful to perform a computation in a separate R process, without affecting the current R process at all. This package does exactly that.

This package provides improved predictive models by indirect classification and bagging for classification, regression and survival problems as well as resampling based estimators of prediction error.

This package performs penalized quantile regression with LASSO, elastic net, SCAD and MCP penalty functions including group penalties. In addition, offers a group penalty that provides consistent variable selection across quantiles. Provides a function that automatically generates lambdas and evaluates different models with cross validation or BIC, including a large p version of BIC. Below URL provides a link to a work in progress vignette.

Maximum likelihood estimation for univariate reducible stochastic differential equation models. Discrete, possibly noisy observations, not necessarily evenly spaced in time. Can fit multiple individuals/units with global and local parameters, by fixed-effects or mixed-effects methods. Ref.: Garcia, O. (2019) "Estimating reducible stochastic differential equations by conversion to a least-squares problem", Computational Statistics 34(1): 23-46, <doi:10.1007/s00180-018-0837-4>.

This package contains functions for simulating the linear fractional stable motion according to the algorithm developed by Mazur and Otryakhin <doi:10.32614/RJ-2020-008> based on the method from Stoev and Taqqu (2004) <doi:10.1142/S0218348X04002379>, as well as functions for estimation of parameters of these processes introduced by Mazur, Otryakhin and Podolskij (2018) <arXiv:1802.06373>, and also different related quantities.

An R interface to Weka (Version 3.9.3). Weka is a collection of machine learning algorithms for data mining tasks written in Java, containing tools for data pre-processing, classification, regression, clustering, association rules, and visualization. Package RWeka contains the interface code, the Weka jar is in a separate package RWekajars'. For more information on Weka see <https://www.cs.waikato.ac.nz/ml/weka/>.