Examples: visualization, C++, networks, data cleaning, html widgets, ropensci.

Found 68 packages in 0.04 seconds

Rcmdr — by Manuel Munoz-Marquez, 2 months ago

R Commander

A platform-independent basic-statistics GUI (graphical user interface) for R, based on the tcltk package.

gWQS — by Stefano Renzetti, 3 years ago

Generalized Weighted Quantile Sum Regression

Fits Weighted Quantile Sum (WQS) regression (Carrico et al. (2014) ), a random subset implementation of WQS (Curtin et al. (2019) ), a repeated holdout validation WQS (Tanner et al. (2019) ) and a WQS with 2 indices (Renzetti et al. (2023) ) for continuous, binomial, multinomial, Poisson, quasi-Poisson and negative binomial outcomes.

tweedieDistr — by Stefano Damato, 18 days ago

Tweedie Distribution

Provides density, distribution function, quantile function, and random generation for the Tweedie distribution under the compound Poisson-Gamma parameterisation with power parameter in (1, 2). The density is evaluated using the series expansion of Dunn and Smyth (2005) , implemented in C++ via 'Rcpp' and 'RcppArmadillo' for performance. A constructor compatible with the 'distributional' package is also provided for use in tidy modelling workflows.

FVDDPpkg — by Stefano Damato, 2 years ago

Implement Fleming-Viot-Dependent Dirichlet Processes

A Bayesian Nonparametric model for the study of time-evolving frequencies, which has become renowned in the study of population genetics. The model consists of a Hidden Markov Model (HMM) in which the latent signal is a distribution-valued stochastic process that takes the form of a finite mixture of Dirichlet Processes, indexed by vectors that count how many times each value is observed in the population. The package implements methodologies presented in Ascolani, Lijoi and Ruggiero (2021) and Ascolani, Lijoi and Ruggiero (2023) that make it possible to study the process at the time of data collection or to predict its evolution in future or in the past.

SpatialKWD — by Stefano Gualandi, 4 years ago

Spatial KWD for Large Spatial Maps

Contains efficient implementations of Discrete Optimal Transport algorithms for the computation of Kantorovich-Wasserstein distances between pairs of large spatial maps (Bassetti, Gualandi, Veneroni (2020), ). All the algorithms are based on an ad-hoc implementation of the Network Simplex algorithm. The package has four main helper functions: compareOneToOne() (to compare two spatial maps), compareOneToMany() (to compare a reference map with a list of other maps), compareAll() (to compute a matrix of distances between a list of maps), and focusArea() (to compute the KWD distance within a focus area). In non-convex maps, the helper functions first build the convex-hull of the input bins and pad the weights with zeros.

ttservice — by Stefano Mangiola, a year ago

A Service for Tidy Transcriptomics Software Suite

It provides generic methods that are used by more than one package, avoiding conflicts. This package will be imported by 'tidySingleCellExperiment' and 'tidyseurat'.

tidygam — by Stefano Coretta, 2 years ago

Tidy Prediction and Plotting of Generalised Additive Models

Provides functions that compute predictions from Generalised Additive Models (GAMs) fitted with 'mgcv' and return them as a tibble. These can be plotted with a generic plot()-method that uses 'ggplot2' or plotted as any other data frame. The main function is predict_gam().

rticulate — by Stefano Coretta, a year ago

Articulatory Data Processing in R

A tool for processing Articulate Assistant Advanced™ (AAA) ultrasound tongue imaging data and Carstens AG500/1 electro-magnetic articulographic data.

tidyseurat — by Stefano Mangiola, 5 months ago

Brings Seurat to the Tidyverse

It creates an invisible layer that allow to see the 'Seurat' object as tibble and interact seamlessly with the tidyverse.

tidyHeatmap — by Stefano Mangiola, 9 months ago

A Tidy Implementation of Heatmap

This is a tidy implementation for heatmap. At the moment it is based on the (great) package 'ComplexHeatmap'. The goal of this package is to interface a tidy data frame with this powerful tool. Some of the advantages are: Row and/or columns colour annotations are easy to integrate just specifying one parameter (column names). Custom grouping of rows is easy to specify providing a grouped tbl. For example: df %>% group_by(...). Labels size adjusted by row and column total number. Default use of Brewer and Viridis palettes.