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

Found 979 packages in 0.04 seconds

detect — by Peter Solymos, 2 years ago

Analyzing Wildlife Data with Detection Error

Models for analyzing site occupancy and count data models with detection error, including single-visit based models, conditional distance sampling and time-removal models. Package development was supported by the Alberta Biodiversity Monitoring Institute and the Boreal Avian Modelling Project.

kernlab — by Alexandros Karatzoglou, a year ago

Kernel-Based Machine Learning Lab

Kernel-based machine learning methods for classification, regression, clustering, novelty detection, quantile regression and dimensionality reduction. Among other methods 'kernlab' includes Support Vector Machines, Spectral Clustering, Kernel PCA, Gaussian Processes and a QP solver.

corrplot — by Taiyun Wei, 9 months ago

Visualization of a Correlation Matrix

Provides a visual exploratory tool on correlation matrix that supports automatic variable reordering to help detect hidden patterns among variables.

styler — by Lorenz Walthert, a year ago

Non-Invasive Pretty Printing of R Code

Pretty-prints R code without changing the user's formatting intent.

dynamicTreeCut — by Peter Langfelder, 9 years ago

Methods for Detection of Clusters in Hierarchical Clustering Dendrograms

Contains methods for detection of clusters in hierarchical clustering dendrograms.

sna — by Carter T. Butts, 10 months ago

Tools for Social Network Analysis

A range of tools for social network analysis, including node and graph-level indices, structural distance and covariance methods, structural equivalence detection, network regression, random graph generation, and 2D/3D network visualization.

changepoint — by Rebecca Killick, 8 months ago

Methods for Changepoint Detection

Implements various mainstream and specialised changepoint methods for finding single and multiple changepoints within data. Many popular non-parametric and frequentist methods are included. The cpt.mean(), cpt.var(), cpt.meanvar() functions should be your first point of call.

gtools — by Ben Bolker, 2 years ago

Various R Programming Tools

Functions to assist in R programming, including: - assist in developing, updating, and maintaining R and R packages ('ask', 'checkRVersion', 'getDependencies', 'keywords', 'scat'), - calculate the logit and inverse logit transformations ('logit', 'inv.logit'), - test if a value is missing, empty or contains only NA and NULL values ('invalid'), - manipulate R's .Last function ('addLast'), - define macros ('defmacro'), - detect odd and even integers ('odd', 'even'), - convert strings containing non-ASCII characters (like single quotes) to plain ASCII ('ASCIIfy'), - perform a binary search ('binsearch'), - sort strings containing both numeric and character components ('mixedsort'), - create a factor variable from the quantiles of a continuous variable ('quantcut'), - enumerate permutations and combinations ('combinations', 'permutation'), - calculate and convert between fold-change and log-ratio ('foldchange', 'logratio2foldchange', 'foldchange2logratio'), - calculate probabilities and generate random numbers from Dirichlet distributions ('rdirichlet', 'ddirichlet'), - apply a function over adjacent subsets of a vector ('running'), - modify the TCP_NODELAY ('de-Nagle') flag for socket objects, - efficient 'rbind' of data frames, even if the column names don't match ('smartbind'), - generate significance stars from p-values ('stars.pval'), - convert characters to/from ASCII codes ('asc', 'chr'), - convert character vector to ASCII representation ('ASCIIfy'), - apply title capitalization rules to a character vector ('capwords').

face — by Cai Li, 3 years ago

Fast Covariance Estimation for Sparse Functional Data

We implement the Fast Covariance Estimation for Sparse Functional Data paper published in Statistics and Computing .

trend — by Thorsten Pohlert, 2 years ago

Non-Parametric Trend Tests and Change-Point Detection

The analysis of environmental data often requires the detection of trends and change-points. This package includes tests for trend detection (Cox-Stuart Trend Test, Mann-Kendall Trend Test, (correlated) Hirsch-Slack Test, partial Mann-Kendall Trend Test, multivariate (multisite) Mann-Kendall Trend Test, (Seasonal) Sen's slope, partial Pearson and Spearman correlation trend test), change-point detection (Lanzante's test procedures, Pettitt's test, Buishand Range Test, Buishand U Test, Standard Normal Homogeinity Test), detection of non-randomness (Wallis-Moore Phase Frequency Test, Bartels rank von Neumann's ratio test, Wald-Wolfowitz Test) and the two sample Robust Rank-Order Distributional Test.