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

Found 156 packages in 0.02 seconds

arakno — by Pedro Cardoso, 2 years ago

ARAchnid KNowledge Online

Allows the user to connect with the World Spider Catalogue (WSC; < https://wsc.nmbe.ch/>) and the World Spider Trait (WST; < https://spidertraits.sci.muni.cz/>) databases. Also performs several basic functions such as checking names validity, retrieving coordinate data from the Global Biodiversity Information Facility (GBIF; < https://www.gbif.org/>), and mapping.

spidR — by Pedro Cardoso, 3 years ago

Spider Knowledge Online

Allows the user to connect with the World Spider Catalogue (WSC; < https://wsc.nmbe.ch/>) and the World Spider Trait (WST; < https://spidertraits.sci.muni.cz/>) databases. Also performs several basic functions such as checking names validity, retrieving coordinate data from the Global Biodiversity Information Facility (GBIF; < https://www.gbif.org/>), and mapping.

SoundShape — by Pedro Rocha, 3 days ago

Sound Waves Onto Morphometric Data

Implement a promising, and yet little explored protocol for bioacoustical analysis, the eigensound method by MacLeod, Krieger and Jones (2013) . Eigensound is a multidisciplinary method focused on the direct comparison between stereotyped sounds from different species. 'SoundShape', in turn, provide the tools required for anyone to go from sound waves to Principal Components Analysis, using tools extracted from traditional bioacoustics (i.e. 'tuneR' and 'seewave' packages), geometric morphometrics (i.e. 'geomorph' package) and multivariate analysis (e.g. 'stats' package). For more information, please see Rocha and Romano (2021) and check 'SoundShape' repository on GitHub for news and updates < https://github.com/p-rocha/SoundShape>.

phylin — by Pedro Tarroso, 5 years ago

Spatial Interpolation of Genetic Data

The spatial interpolation of genetic distances between samples is based on a modified kriging method that accepts a genetic distance matrix and generates a map of probability of lineage presence. This package also offers tools to generate a map of potential contact zones between groups with user-defined thresholds in the tree to account for old and recent divergence. Additionally, it has functions for IDW interpolation using genetic data and midpoints.

Rlda — by Pedro Albuquerque, 6 years ago

Bayesian LDA for Mixed-Membership Clustering Analysis

Estimates the Bayesian LDA model for mixed-membership clustering based on different types of data (i.e., Multinomial, Bernoulli, and Binomial entries). Albuquerque, Valle and Li (2019) .

nLTT — by Thijs Janzen, a year ago

Calculate the NLTT Statistic

Provides functions to calculate the normalised Lineage-Through- Time (nLTT) statistic, given two phylogenetic trees. The nLTT statistic measures the difference between two Lineage-Through-Time curves, where each curve is normalised both in time and in number of lineages.

RAT — by Pedro Cardoso, 2 years ago

Research Assessment Tools

Includes algorithms to assess research productivity and patterns, such as the h-index and i-index. Cardoso et al. (2022) Cardoso, P., Fukushima, C.S. & Mammola, S. (2022) Quantifying the internationalization and representativeness in research. Trends in Ecology and Evolution, 37: 725-728.

HiDimDA — by Antonio Pedro Duarte Silva, 2 months ago

High Dimensional Discriminant Analysis

Performs linear discriminant analysis in high dimensional problems based on reliable covariance estimators for problems with (many) more variables than observations. Includes routines for classifier training, prediction, cross-validation and variable selection.

MultipleBubbles — by Pedro Araujo, 6 years ago

Test and Detection of Explosive Behaviors for Time Series

Provides the Augmented Dickey-Fuller test and its variations to check the existence of bubbles (explosive behavior) for time series, based on the article by Peter C. B. Phillips, Shuping Shi and Jun Yu (2015a) . Some functions may take a while depending on the size of the data used, or the number of Monte Carlo replications applied.

rplotengine — by Pedro-Pablo Garrido Abenza, 2 years ago

R as a Plotting Engine

Generate basic charts either by custom applications, or from a small script launched from the system console, or within the R console. Two ASCII text files are necessary: (1) The graph parameters file, which name is passed to the function 'rplotengine()'. The user can specify the titles, choose the type of the graph, graph output formats (e.g. png, eps), proportion of the X-axis and Y-axis, position of the legend, whether to show or not a grid at the background, etc. (2) The data to be plotted, which name is specified as a parameter ('data_filename') in the previous file. This data file has a tabulated format, with a single character (e.g. tab) between each column. Optionally, the file could include data columns for showing confidence intervals.