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ethan-alt

hdbayes:Bayesian Analysis of Generalized Linear Models with Historical Data

User-friendly functions for leveraging (multiple) historical data set(s) for generalized linear models. The package contains functions for sampling from the posterior distribution of a generalized linear model using the prior induced by the Bayesian hierarchical model, power prior by Ibrahim and Chen (2000) <doi:10.1214/ss/1009212673>, normalized power prior by Duan et al. (2006) <doi:10.1002/env.752>, normalized asymptotic power prior by Ibrahim et al. (2015) <doi:10.1002/sim.6728>, commensurate prior by Hobbs et al. (2011) <doi:10.1111/j.1541-0420.2011.01564.x>, robust meta-analytic-predictive prior by Schmidli et al. (2014) <doi:10.1111/biom.12242>, the latent exchangeability prior by Alt et al. (2023) <doi:10.48550/arXiv.2303.05223>, and a normal (or half-normal) prior. Functions for computing the marginal log-likelihood under each of the implemented priors are also included. The package compiles all the 'CmdStan' models once during installation using the 'instantiate' package.

Maintained by Ethan M. Alt. Last updated 7 days ago.

16.0 match 4 stars 6.23 score 7 scripts

dipterix

threeBrain:Your Advanced 3D Brain Visualization

A fast, interactive cross-platform, and easy to share 'WebGL'-based 3D brain viewer that visualizes 'FreeSurfer' and/or 'AFNI/SUMA' surfaces. The viewer widget can be either standalone or embedded into 'R-shiny' applications. The standalone version only require a web browser with 'WebGL2' support (for example, 'Chrome', 'Firefox', 'Safari'), and can be inserted into any websites. The 'R-shiny' support allows the 3D viewer to be dynamically generated from reactive user inputs. Please check the publication by Wang, Magnotti, Zhang, and Beauchamp (2023, <doi:10.1523/ENEURO.0328-23.2023>) for electrode localization. This viewer has been fully adopted by 'RAVE' <https://rave.wiki>, an interactive toolbox to analyze 'iEEG' data by Magnotti, Wang, and Beauchamp (2020, <doi:10.1016/j.neuroimage.2020.117341>). Please check citation("threeBrain") for details.

Maintained by Zhengjia Wang. Last updated 1 hours ago.

brainecogelectroencephalographyieeglocalizationmri-brainneuroimagingtemplate-brains

11.0 match 44 stars 8.26 score 38 scripts 1 dependents

cran

smcure:Fit Semiparametric Mixture Cure Models

An R-package for Estimating Semiparametric PH and AFT Mixture Cure Models.

Maintained by Chao Cai. Last updated 3 years ago.

3.5 match 1 stars 1.95 score 1 dependents

babaknaimi

sdm:Species Distribution Modelling

An extensible framework for developing species distribution models using individual and community-based approaches, generate ensembles of models, evaluate the models, and predict species potential distributions in space and time. For more information, please check the following paper: Naimi, B., Araujo, M.B. (2016) <doi:10.1111/ecog.01881>.

Maintained by Babak Naimi. Last updated 2 months ago.

0.5 match 24 stars 9.53 score 312 scripts 1 dependents

ecospat

ecospat:Spatial Ecology Miscellaneous Methods

Collection of R functions and data sets for the support of spatial ecology analyses with a focus on pre, core and post modelling analyses of species distribution, niche quantification and community assembly. Written by current and former members and collaborators of the ecospat group of Antoine Guisan, Department of Ecology and Evolution (DEE) and Institute of Earth Surface Dynamics (IDYST), University of Lausanne, Switzerland. Read Di Cola et al. (2016) <doi:10.1111/ecog.02671> for details.

Maintained by Olivier Broennimann. Last updated 1 months ago.

0.5 match 32 stars 9.35 score 418 scripts 1 dependents

adokter

bioRad:Biological Analysis and Visualization of Weather Radar Data

Extract, visualize and summarize aerial movements of birds and insects from weather radar data. See Dokter, A. M. et al. (2018) "bioRad: biological analysis and visualization of weather radar data" <doi:10.1111/ecog.04028> for a software paper describing package and methodologies.

Maintained by Adriaan M. Dokter. Last updated 20 days ago.

aeroecologyenrameumetnet-operalifewatchmovement-ecologynexradoscibioradarweather-radarwsr-88d

0.5 match 29 stars 8.65 score 56 scripts

evolecolgroup

pastclim:Manipulate Time Series of Climate Reconstructions

Methods to easily extract and manipulate climate reconstructions for ecological and anthropological analyses, as described in Leonardi et al. (2023) <doi:10.1111/ecog.06481>. The package includes datasets of palaeoclimate reconstructions, present observations, and future projections from multiple climate models.

Maintained by Andrea Manica. Last updated 4 days ago.

climate-datapaleoclimatespecies-distribution-modelling

0.5 match 38 stars 8.12 score 49 scripts

funecology

fundiversity:Easy Computation of Functional Diversity Indices

Computes six functional diversity indices. These are namely, Functional Divergence (FDiv), Function Evenness (FEve), Functional Richness (FRic), Functional Richness intersections (FRic_intersect), Functional Dispersion (FDis), and Rao's entropy (Q) (reviewed in Villéger et al. 2008 <doi:10.1890/07-1206.1>). Provides efficient, modular, and parallel functions to compute functional diversity indices (Grenié & Gruson 2023 <doi:10.1111/ecog.06585>).

Maintained by Matthias Grenié. Last updated 8 months ago.

biodiversitybiodiversity-indicatorsbiodiversity-informaticsfunctional-diversityfunctional-ecologyfunctional-traitfunctional-traitstraittrait-basedtraits

0.5 match 38 stars 7.34 score 38 scripts

cran

NPHMC:Sample Size Calculation for the Proportional Hazards Mixture Cure Model

An R-package for calculating sample size of a survival trial with or without cure fractions.

Maintained by Chao Cai. Last updated 3 years ago.

3.5 match 1.00 score

danlwarren

ENMTools:Analysis of Niche Evolution using Niche and Distribution Models

Constructing niche models and analyzing patterns of niche evolution. Acts as an interface for many popular modeling algorithms, and allows users to conduct Monte Carlo tests to address basic questions in evolutionary ecology and biogeography. Warren, D.L., R.E. Glor, and M. Turelli (2008) <doi:10.1111/j.1558-5646.2008.00482.x> Glor, R.E., and D.L. Warren (2011) <doi:10.1111/j.1558-5646.2010.01177.x> Warren, D.L., R.E. Glor, and M. Turelli (2010) <doi:10.1111/j.1600-0587.2009.06142.x> Cardillo, M., and D.L. Warren (2016) <doi:10.1111/geb.12455> D.L. Warren, L.J. Beaumont, R. Dinnage, and J.B. Baumgartner (2019) <doi:10.1111/ecog.03900>.

Maintained by Dan Warren. Last updated 2 months ago.

0.5 match 105 stars 6.91 score 126 scripts

smbc-nzp

MigConnectivity:Estimate Migratory Connectivity for Migratory Animals

Allows the user to estimate transition probabilities for migratory animals between any two phases of the annual cycle, using a variety of different data types. Also quantifies the strength of migratory connectivity (MC), a standardized metric to quantify the extent to which populations co-occur between two phases of the annual cycle. Includes functions to estimate MC and the more traditional metric of migratory connectivity strength (Mantel correlation) incorporating uncertainty from multiple sources of sampling error. For cross-species comparisons, methods are provided to estimate differences in migratory connectivity strength, incorporating uncertainty. See Cohen et al. (2018) <doi:10.1111/2041-210X.12916>, Cohen et al. (2019) <doi:10.1111/ecog.03974>, and Roberts et al. (2023) <doi:10.1002/eap.2788> for details on some of these methods.

Maintained by Jeffrey A. Hostetler. Last updated 12 months ago.

jagscpp

0.5 match 8 stars 6.77 score 41 scripts

neon-biodiversity

Ostats:O-Stats, or Pairwise Community-Level Niche Overlap Statistics

O-statistics, or overlap statistics, measure the degree of community-level trait overlap. They are estimated by fitting nonparametric kernel density functions to each species’ trait distribution and calculating their areas of overlap. For instance, the median pairwise overlap for a community is calculated by first determining the overlap of each species pair in trait space, and then taking the median overlap of each species pair in a community. This median overlap value is called the O-statistic (O for overlap). The Ostats() function calculates separate univariate overlap statistics for each trait, while the Ostats_multivariate() function calculates a single multivariate overlap statistic for all traits. O-statistics can be evaluated against null models to obtain standardized effect sizes. 'Ostats' is part of the collaborative Macrosystems Biodiversity Project "Local- to continental-scale drivers of biodiversity across the National Ecological Observatory Network (NEON)." For more information on this project, see the Macrosystems Biodiversity Website (<https://neon-biodiversity.github.io/>). Calculation of O-statistics is described in Read et al. (2018) <doi:10.1111/ecog.03641>, and a teaching module for introducing the underlying biological concepts at an undergraduate level is described in Grady et al. (2018) <http://tiee.esa.org/vol/v14/issues/figure_sets/grady/abstract.html>.

Maintained by Quentin D. Read. Last updated 4 months ago.

ecology

0.5 match 7 stars 6.69 score 28 scripts

cszang

treeclim:Numerical Calibration of Proxy-Climate Relationships

Bootstrapped response and correlation functions, seasonal correlations and evaluation of reconstruction skills for use in dendroclimatology and dendroecology, see Zang and Biondi (2015) <doi:10.1111/ecog.01335>.

Maintained by Christian Zang. Last updated 3 months ago.

climate-relationshipsdendroclimatologydendroecologyopenblascpp

0.5 match 18 stars 5.66 score 36 scripts

blasbenito

distantia:Advanced Toolset for Efficient Time Series Dissimilarity Analysis

Fast C++ implementation of Dynamic Time Warping for time series dissimilarity analysis, with applications in environmental monitoring and sensor data analysis, climate science, signal processing and pattern recognition, and financial data analysis. Built upon the ideas presented in Benito and Birks (2020) <doi:10.1111/ecog.04895>, provides tools for analyzing time series of varying lengths and structures, including irregular multivariate time series. Key features include individual variable contribution analysis, restricted permutation tests for statistical significance, and imputation of missing data via GAMs. Additionally, the package provides an ample set of tools to prepare and manage time series data.

Maintained by Blas M. Benito. Last updated 26 days ago.

dissimilaritydynamic-time-warpinglock-steptime-seriescpp

0.5 match 23 stars 5.76 score 11 scripts

txm676

gambin:Fit the Gambin Model to Species Abundance Distributions

Fits unimodal and multimodal gambin distributions to species-abundance distributions from ecological data, as in in Matthews et al. (2014) <DOI:10.1111/ecog.00861>. 'gambin' is short for 'gamma-binomial'. The main function is fit_abundances(), which estimates the 'alpha' parameter(s) of the gambin distribution using maximum likelihood. Functions are also provided to generate the gambin distribution and for calculating likelihood statistics.

Maintained by Thomas Matthews. Last updated 2 years ago.

0.5 match 3 stars 5.03 score 12 scripts

f-rousset

spaMM:Mixed-Effect Models, with or without Spatial Random Effects

Inference based on models with or without spatially-correlated random effects, multivariate responses, or non-Gaussian random effects (e.g., Beta). Variation in residual variance (heteroscedasticity) can itself be represented by a mixed-effect model. Both classical geostatistical models (Rousset and Ferdy 2014 <doi:10.1111/ecog.00566>), and Markov random field models on irregular grids (as considered in the 'INLA' package, <https://www.r-inla.org>), can be fitted, with distinct computational procedures exploiting the sparse matrix representations for the latter case and other autoregressive models. Laplace approximations are used for likelihood or restricted likelihood. Penalized quasi-likelihood and other variants discussed in the h-likelihood literature (Lee and Nelder 2001 <doi:10.1093/biomet/88.4.987>) are also implemented.

Maintained by François Rousset. Last updated 9 months ago.

gslcppopenmp

0.5 match 4.94 score 208 scripts 5 dependents

birdscancommunity

birdscanR:Migration Traffic Rate Calculation Package for 'Birdscan MR1' Radars

Extract data from 'Birdscan MR1' 'SQL' vertical-looking radar databases, filter, and process them to Migration Traffic Rates (#objects per hour and km) or density (#objects per km3) of, for example birds, and insects. Object classifications in the 'Birdscan MR1' databases are based on the dataset of Haest et al. (2021) <doi:10.5281/zenodo.5734960>). Migration Traffic Rates and densities can be calculated separately for different height bins (with a height resolution of choice) as well as over time periods of choice (e.g., 1/2 hour, 1 hour, 1 day, day/night, the full time period of observation, and anything in between). Two plotting functions are also included to explore the data in the 'SQL' databases and the resulting Migration Traffic Rate results. For details on the Migration Traffic Rate calculation procedures, see Schmid et al. (2019) <doi:10.1111/ecog.04025>.

Maintained by Birgen Haest. Last updated 3 months ago.

0.5 match 3 stars 4.86 score 3 scripts

marlonecobos

nichevol:Tools for Ecological Niche Evolution Assessment Considering Uncertainty

A collection of tools that allow users to perform critical steps in the process of assessing ecological niche evolution over phylogenies, with uncertainty incorporated explicitly in reconstructions. The method proposed here for ancestral reconstruction of ecological niches characterizes species' niches using a bin-based approach that incorporates uncertainty in estimations. Compared to other existing methods, the approaches presented here reduce risk of overestimation of amounts and rates of ecological niche evolution. The main analyses include: initial exploration of environmental data in occurrence records and accessible areas, preparation of data for phylogenetic analyses, executing comparative phylogenetic analyses of ecological niches, and plotting for interpretations. Details on the theoretical background and methods used can be found in: Owens et al. (2020) <doi:10.1002/ece3.6359>, Peterson et al. (1999) <doi:10.1126/science.285.5431.1265>, Soberón and Peterson (2005) <doi:10.17161/bi.v2i0.4>, Peterson (2011) <doi:10.1111/j.1365-2699.2010.02456.x>, Barve et al. (2011) <doi:10.1111/ecog.02671>, Machado-Stredel et al. (2021) <doi:10.21425/F5FBG48814>, Owens et al. (2013) <doi:10.1016/j.ecolmodel.2013.04.011>, Saupe et al. (2018) <doi:10.1093/sysbio/syx084>, and Cobos et al. (2021) <doi:10.1111/jav.02868>.

Maintained by Marlon E. Cobos. Last updated 2 years ago.

0.5 match 14 stars 3.85 score 2 scripts

anastasiou-andreas

ccid:Cross-Covariance Isolate Detect: a New Change-Point Method for Estimating Dynamic Functional Connectivity

Provides efficient implementation of the Cross-Covariance Isolate Detect (CCID) methodology for the estimation of the number and location of multiple change-points in the second-order (cross-covariance or network) structure of multivariate, possibly high-dimensional time series. The method is motivated by the detection of change points in functional connectivity networks for functional magnetic resonance imaging (fMRI), electroencephalography (EEG), magentoencephalography (MEG) and electrocorticography (ECoG) data. The main routines in the package have been extensively tested on fMRI data. For details on the CCID methodology, please see Anastasiou et al (2020).

Maintained by Andreas Anastasiou. Last updated 4 years ago.

0.5 match 2.70 score 2 scripts

cran

replicatedpp2w:Two-Way ANOVA-Like Method to Analyze Replicated Point Patterns

Test for effects of both individual factors and their interaction on replicated spatial patterns in a two factorial design, as explained in Ramon et al. (2016) <doi:10.1111/ecog.01848>.

Maintained by Marcelino de la Cruz. Last updated 2 years ago.

0.5 match 1.00 score

codelab3

kissmig:a Keep It Simple Species Migration Model

Simulating species migration and range dynamics under stable or changing environmental conditions based on a simple, raster-based, deterministic or stochastic migration model. Kissmig runs on binary or quantitative suitability maps, which are pre-calculated with niche-based habitat suitability models (also called ecological niche models (ENMs) or species distribution models (SDMs)). Nobis & Normand (2014), <doi:10.1111/ecog.00930>.

Maintained by Michael P. Nobis. Last updated 8 months ago.

0.5 match 1 stars 1.00 score 6 scripts

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