14 packages on CRAN
A lightweight package that adds progress bar to vectorized R functions ('*apply'). The implementation can easily be added to functions where showing the progress is useful (e.g. bootstrap). The type and style of the progress bar (with percentages or remaining time) can be set through options. Supports several parallel processing backends.
Resource Selection (Probability) Functions for use-availability wildlife data based on weighted distributions as described in Lele and Keim (2006) <doi:10.1890/0012-9658(2006)87[3021:WDAEOR]2.0.CO;2>, Lele (2009) <doi:10.2193/2007-535>, and Solymos & Lele (2016) <doi:10.1111/2041-210X.12432>.
A framework package aimed to provide standardized computational environment for specialist work via object classes to represent the data coded by samples, taxa and segments (i.e. subpopulations, repeated measures). It supports easy processing of the data along with cross tabulation and relational data tables for samples and taxa. An object of class `mefa' is a project specific compendium of the data and can be easily used in further analyses. Methods are provided for extraction, aggregation, conversion, plotting, summary and reporting of `mefa' objects. Reports can be generated in plain text or LaTeX format. Vignette contains worked examples.
Low level functions for implementing maximum likelihood estimating procedures for complex models using data cloning and Bayesian Markov chain Monte Carlo methods. Sequential and parallel MCMC support for JAGS, WinBUGS and OpenBUGS.
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 (www.abmi.ca) and the Boreal Avian Modelling Project (borealbirds.ca).
An S4 update of the 'mefa' package using sparse matrices for enhanced efficiency. Sparse array-like objects are supported via lists of sparse matrices.
S4 classes around infrastructure provided by the 'coda' and 'dclone' packages to make package development easy as a breeze with data cloning for hierarchical models.
Likelihood based population viability analysis in the presence of observation error and missing data. The package can be used to fit, compare, predict, and forecast various growth model types using data cloning.
Evaluating if values of vectors are within different open/closed intervals (`x %% c(a, b)`), or if two closed intervals overlap (`c(a1, b1) %o% c(a2, b2)`). Operators for negation and directional relations also implemented.
Hierarchical models for the analysis of species-area relationships (SARs) by combining several data sets and covariates; with a global data set combining individual SAR studies; as described in Solymos and Lele (2012, Global Ecology and Biogeography 21, 109-120).
Likelihood based optimal partitioning for indicator species analysis. Finding the best binary partition for each species based on model selection, possibly controlling for modifying/confounding variables as described in Kemencei et al. (2014) <doi:10.1556/ComEc.15.2014.2.6>. The package also implements various measures of uncertainty based on binary partitions, optimal multinomial partitioning, and exploratory suitability indices, with native support for parallel computations.
Lots of plots, various labeling, axis and color scaling functions.
Ordination methods, diversity analysis and other functions for community and vegetation ecologists.
Toolset for the exploration of genetic and genomic data. Adegenet provides formal (S4) classes for storing and handling various genetic data, including genetic markers with varying ploidy and hierarchical population structure ('genind' class), alleles counts by populations ('genpop'), and genome-wide SNP data ('genlight'). It also implements original multivariate methods (DAPC, sPCA), graphics, statistical tests, simulation tools, distance and similarity measures, and several spatial methods. A range of both empirical and simulated datasets is also provided to illustrate various methods.