Compares how well different models estimate a quantity of interest (the "focus") so that different models may be preferred for different purposes. Comparisons within any class of models fitted by maximum likelihood are supported, with shortcuts for commonly-used classes such as generalised linear models and parametric survival models. The methods originate from Claeskens and Hjort (2003) <doi:10.1198/016214503000000819> and Claeskens and Hjort (2008, ISBN:9780521852258).

Flexible framework for specifying survival distributions through their hazard (failure rate) functions. Define arbitrary time-varying hazard functions to model complex failure patterns including bathtub curves, proportional hazards with covariates, and other non-standard hazard behaviors. Provides automatic computation of survival, CDF, PDF, quantiles, and sampling. Implements the likelihood model interface for maximum likelihood estimation with right-censored and left-censored survival data.

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 (preprint: <doi:10.32942/osf.io/dg7hw>).

This package provides tools to quickly compile taxonomic and distribution data from the Brazilian Flora 2020.

Fits the lifespan datasets of biological systems such as yeast, fruit flies, and other similar biological units with well-known finite mixture models introduced by Farewell et al. (1982) <doi:10.2307/2529885> and Al-Hussaini et al. (2000) <doi:10.1080/00949650008812033>. Estimates parameter space fitting of a lifespan dataset with finite mixtures of parametric distributions. Computes the following tasks; 1) Estimates parameter space of the finite mixture model by implementing the expectation maximization (EM) algorithm. 2) Finds a sequence of four goodness-of-fit measures consist of Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), Kolmogorov-Smirnov (KS), and log-likelihood (log-likelihood) statistics. 3)The initial values is determined by k-means clustering.

An interface to the fast_matrix_market C++ library, this package offers efficient read and write operations for Matrix Market files in R. It supports both sparse and dense matrix formats. Peer-reviewed at rOpenSci (<https://github.com/ropensci/software-review/issues/606>).

An implementation of a clustering algorithm for functional data based on adaptive density peak detection technique, in which the density is estimated by functional k-nearest neighbor density estimation based on a proposed semi-metric between functions. The proposed functional data clustering algorithm is computationally fast since it does not need iterative process. (Alex Rodriguez and Alessandro Laio (2014) <doi:10.1126/science.1242072>; Xiao-Feng Wang and Yifan Xu (2016) <doi:10.1177/0962280215609948>).

This package creates dynamic grid layouts of images that can be included in Shiny applications and R markdown documents.

Generate cost effective minimally changed run sequences for symmetrical as well as asymmetrical factorial designs.

Likelihood-free inference method for stochastic models. Uses a deterministic optimizer on simple simulations of the model that are performed with a prior drawn randomness by applying the inverse transform method. Is designed to work on its own and also by using the Julia package Jflimo available on the git page of the project: <https://metabarcoding.org/flimo>.

To help you access, transform, analyze, and visualize ForestGEO data, we developed a collection of R packages (<https://forestgeo.github.io/fgeo/>). This package, in particular, helps you to implement analyses of plot species distributions, topography, demography, and biomass. It also includes a torus translation test to determine habitat associations of tree species as described by Zuleta et al. (2018) <doi:10.1007/s11104-018-3878-0>. To learn more about ForestGEO visit <https://forestgeo.si.edu/>.

Play or simulate games of "Four in a Row" in the R console. This package is designed for educational purposes, encouraging users to write their own functions to play the game automatically. It contains a collection of built-in functions that play the game at various skill levels, for users to test their own functions against.

Algorithms for classical symmetric and deflation-based FastICA, reloaded deflation-based FastICA algorithm and an algorithm for adaptive deflation-based FastICA using multiple nonlinearities. For details, see Miettinen et al. (2014) <doi:10.1109/TSP.2014.2356442> and Miettinen et al. (2017) <doi:10.1016/j.sigpro.2016.08.028>. The package is described in Miettinen, Nordhausen and Taskinen (2018) <doi:10.32614/RJ-2018-046>.

SHE, FORAM Index and ABC Method analyses and custom plot functions for community data.

This package provides functions to access and retrieve metadata from the Finna API <https://api.finna.fi/>, which aggregates content from Finnish archives, libraries, and museums.

Calculate numerical asymptotic distribution functions of likelihood ratio statistics for fractional unit root tests and tests of cointegration rank. For these distributions, the included functions calculate critical values and P-values used in unit root tests, cointegration tests, and rank tests in the Fractionally Cointegrated Vector Autoregression (FCVAR) model. The functions implement procedures for tests described in the following articles: Johansen, S. and M. Ã . Nielsen (2012) <doi:10.3982/ECTA9299>, MacKinnon, J. G. and M. Ã . Nielsen (2014) <doi:10.1002/jae.2295>.

Processes data from The Social Networks and Fertility Survey, downloaded from <https://dataarchive.lissdata.nl>, including correcting respondent errors and transforming network data into network objects to facilitate analyses and visualisation.

This package provides methods for distance-based fuzzy unsupervised and semi-supervised clustering, including fuzzy and possibilistic models based on alternating optimization (AO) algorithm. The package introduces a vectorized estimation framework for prototype-based fuzzy clustering algorithms, enabling modular algorithm design and extensibility. It also supports storage and retrieval of intermediate AO optimization results for downstream analysis and processing. For more details see Kmita et al. (2024) <doi:10.1109/TFUZZ.2024.3370768>.

Create Frequently Asked Questions page for Shiny application.

Provide functions for forest inventory calculations. Common volumetric equations (Smalian, Newton and Huber) as well stacking factor and form.

This package provides three methods to generate fully-sequential space-filling designs inside a unit hypercube. A fully-sequential space-filling design means a sequence of nested designs (as the design size varies from one point up to some maximum number of points) with the design points added one at a time and such that the design at each size has good space-filling properties. Two methods target the minimum pairwise distance criterion and generate maximin designs, among which one method is more efficient when design size is large. One method targets the maximum hole size criterion and uses a heuristic to generate what is closer to a minimax design.

Scrapes data from Fitbit <http://www.fitbit.com>. This does not use the official API, but instead uses the API that the web dashboard uses to generate the graphs displayed on the dashboard after login at <http://www.fitbit.com>.

Approximate false positive rate control in selection frequency for random forest using the methods described by Ender Konukoglu and Melanie Ganz (2014) <arXiv:1410.2838>. Methods for calculating the selection frequency threshold at false positive rates and selection frequency false positive rate feature selection.

This package provides a "tabular-data-resource" (<https://specs.frictionlessdata.io/tabular-data-resource/>) is a simple format to describe a singular tabular data resource such as a CSV file. It includes support both for metadata such as author and title and a schema to describe the data, for example the types of the fields/columns in the data. Create a tabular-data-resource by providing a data.frame and specifying metadata. Write and read tabular-data-resources to and from disk.