This package provides data sets and R Codes for E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement, CSIRO Publishing.

Expectile regression is a nice tool for estimating the conditional expectiles of a response variable given a set of covariates. This package implements a regression tree based gradient boosting estimator for nonparametric multiple expectile regression, proposed by Yang, Y., Qian, W. and Zou, H. (2018) <doi:10.1080/00949655.2013.876024>. The code is based on the gbm package originally developed by Greg Ridgeway.

Facilitates univariate and multivariate analysis of evolutionary sequences of phenotypic change. The package extends the modeling framework available in the paleoTS package. Please see <https://klvoje.github.io/evoTS/index.html> for information about the package and the implemented models.

This package provides functions for covariance matrix comparisons, estimation of repeatabilities in measurements and matrices, and general evolutionary quantitative genetics tools. Melo D, Garcia G, Hubbe A, Assis A P, Marroig G. (2016) <doi:10.12688/f1000research.7082.3>.

Support functions for R-based EQUAL-STATS software which automatically classifies the data and performs appropriate statistical tests. EQUAL-STATS software is a shiny application with an user-friendly interface to perform complex statistical analysis. Gurusamy,K (2024)<doi:10.5281/zenodo.13354162>.

Fits a variety of hidden Markov models, structured in an extended generalized linear model framework. See T. Rolf Turner, Murray A. Cameron, and Peter J. Thomson (1998) <doi:10.2307/3315677>, and Rolf Turner (2008) <doi:10.1016/j.csda.2008.01.029> and the references cited therein.

This package provides an implementation of the maximum likelihood methods for deriving Elo scores as published in Foerster, Franz et al. (2016) <DOI:10.1038/srep35404>.

This package provides tools for calculating evolvability parameters from estimated G-matrices as defined in Hansen and Houle (2008) <doi:10.1111/j.1420-9101.2008.01573.x> and fits phylogenetic comparative models that link the rate of evolution of a trait to the state of another evolving trait (see Hansen et al. 2021 Systematic Biology <doi:10.1093/sysbio/syab079>). The package was released with Bolstad et al. (2014) <doi:10.1098/rstb.2013.0255>, which contains some examples of use.

An implementation for using efficient initials to compute the maximal eigenpair in R. It provides three algorithms to find the efficient initials under two cases: the tridiagonal matrix case and the general matrix case. Besides, it also provides two algorithms for the next to the maximal eigenpair under these two cases.

This package provides a collection of nice plotting functions directly from a data.frame with limited customisation possibilities.

Supports designing efficient discrete choice experiments (DCEs). Experimental designs can be formed on the basis of orthogonal arrays or search methods for optimal designs (Federov or mixed integer programs). Various methods for converting these experimental designs into a discrete choice experiment. Many efficiency measures! Draws from literature of Kuhfeld (2010) and Street et. al (2005) <doi:10.1016/j.ijresmar.2005.09.003>.

Offers a set of functions to easily download and clean Brazilian electoral data from the Superior Electoral Court and CepespData websites. Among other features, the package retrieves data on local and federal elections for all positions (city councilor, mayor, state deputy, federal deputy, governor, and president) aggregated by state, city, and electoral zones.

This package performs hypothesis testing for general block designs with empirical likelihood. The core computational routines are implemented using the Eigen C++ library and RcppEigen interface, with OpenMP for parallel computation. Details of the methods are given in Kim, MacEachern, and Peruggia (2023) <doi:10.1080/10485252.2023.2206919>. This work was supported by the U.S. National Science Foundation under Grants No. SES-1921523 and DMS-2015552.

Implementation of the scaling functions presented in "General statistical scaling laws for stability in ecological systems" by Clark et al in Ecology Letters <DOI:10.1111/ele.13760>. Includes functions for extrapolating variability, resistance, and resilience across spatial and ecological scales, as well as a basic simulation function for producing time series, and a regression routine for generating unbiased parameter estimates. See the main text of the paper for more details.

This package provides a collection of functions and jamovi module for the estimation approach to inferential statistics, the approach which emphasizes effect sizes, interval estimates, and meta-analysis. Nearly all functions are based on statpsych and metafor'. This package is still under active development, and breaking changes are likely, especially with the plot and hypothesis test functions. Data sets are included for all examples from Cumming & Calin-Jageman (2024) <ISBN:9780367531508>.

Implementation in a simple and efficient way of fully customisable population genetics simulations, considering multiple loci that have epistatic interactions. Specifically suited to the modelling of multilocus nucleocytoplasmic systems (with both diploid and haploid loci), it is nevertheless possible to simulate purely diploid (or purely haploid) genetic models. Examples of models that can be simulated with Ease are numerous, for example models of genetic incompatibilities as presented by Marie-Orleach et al. (2022) <doi:10.1101/2022.07.25.501356>. Many others are conceivable, although few are actually explored, Ease having been developed in particular to provide a solution so that these kinds of models can be simulated simply.

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>.

Computing economic analysis in civil infrastructure and ecosystem restoration projects is a typical activity. This package contains Standard cost engineering and engineering economics methods that are applied to convert between present, future, and annualized costs. Newnan D. (2020) <ISBN 9780190931919> â Engineering Economic Analysisâ .

This package provides tools to download and manipulate the Permanent Household Survey from Argentina (EPH is the Spanish acronym for Permanent Household Survey). e.g: get_microdata() for downloading the datasets, get_poverty_lines() for downloading the official poverty baskets, calculate_poverty() for the calculation of stating if a household is in poverty or not, following the official methodology. organize_panels() is used to concatenate observations from different periods, and organize_labels() adds the official labels to the data. The implemented methods are based on INDEC (2016) <http://www.estadistica.ec.gba.gov.ar/dpe/images/SOCIEDAD/EPH_metodologia_22_pobreza.pdf>. As this package works with the argentinian Permanent Household Survey and its main audience is from this country, the documentation was written in Spanish.

Split experiment sentences by different experiment design given by the user and the result can be used in E-prime (<https://pstnet.com/products/e-prime/>).

Provide an optimal histogram, in the sense of probability density estimation and features detection, by means of multiscale variational inference. In other words, the resulting histogram servers as an optimal density estimator, and meanwhile recovers the features, such as increases or modes, with both false positive and false negative controls. Moreover, it provides a parsimonious representation in terms of the number of blocks, which simplifies data interpretation. The only assumption for the method is that data points are independent and identically distributed, so it applies to fairly general situations, including continuous distributions, discrete distributions, and mixtures of both. For details see Li, Munk, Sieling and Walther (2016) <arXiv:1612.07216>.

Work with Ecological Metadata Language ('EML') files. EML is a widely used metadata standard in the ecological and environmental sciences, described in Jones et al. (2006), <doi:10.1146/annurev.ecolsys.37.091305.110031>.

Support in preparing a raw ESM dataset for statistical analysis. Preparation includes the handling of errors (mostly due to technological reasons) and the generating of new variables that are necessary and/or helpful in meeting the conditions when statistically analyzing ESM data. The functions in esmprep are meant to hierarchically lead from bottom, i.e. the raw (separated) ESM dataset(s), to top, i.e. a single ESM dataset ready for statistical analysis. This hierarchy evolved out of my personal experience in working with ESM data.

Chat with large language models from a range of providers including Claude <https://claude.ai>, OpenAI <https://chatgpt.com>, and more. Supports streaming, asynchronous calls, tool calling, and structured data extraction.