This package provides a dataset containing several color naming conventions established by multiple sources, along with associated color metadata. The package also provides related helper functions for mapping among the different Lego color naming conventions and between Lego colors, hex colors, and R color names, making it easy to convert any color palette to one based on existing Lego colors while keeping as close to the original color palette as possible. The functions use nearest color matching based on Euclidean distance in RGB space. Naming conventions for color mapping include those from BrickLink (<https://www.bricklink.com>), The Lego Group (<https://www.lego.com>), LDraw (<https://www.ldraw.org/>), and Peeron (<http://www.peeron.com/>).

This package provides density, distribution and random generation functions for the Linear Ballistic Accumulation (LBA) model, a widely used choice response time model in cognitive psychology. The package supports model specifications, parameter estimation, and likelihood computation, facilitating simulation and statistical inference for LBA-based experiments. For details on the LBA model, see Brown and Heathcote (2008) <doi:10.1016/j.cogpsych.2007.12.002>.

Estimates marginal likelihood from a posterior sample using the method described in Wang et al. (2023) <doi:10.1093/sysbio/syad007>, which does not require evaluation of any additional points and requires only the log of the unnormalized posterior density for each sampled parameter vector.

This package provides a LaTeX Letter class for rmarkdown', using the pandoc-letter template adapted for use with markdown'.

Computing statistical hypothesis testing for loading in principal component analysis (PCA) (Yamamoto, H. et al. (2014) <doi:10.1186/1471-2105-15-51>), orthogonal smoothed PCA (OS-PCA) (Yamamoto, H. et al. (2021) <doi:10.3390/metabo11030149>), one-sided kernel PCA (Yamamoto, H. (2023) <doi:10.51094/jxiv.262>), partial least squares (PLS) and PLS discriminant analysis (PLS-DA) (Yamamoto, H. et al. (2009) <doi:10.1016/j.chemolab.2009.05.006>), PLS with rank order of groups (PLS-ROG) (Yamamoto, H. (2017) <doi:10.1002/cem.2883>), regularized canonical correlation analysis discriminant analysis (RCCA-DA) (Yamamoto, H. et al. (2008) <doi:10.1016/j.bej.2007.12.009>), multiset PLS and PLS-ROG (Yamamoto, H. (2022) <doi:10.1101/2022.08.30.505949>).

Calculation of rectifying LTPD and AOQL plans for sampling inspection by variables which minimize mean inspection cost per lot of process average quality.

This package provides a suite of functions for reading in a rate file in XML format, stratify a cohort, and calculate SMRs from the stratified cohort and rate file.

This package performs adjusted inferences based on model objects fitted, using maximum likelihood estimation, by the extreme value analysis packages eva <https://cran.r-project.org/package=eva>, evd <https://cran.r-project.org/package=evd>, evir <https://cran.r-project.org/package=evir>, extRemes <https://cran.r-project.org/package=extRemes>, fExtremes <https://cran.r-project.org/package=fExtremes>, ismev <https://cran.r-project.org/package=ismev>, mev <https://cran.r-project.org/package=mev>, POT <https://cran.r-project.org/package=POT> and texmex <https://cran.r-project.org/package=texmex>. Adjusted standard errors and an adjusted loglikelihood are provided, using the chandwich package <https://cran.r-project.org/package=chandwich> and the object-oriented features of the sandwich package <https://cran.r-project.org/package=sandwich>. The adjustment is based on a robust sandwich estimator of the parameter covariance matrix, based on the methodology in Chandler and Bate (2007) <doi:10.1093/biomet/asm015>. This can be used for cluster correlated data when interest lies in the parameters of the marginal distributions, or for performing inferences that are robust to certain types of model misspecification. Univariate extreme value models, including regression models, are supported.

This package contains data sets to accompany the book: Lazic SE (2016). "Experimental Design for Laboratory Biologists: Maximising Information and Improving Reproducibility". Cambridge University Press.

This package provides functions for simulating missing morphometric data randomly, with taxonomic bias and with anatomical bias. LOST also includes functions for estimating linear and geometric morphometric data.

This package provides functions to estimate and visualize linear as well as nonlinear impulse responses based on local projections by Jordà (2005) <doi:10.1257/0002828053828518>. The methods and the package are explained in detail in Adämmer (2019) <doi:10.32614/RJ-2019-052>.

This package provides functions for normalizing standard laboratory measurements (e.g. hemoglobin, cholesterol levels) according to age and sex, based on the algorithms described in "Personalized lab test models to quantify disease potentials in healthy individuals" (Netta Mendelson Cohen, Omer Schwartzman, Ram Jaschek, Aviezer Lifshitz, Michael Hoichman, Ran Balicer, Liran I. Shlush, Gabi Barbash & Amos Tanay, <doi:10.1038/s41591-021-01468-6>). Allows users to easily obtain normalized values for standard lab results, and to visualize their distributions. See more at <https://tanaylab.weizmann.ac.il/labs/>.

This package creates a consensus genetic map by merging linkage maps from different populations. The software uses linear programming (LP) to efficiently minimize the mean absolute error between the consensus map and the linkage maps. This minimization is performed subject to linear inequality constraints that ensure the ordering of the markers in the linkage maps is preserved. When marker order is inconsistent between linkage maps, a minimum set of ordinal constraints is deleted to resolve the conflicts.

Bayesian population size estimation using non parametric latent-class models.

Given a postulated model and a set of data, the comparison density is estimated and the deviance test is implemented in order to assess if the data distribution deviates significantly from the postulated model. Finally, the results are summarized in a CD-plot as described in Algeri S. (2019) <arXiv:1906.06615>.

Affords an alternative, vector-based syntax to lavaan', as well as other convenience functions such as naming paths and defining indirect links automatically, in addition to convenience formatting optimized for a publication and script sharing workflow.

This package provides tools to retrieve and summarize taxonomic information and synonymy data for reptile species using data scraped from The Reptile Database website (<https://reptile-database.reptarium.cz/>). Outputs include clean and structured data frames useful for ecological, evolutionary, and conservation research.

An interface to LuaJIT <https://luajit.org>, a just-in-time compiler for the Lua scripting language <https://www.lua.org>. Allows users to run Lua code from R'.

Add and modify small charts on an interactive map created with package leaflet'. These charts can be used to represent at same time multiple variables on a single map.

This package provides a flexible approach, inspired by cosinor regression, for differential analysis of rhythmic transcriptome data. See Singer and Hughey (2018) <doi:10.1177/0748730418813785>.

Simulation and estimation of univariate and multivariate log-GARCH models. The main functions of the package are: lgarchSim(), mlgarchSim(), lgarch() and mlgarch(). The first two functions simulate from a univariate and a multivariate log-GARCH model, respectively, whereas the latter two estimate a univariate and multivariate log-GARCH model, respectively.

This package provides an extension to factors called lfactor that are similar to factors but allows users to refer to lfactor levels by either the level or the label.

Summarizes characteristics of linear mixed effects models without data or a fitted model by converting code for fitting lmer() from lme4 and lme() from nlme into tables, equations, and visuals. Outputs can be used to learn how to fit linear mixed effects models in R and to communicate about these models in presentations, manuscripts, and analysis plans.

Prototypes for construction of a Gaussian Stochastic Process emulator (GASP) of a computer model. This is done within the objective Bayesian implementation of the GASP. The package allows for construction of a linked GASP of the composite computer model. Computational implementation follows the mathematical exposition given in publication: Ksenia N. Kyzyurova, James O. Berger, Robert L. Wolpert. Coupling computer models through linking their statistical emulators. SIAM/ASA Journal on Uncertainty Quantification, 6(3): 1151-1171, (2018).<DOI:10.1137/17M1157702>.