This package provides a variety of models to analyze latent variables based on Bayesian learning: the partially CFA (Chen, Guo, Zhang, & Pan, 2020) <DOI: 10.1037/met0000293>; generalized PCFA; partially confirmatory IRM (Chen, 2020) <DOI: 10.1007/s11336-020-09724-3>; Bayesian regularized EFA <DOI: 10.1080/10705511.2020.1854763>; Fully and partially EFA.

Fit different model forms to single-cohort litter decomposition data (mass remaining through time) using likelihood-based estimation. Models span simple empirical to process-motivated forms with differing numbers of free parameters. Provides parameter estimates, uncertainty, and tools for model comparison/selection. Based on Cornwell & Weedon (2013) <doi:10.1111/2041-210X.12138>.

This package provides tools for detecting and correcting sample mix-ups between two sets of measurements, such as between gene expression data on two tissues. This is a revised version of the lineup package, to be more general and not tied to the qtl package.

This package provides a single analysis path that includes distance-based ordination, global tests of any effect of the microbiome, and tests of the effects of individual taxa with false-discovery-rate (FDR) control. It accommodates both continuous and discrete covariates as well as interaction terms to be tested either singly or in combination, allows for adjustment of confounding covariates, and uses permutation-based p-values that can control for sample correlations. It can be applied to transformed data, and an omnibus test can combine results from analyses conducted on different transformation scales. It can also be used for testing presence-absence associations based on infinite number of rarefaction replicates, testing mediation effects of the microbiome, analyzing censored time-to-event outcomes, and for compositional analysis by fitting linear models to centered-log-ratio taxa count data.

Highly optimized toolkit for approximately solving L0-regularized learning problems (a.k.a. best subset selection). The algorithms are based on coordinate descent and local combinatorial search. For more details, check the paper by Hazimeh and Mazumder (2020) <doi:10.1287/opre.2019.1919>.

This package provides functions to automatically retrieve and deduplicate reference metadata based on saved search strings. Access to Web of Science and Scopus requires personal API keys, while PubMed can be queried without one. The optional deduplication functionality requires the package ASySD available from <https://github.com/camaradesuk/ASySD>.

Estimation of various extensions of the mixed models including latent class mixed models, joint latent class mixed models, mixed models for curvilinear outcomes, mixed models for multivariate longitudinal outcomes using a maximum likelihood estimation method (Proust-Lima, Philipps, Liquet (2017) <doi:10.18637/jss.v078.i02>).

This package provides a system for fitting Logistic Curve by Rhodes Method. Method for fitting logistic curve by Rhodes Method is described in A.M.Gun,M.K.Gupta and B.Dasgupta(2019,ISBN:81-87567-81-3).

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

The first stand-alone R package for computation of latent correlation that takes into account all variable types (continuous/binary/ordinal/zero-inflated), comes with an optimized memory footprint, and is computationally efficient, essentially making latent correlation estimation almost as fast as rank-based correlation estimation. The estimation is based on latent copula Gaussian models. For continuous/binary types, see Fan, J., Liu, H., Ning, Y., and Zou, H. (2017). For ternary type, see Quan X., Booth J.G. and Wells M.T. (2018) <doi:10.48550/arXiv.1809.06255>. For truncated type or zero-inflated type, see Yoon G., Carroll R.J. and Gaynanova I. (2020) <doi:10.1093/biomet/asaa007>. For approximation method of computation, see Yoon G., Müller C.L. and Gaynanova I. (2021) <doi:10.1080/10618600.2021.1882468>. The latter method uses multi-linear interpolation originally implemented in the R package <https://cran.r-project.org/package=chebpol>.

Implementation of a theoretically supported alternative to k-nearest neighbors for functional data to solve problems of estimating unobserved segments of a partially observed functional data sample, functional classification and outlier detection. The approximating neighbor curves are piecewise functions built from a functional sample. Instead of a distance on a function space we use a locally defined distance function that satisfies stabilization criteria. The package allows the implementation of the methodology and the replication of the results in Elà as, A., Jiménez, R. and Yukich, J. (2020) <arXiv:2007.16059>.

Converts table-like objects to stand-alone PDF or PNG. Can be used to embed tables and arbitrary content in PDF or Word documents. Provides a low-level R interface for creating LaTeX code, e.g. command() and a high-level interface for creating PDF documents, e.g. as.pdf.data.frame(). Extensive customization is available via mid-level functions, e.g. as.tabular(). See also package?latexpdf'. Support for PNG is experimental; see as.png.data.frame'. Adapted from metrumrg <https://r-forge.r-project.org/R/?group_id=1215>. Requires a compatible installation of pdflatex', e.g. <https://miktex.org/>.

Classical tests of goodness-of-fit aim to validate the conformity of a postulated model to the data under study. In their standard formulation, however, they do not allow exploring how the hypothesized model deviates from the truth nor do they provide any insight into how the rejected model could be improved to better fit the data. To overcome these shortcomings, we establish a comprehensive framework for goodness-of-fit which naturally integrates modeling, estimation, inference and graphics. In this package, the deviance tests and comparison density plots are performed to conduct the LP smoothed inference, where the letter L denotes nonparametric methods based on quantiles and P stands for polynomials. Simulations methods are used to perform variance estimation, inference and post-selection adjustments. Algeri S. and Zhang X. (2020) <arXiv:2005.13011>.

Allows identification of palettes derived from LTER (Long Term Ecological Research) photographs based on user criteria. Also facilitates extraction of palettes from users photos directly.

Logger to keep track of informational events and errors useful for debugging.

Time series analysis based on lambda transformer and variational seq2seq, built on Torch'.

Suite of R functions for the estimation of the local false discovery rate (LFDR) using Type II maximum likelihood estimation (MLE).

Allows you to read and change the state of LIFX smart light bulbs via the LIFX developer api <https://api.developer.lifx.com/>. Covers most LIFX api endpoints, including changing light color and brightness, selecting lights by id, group or location as well as activating effects.

Parse various reflectance/transmittance/absorbance spectra file formats to extract spectral data and metadata, as described in Gruson, White & Maia (2019) <doi:10.21105/joss.01857>. Among other formats, it can import files from Avantes <https://www.avantes.com/>, CRAIC <https://www.microspectra.com/>, and OceanOptics'/'OceanInsight <https://www.oceanoptics.com/> brands.

The "Manual on Low-flow Estimation and Prediction" (Gustard & Demuth (2009, ISBN:978-92-63-11029-9)), published by the World Meteorological Organisation, gives a comprehensive summary on how to analyse stream flow data focusing on low-flows. This packages provides functions to compute the described statistics and produces plots similar to the ones in the manual.

Spatial and spatio-temporal modelling of point patterns using the log-Gaussian Cox process. Bayesian inference for spatial, spatiotemporal, multivariate and aggregated point processes using Markov chain Monte Carlo. See Benjamin M. Taylor, Tilman M. Davies, Barry S. Rowlingson, Peter J. Diggle (2015) <doi:10.18637/jss.v063.i07>.

Create and use data frame labels for data frame objects (frame labels), their columns (name labels), and individual values of a column (value labels). Value labels include one-to-one and many-to-one labels for nominal and ordinal variables, as well as numerical range-based value labels for continuous variables. Convert value-labeled variables so each value is replaced by its corresponding value label. Add values-converted-to-labels columns to a value-labeled data frame while preserving parent columns. Filter and subset a value-labeled data frame using labels, while returning results in terms of values. Overlay labels in place of values in common R commands to increase interpretability. Generate tables of value frequencies, with categories expressed as raw values or as labels. Access data frames that show value-to-label mappings for easy reference.

Statistical tests widely utilized in biostatistics, public policy, and law. Along with the well-known tests for equality of means and variances, randomness, and measures of relative variability, the package contains new robust tests of symmetry, omnibus and directional tests of normality, and their graphical counterparts such as robust QQ plot, robust trend tests for variances, etc. All implemented tests and methods are illustrated by simulations and real-life examples from legal statistics, economics, and biostatistics.

Data sets on various litter types like beach litter, riverain litter, floating litter, and seafloor litter are rapidly growing. This package offers a simple user interface to analyse these litter data in a consistent and reproducible way. It also provides functions to facilitate several kinds of litter analysis, e.g., trend analysis, power analysis, and baseline analysis. Under the hood, these functions are also used by the user interface. See Schulz et al. (2019) <doi:10.1016/j.envpol.2019.02.030> for details. MS-Windows users are advised to run litteR in RStudio'. See our vignette: Installation manual for RStudio and litteR'.