This package provides a collection of functions for processing raw data from Stream Temperature, Intermittency, and Conductivity (STIC) loggers. STICr (pronounced "sticker") includes functions for tidying, calibrating, classifying, and doing quality checks on data from STIC sensors. Some package functionality is described in Wheeler/Zipper et al. (2023) <doi:10.31223/X5636K>.

Computes Value at risk and expected shortfall, two most popular measures of financial risk, for over one hundred parametric distributions, including all commonly known distributions. Also computed are the corresponding probability density function and cumulative distribution function. See Chan, Nadarajah and Afuecheta (2015) <doi:10.1080/03610918.2014.944658> for more details.

High-level functions to render LaTeX fragments in plots, including as labels and data symbols in ggplot2 plots, plus low-level functions to author LaTeX fragments (to produce LaTeX documents), typeset LaTeX documents (to produce DVI files), read DVI files (to produce "DVI" objects), and render "DVI" objects.

The FDA Adverse Event Reporting System (FAERS) is a database used for the spontaneous reporting of adverse events and medication errors related to human drugs and therapeutic biological products. faers pacakge serves as the interface between the FAERS database and R. Furthermore, faers pacakge offers a standardized approach for performing pharmacovigilance analysis.

Alternative polyadenylation (APA) is one of the important post- transcriptional regulation mechanisms which occurs in most human genes. InPAS facilitates the discovery of novel APA sites and the differential usage of APA sites from RNA-Seq data. It leverages cleanUpdTSeq to fine tune identified APA sites by removing false sites.

This package provides a pipeline for analysis of GC-MS data acquired in selected ion monitoring (SIM) mode. The tool also provides a guidance in choosing appropriate fragments for the targets of interest by using an optimization algorithm. This is done by considering overlapping peaks from a provided library by the user.

This package provides a toolbox for sparse contrastive principal component analysis (scPCA) of high-dimensional biological data. scPCA combines the stability and interpretability of sparse PCA with contrastive PCA's ability to disentangle biological signal from unwanted variation through the use of control data. Also implements and extends cPCA.

The tigre package implements our methodology of Gaussian process differential equation models for analysis of gene expression time series from single input motif networks. The package can be used for inferring unobserved transcription factor (TF) protein concentrations from expression measurements of known target genes, or for ranking candidate targets of a TF.

The Power Law Global Error Model (PLGEM) has been shown to faithfully model the variance-versus-mean dependence that exists in a variety of genome-wide datasets, including microarray and proteomics data. The use of PLGEM has been shown to improve the detection of differentially expressed genes or proteins in these datasets.

The main function of this package is beep(), with the purpose to make it easy to play notification sounds on whatever platform you are on. It is intended to be useful, for example, if you are running a long analysis in the background and want to know when it is ready.

Traditional latent variable models assume that the population is homogeneous, meaning that all individuals in the population are assumed to have the same latent structure. However, this assumption is often violated in practice given that individuals may differ in their age, gender, socioeconomic status, and other factors that can affect their latent structure. The robust expectation maximization (REM) algorithm is a statistical method for estimating the parameters of a latent variable model in the presence of population heterogeneity as recommended by Nieser & Cochran (2023) <doi:10.1037/met0000413>. The REM algorithm is based on the expectation-maximization (EM) algorithm, but it allows for the case when all the data are generated by the assumed data generating model.

Placental epigenetic clock to estimate aging based on gestational age using DNA methylation levels, so called placental epigenetic clock (PlEC). We developed a PlEC for the 2024 Placental Clock DREAM Challenge (<https://www.synapse.org/Synapse:syn59520082/wiki/628063>). Our PlEC achieved the top performance based on an independent test set. PlEC can be used to identify accelerated/decelerated aging of placenta for understanding placental dysfunction-related conditions, e.g., great obstetrical syndromes including preeclampsia, fetal growth restriction, preterm labor, preterm premature rupture of the membranes, late spontaneous abortion, and placental abruption. Detailed methodologies and examples are documented in our vignette, available at <https://herdiantrisufriyana.github.io/rplec/doc/placental_aging_analysis.html>.

Iterative least cost path and minimum spanning tree methods for projecting forest road networks. The methods connect a set of target points to an existing road network using igraph <https://igraph.org> to identify least cost routes. The cost of constructing a road segment between adjacent pixels is determined by a user supplied weight raster and a weight function; options include the average of adjacent weight raster values, and a function of the elevation differences between adjacent cells that penalizes steep grades. These road network projection methods are intended for integration into R workflows and modelling frameworks used for forecasting forest change, and can be applied over multiple time-steps without rebuilding a graph at each time-step.

The base functions for set operations in R can be used for only two sets. This package RVenn provides functions for dealing with multiple sets. It uses purr to find the union, intersection and difference of three or more sets. This package also provides functions for pairwise set operations among several sets. Further, based on ggplot2 and ggforce, a Venn diagram can be drawn for two or three sets. For bigger data sets, a clustered heatmap showing the presence or absence of the elements of the sets can be drawn based on the pheatmap package. Finally, enrichment test can be applied to two sets whether an overlap is statistically significant or not.

Eases the use of ecotoxicological effect models. Can simulate common toxicokinetic-toxicodynamic (TK/TD) models such as General Unified Threshold models of Survival (GUTS) and Lemna. It can derive effects and effect profiles (EPx) from scenarios. It supports the use of tidyr workflows employing the pipe symbol. Time-consuming tasks can be parallelized.

This package provides a fast and general implementation of the Elston-Stewart algorithm that can calculate the likelihoods of large and complex pedigrees. References for the Elston-Stewart algorithm are Elston & Stewart (1971) <doi:10.1159/000152448>, Lange & Elston (1975) <doi:10.1159/000152714> and Cannings et al. (1978) <doi:10.2307/1426718>.

Get text from images of text using Captricity Optical Character Recognition (OCR) API. Captricity allows you to get text from handwritten forms --- think surveys --- and other structured paper documents. And it can output data in form a delimited file keeping field information intact. For more information, read <https://shreddr.captricity.com/developer/overview/>.

We provide the main R functions to compute the posterior interval for the noncentrality parameter of the chi-squared distribution. The skewness estimate of the posterior distribution is also available to improve the coverage rate of posterior intervals. Details can be found in Du and Hu (2020) <doi:10.1080/01621459.2020.1777137>.

This package provides a neighborhood-based, greedy search algorithm is performed to estimate a feature allocation by minimizing the expected loss based on posterior samples from the feature allocation distribution. The method is described in Dahl, Johnson, and Andros (2023) "Comparison and Bayesian Estimation of Feature Allocations" <doi:10.1080/10618600.2023.2204136>.

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>.

Generate concentration-time profiles from linear pharmacokinetic (PK) systems, possibly with first-order absorption or zero-order infusion, possibly with one or more peripheral compartments, and possibly under steady-state conditions. Single or multiple doses may be specified. Secondary (derived) PK parameters (e.g. Cmax, Ctrough, AUC, Tmax, half-life, etc.) are computed.

Compose generic monadic function pipelines with %>>% and %>-% based on implementing the S7 generics fmap() and bind(). Methods are provided for the built-in list type and the maybe class from the maybe package. The concepts are modelled directly after the Monad typeclass in Haskell, but adapted for idiomatic use in R.

This package implements a simulation study to assess the strengths and weaknesses of causal inference methods for estimating policy effects using panel data. See Griffin et al. (2021) <doi:10.1007/s10742-022-00284-w> and Griffin et al. (2022) <doi:10.1186/s12874-021-01471-y> for a description of our methods.

Automates and standardizes the import of raw data from Oregon RFID (radio-frequency identification) ORMR (Oregon RFID Multi-Reader) and ORSR (Oregon RFID Single Reader) antenna readers. Compiled data can then be combined within multi-reader arrays for further analysis, including summarizing tag and reader detections, determining tag direction, and calculating antenna efficiency.