These functions take a gene expression value matrix, a primary covariate vector, an additional known covariates matrix. A two stage analysis is applied to counter the effects of latent variables on the rankings of hypotheses. The estimation and adjustment of latent effects are proposed by Sun, Zhang and Owen (2011). "leapp" is developed in the context of microarray experiments, but may be used as a general tool for high throughput data sets where dependence may be involved.

This package provides three classes: Queue, PriorityQueue and Stack. Queue is just a "plain vanilla" FIFO queue; PriorityQueue orders items according to priority. Stack implements LIFO.

Flexible functions that use lme4 as computational engine for fitting models used in Genomic Selection (GS). GS is a technology used for genetic improvement, and it has many advantages over phenotype-based selection. There are several statistical models that adequately approach the statistical challenges in GS, such as in linear mixed models (LMMs). The lme4 is the standard package for fitting linear and generalized LMMs in the R-package, but its use for genetic analysis is limited because it does not allow the correlation between individuals or groups of individuals to be defined. The lme4GS package is focused on fitting LMMs with covariance structures defined by the user, bandwidth selection, and genomic prediction. The new package is focused on genomic prediction of the models used in GS and can fit LMMs using different variance-covariance matrices. Several examples of GS models are presented using this package as well as the analysis using real data. For more details see Caamal-Pat et.al. (2021) <doi:10.3389/fgene.2021.680569>.

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.

Adds smoothing spline modelling capability to nlme. Fits smoothing spline terms in Gaussian linear and nonlinear mixed-effects models.

An implementation of algorithms described in Jewell and Witten (2017) <arXiv:1703.08644>.

Fast estimation of multinomial (MNL) and mixed logit (MXL) models in R. Models can be estimated using "Preference" space or "Willingness-to-pay" (WTP) space utility parameterizations. Weighted models can also be estimated. An option is available to run a parallelized multistart optimization loop with random starting points in each iteration, which is useful for non-convex problems like MXL models or models with WTP space utility parameterizations. The main optimization loop uses the nloptr package to minimize the negative log-likelihood function. Additional functions are available for computing and comparing WTP from both preference space and WTP space models and for predicting expected choices and choice probabilities for sets of alternatives based on an estimated model. Mixed logit models can include uncorrelated or correlated heterogeneity covariances and are estimated using maximum simulated likelihood based on the algorithms in Train (2009) <doi:10.1017/CBO9780511805271>. More details can be found in Helveston (2023) <doi:10.18637/jss.v105.i10>.

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.

Creating efficiently new column(s) in a data frame (including tibble) by applying a function one row at a time.

Apply Univariate Long Memory Models, Apply Multivariate Short Memory Models To Hydrological Dataset, Estimate Intensity Duration Frequency curve to rainfall series. NEW -- Calculate the monthly water requirement for herbaceous and arboreal plants.

Whole-buffer DEFLATE-based compression and decompression of raw vectors using the libdeflate library (see <https://github.com/ebiggers/libdeflate>). Provides the user with additional control over the speed and the quality of DEFLATE compression compared to the fixed level of compression offered in R's memCompress() function. Also provides the libdeflate static library and C headers along with a CMake target and packageâ config file that ease linking of libdeflate in packages that compile and statically link bundled libraries using CMake'.

This package provides tools for common operations on lists. Provided are short-cuts to operations like selecting and merging data stored in lists. The functions in this package are designed to be used with pipes.

R interface for working with nanometer scale secondary ion mass spectrometry (NanoSIMS) data exported from Look at NanoSIMS.

Allows to install the R languageserver with all dependencies into a separate library and use that independent installation automatically when R is instantiated as a language server process. Useful for making language server seamless to use without running into package version conflicts.

Brush a row-level display and see aggregate displays fill in proportion to the rows selected, with the row-to-group mapping retained. Aggregate views such as bar charts summarise many rows into one mark, so a selection made on individual rows is resolved back through the row to group mapping to fill each mark partially. Output is an htmlwidget that works inside shiny or as a single self-contained HTML file with no server, so an interactive figure can be archived or shared like a static one.

Implementation of several phenotype-based family genetic risk scores with unified input data and data preparation functions to help facilitate the required data preparation and management. The implemented family genetic risk scores are the extended liability threshold model conditional on family history from Pedersen (2022) <doi:10.1016/j.ajhg.2022.01.009> and Pedersen (2023) <https://www.nature.com/articles/s41467-023-41210-z>, Pearson-Aitken Family Genetic Risk Scores from Krebs (2024) <doi:10.1016/j.ajhg.2024.09.009>, and family genetic risk score from Kendler (2021) <doi:10.1001/jamapsychiatry.2021.0336>.

This package provides utility functions for some of the less-glamorous tasks involved in landscape analysis. It includes functions to coerce raster data to the common tibble format and vice versa, it helps with flexible reclassification tasks of raster data and it provides a function to merge multiple raster. Furthermore, landscapetools helps landscape scientists to visualize their data by providing optional themes and utility functions to plot single landscapes, rasterstacks', -bricks and lists of raster.

This package provides functions to fit quantile regression models for hierarchical data (2-level nested designs) as described in Geraci and Bottai (2014, Statistics and Computing) <doi:10.1007/s11222-013-9381-9>. A vignette is given in Geraci (2014, Journal of Statistical Software) <doi:10.18637/jss.v057.i13> and included in the package documents. The packages also provides functions to fit quantile models for independent data and for count responses.

It is an extension of lmom R package: pel...()','cdf...()',qua...() function families are lumped and called from one function per each family respectively in order to create robust automatic tools to fit data with different probability distributions and then to estimate probability values and return periods. The implemented functions are able to manage time series with constant and/or missing values without stopping the execution with error messages. The package also contains tools to calculate several indices based on variability (e.g. SPI , Standardized Precipitation Index, see <https://climatedataguide.ucar.edu/climate-data/standardized-precipitation-index-spi> and <http://spei.csic.es/>) for multiple time series or spatially gridded values.

This package provides an interface to the financial data platform <https://datahub.limex.com/>., enabling users to retrieve real-time and historical financial data. Functions within the package allow access to instruments, candlestick charts, fundamentals, news, events, models, and trading signals. Authentication is managed through user-specific API tokens, which are securely handled via environment variables.

We developed an approach to detect differential expression features in long non-coding RNA low counts, using generalized linear model with zero-inflated exponential quasi likelihood ratio test. Methods implemented in this package are described in Li (2019) <doi:10.1186/s12864-019-5926-4>.

Bayesian Survival models via the mixture of Log-Normal distribution extends the well-known survival models and accommodates different behaviour over time and considers higher censored survival times. The proposal combines mixture distributions Fruhwirth-Schnatter(2006) <doi:10.1007/s11336-009-9121-4>, and data augmentation techniques Tanner and Wong (1987) <doi:10.1080/01621459.1987.10478458>.

This package implements Expectation/Conditional Maximization Either (ECME), rapidly converging algorithms, and Bayesian inference for linear mixed models following Schafer (1998), "Some Improved Procedures for Linear Mixed Models", Department of Statistics, The Pennsylvania State University.

This package creates a series of sets of graphics and statistics related to the longitudinal cascade, all included in a single object. The longitudinal cascade inputs longitudinal data to identify gaps in the HIV and related cascades by observing differences using time to event and survival methods. The stage definitions are set by the user, with default standard options. Outputs include graphics, datasets, and formal statistical tests.