Support for reading/writing simple feature ('sf') spatial objects from/to Parquet files. Parquet files are an open-source, column-oriented data storage format from Apache (<https://parquet.apache.org/>), now popular across programming languages. This implementation converts simple feature list geometries into well-known binary format for use by arrow', and coordinate reference system information is maintained in a standard metadata format.

Implementation of a shiny app to easily compare supervised machine learning model performances. You provide the data and configure each model parameter directly on the shiny app. Different supervised learning algorithms can be tested either on Spark or H2O frameworks to suit your regression and classification tasks. Implementation of available machine learning models on R has been done by Lantz (2013, ISBN:9781782162148).

Analysis of single-cell RNA sequencing data can be simple and clear with the right utility functions. This package collects such functions, aiming to fulfill the following criteria: code clarity over performance (i.e. plain R code instead of C code), most important analysis steps over completeness (analysis by hand', not automated integration etc.), emphasis on quantitative visualization (intensity-coded color scale, etc.).

This package provides Markov Chain Monte Carlo (MCMC) routine for the structural equation modelling described in Maity et. al. (2020) <doi:10.1093/bioinformatics/btaa286>. This MCMC sampler is useful when one attempts to perform an integrative survival analysis for multiple platforms of the Omics data where the response is time to event and the predictors are different omics expressions for different platforms.

Evaluating probabilistic forecasts via proper scoring rules. scoring implements the beta, power, and pseudospherical families of proper scoring rules, along with ordered versions of the latter two families. Included among these families are popular rules like the Brier (quadratic) score, logarithmic score, and spherical score. For two-alternative forecasts, also includes functionality for plotting scores that one would obtain under specific scoring rules.

Download data (tables and datasets) from the Swiss National Bank (SNB; <https://www.snb.ch/en>), the Swiss central bank. The package is lightweight and comes with few dependencies; suggested packages are used only if data is to be transformed into particular data structures, for instance into zoo objects. Downloaded data can optionally be cached, to avoid repeated downloads of the same files.

Algorithms for accelerating the convergence of slow, monotone sequences from smooth, contraction mapping such as the EM and MM algorithms. It can be used to accelerate any smooth, linearly convergent acceleration scheme. A tutorial style introduction to this package is available in a vignette on the CRAN download page or, when the package is loaded in an R session, with vignette("turboEM").

This package provides a set of tools to build tensorflow/keras3-based models in R from amino acid and nucleotide sequences focusing on adaptive immune receptors. The package includes pre-processing of sequences, unifying gene nomenclature usage, encoding sequences, and combining models. This package will serve as the basis of future immune receptor sequence functions/packages/models compatible with the scRepertoire ecosystem.

This package provides a GUI interface for the DAPAR package. The package Prostar (Proteomics statistical analysis with R) is a Bioconductor distributed R package which provides all the necessary functions to analyze quantitative data from label-free proteomics experiments. Contrarily to most other similar R packages, it is endowed with rich and user-friendly graphical interfaces, so that no programming skill is required.

This package performs a gene expression data analysis to detect condition-specific genes. Such genes are significantly up- or down-regulated in a small number of conditions. It does so by fitting a mixture of normal distributions to the expression values. Conditions can be environmental conditions, different tissues, organs or any other sources that you wish to compare in terms of gene expression.

This package lets you replace the standard x-axis in ggplots with a combination matrix to visualize complex set overlaps. UpSet has introduced a new way to visualize the overlap of sets as an alternative to Venn diagrams. This package provides a simple way to produce such plots using ggplot2. In addition it can convert any categorical axis into a combination matrix axis.

The renewal Hawkes (RHawkes) process (Wheatley, Filimonov, and Sornette, 2016 <doi:10.1016/j.csda.2015.08.007>) is an extension to the classical Hawkes self-exciting point process widely used in the modelling of clustered event sequence data. This package provides functions to simulate the RHawkes process with a given immigrant hazard rate function and offspring birth time density function, to compute the exact likelihood of a RHawkes process using the recursive algorithm proposed by Chen and Stindl (2018) <doi:10.1080/10618600.2017.1341324>, to compute the Rosenblatt residuals for goodness-of-fit assessment, and to predict future event times based on observed event times up to a given time. A function implementing the linear time RHawkes process likelihood approximation algorithm proposed in Stindl and Chen (2021) <doi:10.1007/s11222-021-10002-0> is also included.

Visualizing crystal structures and selected area electron diffraction (SAED) patterns. It provides functions cry_demo() and dp_demo() to load a file in CIF (Crystallographic Information Framework) formats and display crystal structures and electron diffraction patterns. The function dp_demo() also performs simple simulation of powder X-ray diffraction (PXRD) patterns, and the results can be saved to a file in the working directory. The package has been tested on several platforms, including Linux on Crostini with a Coreâ ¢ m3-8100Y Chromebook, I found that even on this low-powered platform, the performance was acceptable. T. Hanashima (2001) <https://www2.kek.jp/imss/pf/tools/sasaki/sinram/sinram.html> Todd Helmenstine (2019) <https://sciencenotes.org/molecule-atom-colors-cpk-colors/> Wikipedia contributors (2023) <https://en.wikipedia.org/w/index.php?title=Atomic_radius&oldid=1179864711>.

Read, manipulate and write voxel spaces. Voxel spaces are read from text-based output files of the AMAPVox software. AMAPVox is a LiDAR point cloud voxelisation software that aims at estimating leaf area through several theoretical/numerical approaches. See more in the article Vincent et al. (2017) <doi:10.23708/1AJNMP> and the technical note Vincent et al. (2021) <doi:10.23708/1AJNMP>.

Generate urls and hyperlinks to commonly used biological databases and resources based on standard identifiers. This is primarily useful when writing dynamic reports that reference things like gene symbols in text or tables, allowing you to, for example, convert gene identifiers to hyperlinks pointing to their entry in the NCBI Gene database. Currently supports NCBI Gene, PubMed', Gene Ontology, KEGG', CRAN and Bioconductor.

Estimation of association between disease or death counts (e.g. COVID-19) and socio-environmental risk factors using a zero-inflated Bayesian spatiotemporal model. Non-spatiotemporal models and/or models without zero-inflation are also included for comparison. Functions to produce corresponding maps are also included. See Chakraborty et al. (2022) <doi:10.1007/s13253-022-00487-1> for more details on the method.

Simple functions for plotting linear calibration functions and estimating standard errors for measurements according to the Handbook of Chemometrics and Qualimetrics: Part A by Massart et al. (1997) There are also functions estimating the limit of detection (LOD) and limit of quantification (LOQ). The functions work on model objects from - optionally weighted - linear regression (lm) or robust linear regression ('rlm from the MASS package).

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.

Dynamic and Interactive Maps with R, powered by leaflet <https://leafletjs.com>. evolMap generates a web page with interactive and dynamic maps to which you can add geometric entities (points, lines or colored geographic areas), and/or markers with optional links between them. The dynamic ability of these maps allows their components to evolve over a continuous period of time or by periods.

For ordinal rating data, consider the accelerated EM algorithm to estimate and test models within the family of CUB models (where CUB stands for Combination of a discrete Uniform and a shifted Binomial distributions). The procedure is built upon Louis identity for the observed information matrix. Best-subset variable selection is then implemented since it becomes more feasible from the computational point of view.

This package provides tools for fitting sparse generalised linear mixed models with l0 regularisation. Selects fixed and random effects under the hierarchy constraint that fixed effects must precede random effects. Uses coordinate descent and local search algorithms to rapidly deliver near-optimal estimates. Gaussian and binomial response families are currently supported. For more details see Stroup, Ptukhina, and Garai (2024) <doi:10.1201/9780429092060>.

Fits sparse interaction models for continuous and binary responses subject to the strong (or weak) hierarchy restriction that an interaction between two variables only be included if both (or at least one of) the variables is included as a main effect. For more details, see Bien, J., Taylor, J., Tibshirani, R., (2013) "A Lasso for Hierarchical Interactions." Annals of Statistics. 41(3). 1111-1141.

Comprehensive network analysis package. Calculate correlation network fastly, accelerate lots of analysis by parallel computing. Support for multi-omics data, search sub-nets fluently. Handle bigger data, more than 10,000 nodes in each omics. Offer various layout method for multi-omics network and some interfaces to other software ('Gephi', Cytoscape', ggplot2'), easy to visualize. Provide comprehensive topology indexes calculation, including ecological network stability.

Extends the mlr3 ecosystem to functional analysis by adding support for irregular and regular functional data as defined in the tf package. The package provides PipeOps for preprocessing functional columns and for extracting scalar features, thereby allowing standard machine learning algorithms to be applied afterwards. Available operations include simple functional features such as the mean or maximum, smoothing, interpolation, flattening, and functional PCA'.