Finds adaptive strategies for sequential symmetric games using a genetic algorithm. Currently, any symmetric two by two matrix is allowed, and strategies can remember the history of an opponent's play from the previous three rounds of moves in iterated interactions between players. The genetic algorithm returns a list of adaptive strategies given payoffs, and the mean fitness of strategies in each generation.

Estimating heterogeneous treatment effects with tree-based machine learning algorithms and visualizing estimated results in flexible and presentation-ready ways. For more information, see Brand, Xu, Koch, and Geraldo (2021) <doi:10.1177/0081175021993503>. Our current package first started as a fork of the causalTree package on GitHub and we greatly appreciate the authors for their extremely useful and free package.

An implementation of the induced smoothing (IS) idea to lasso regularization models to allow estimation and inference on the model coefficients (currently hypothesis testing only). Linear, logistic, Poisson and gamma regressions with several link functions are implemented. The algorithm is described in the original paper; see <doi:10.1177/0962280219842890> and discussed in a tutorial <doi:10.13140/RG.2.2.16360.11521>.

Given an image of a formula (typeset or handwritten) this package provides calls to the Mathpix service to produce the LaTeX code which should generate that image, and pastes it into a (e.g. an rmarkdown') document. See <https://docs.mathpix.com/> for full details. Mathpix is an external service and use of the API is subject to their terms and conditions.

National Statistical Office of Mongolia (NSO) is the national statistical service and an organization of Mongolian government. NSO provides open access to official data via its API <http://opendata.1212.mn/en/doc>. The package NSO1212 has functions for accessing the API service. The functions are compatible with the API v2.0 and get data sets and its detailed informations from the API.

This package implements a procedure for forecasting time series data based on an additive model where non-linear trends are fit with yearly, weekly, and daily seasonality, plus holiday effects. It works best with time series that have strong seasonal effects and several seasons of historical data. Prophet is robust to missing data and shifts in the trend, and typically handles outliers well.

This package implements optimization techniques for Lasso regression, R.Tibshirani(1996)<doi:10.1111/j.2517-6161.1996.tb02080.x> using Fast Iterative Shrinkage-Thresholding Algorithm (FISTA) and Iterative Shrinkage-Thresholding Algorithm (ISTA) based on proximal operators, A.Beck(2009)<doi:10.1137/080716542>. The package is useful for high-dimensional regression problems and includes cross-validation procedures to select optimal penalty parameters.

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.

Variable and interaction selection are essential to classification in high-dimensional setting. In this package, we provide the implementation of SODA procedure, which is a forward-backward algorithm that selects both main and interaction effects under logistic regression and quadratic discriminant analysis. We also provide an extension, S-SODA, for dealing with the variable selection problem for semi-parametric models with continuous responses.

This package provides an abstraction for managing, installing, and switching between sets of installed R packages. This allows users to maintain multiple package libraries simultaneously, e.g. to maintain strict, package-version-specific reproducibility of many analyses, or work within a development/production release paradigm. Introduces a generalized package installation process which supports multiple repository and non-repository sources and tracks package provenance.

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.).

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.

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).

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>.

This package provides functions to identify Homozygous-by-Descent (HBD) segments associated with runs of homozygosity (ROH) and to estimate individual autozygosity (or inbreeding coefficient). HBD segments and autozygosity are assigned to multiple HBD classes with a model-based approach relying on a mixture of exponential distributions. The rate of the exponential distribution is distinct for each HBD class and defines the expected length of the HBD segments. These HBD classes are therefore related to the age of the segments (longer segments and smaller rates for recent autozygosity / recent common ancestor). The functions allow to estimate the parameters of the model (rates of the exponential distributions, mixing proportions), to estimate global and local autozygosity probabilities and to identify HBD segments with the Viterbi decoding. The method is fully described in Druet and Gautier (2017) <doi:10.1111/mec.14324>.

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>.