C++ implementation of Maximum Entropy (Maxent) species distribution modeling with R bindings via Rcpp'. Provides a high-performance reimplementation of the Maxent algorithm for modeling species geographic distributions from occurrence data and environmental variables, following Phillips et al. (2006) <doi:10.1016/j.ecolmodel.2005.03.026>. Supports linear, quadratic, product, hinge, and threshold feature transformations, spatial projection in raw, logistic, and cloglog scales, and model diagnostics including Area Under the ROC Curve (AUC), variable importance, response curves, and Multivariate Environmental Similarity Surfaces (MESS) maps.

There are two functions-meta2d and meta3d for detecting rhythmic signals from time-series datasets. For analyzing time-series datasets without individual information, meta2d is suggested, which could incorporates multiple methods from ARSER, JTK_CYCLE and Lomb-Scargle in the detection of interested rhythms. For analyzing time-series datasets with individual information, meta3d is suggested, which takes use of any one of these three methods to analyze time-series data individual by individual and gives out integrated values based on analysis result of each individual.

This package provides official spatial boundary datasets for Malawi at multiple administrative levels: country (level 0), regions (level 1), districts (level 2), and traditional authorities (level 3). Also includes Lake Malawi boundary data. Boundary data are the Common Operational Datasets (COD-AB) sourced from the National Statistics Office of Malawi and distributed via the OCHA Humanitarian Data Exchange (HDX), version 02 <https://data.humdata.org/dataset/cod-ab-mwi>. Intended for use with the mwmap package or any spatial analysis workflow requiring Malawi administrative boundaries.

We introduce factor models designed to jointly analyze high-dimensional count data from multiple studies by extracting study-shared and specified factors. Our factor models account for heterogeneous noises and overdispersion among counts with augmented covariates. We propose an efficient and speedy variational estimation procedure for estimating model parameters, along with a novel criterion for selecting the optimal number of factors and the rank of regression coefficient matrix. More details can be referred to Liu et al. (2024) <doi:10.48550/arXiv.2402.15071>.

In biomedical studies, researchers are often interested in assessing the association between one or more ordinal explanatory variables and an outcome variable, at the same time adjusting for covariates of any type. The outcome variable may be continuous, binary, or represent censored survival times. In the absence of a precise knowledge of the response function, using monotonicity constraints on the ordinal variables improves efficiency in estimating parameters, especially when sample sizes are small. This package implements an active set algorithm that efficiently computes such estimators.

Offers tools to estimate and visualize levels of major pollutants (CO, NO2, SO2, Ozone, PM2.5 and PM10) across the conterminous United States for user-defined time ranges. Provides functions to retrieve pollutant data from the U.S. Environmental Protection Agencyâ s Air Quality System (AQS) API service <https://aqs.epa.gov/aqsweb/documents/data_api.html> for interactive visualization through a shiny application, allowing users to explore pollutant levels for a given location over time relative to the National Ambient Air Quality Standards (NAAQS).

Visualizes panel data. It has four main functionalities: (1) it plots the treatment status and missing values in a panel dataset; (2) it visualizes the temporal dynamics of a main variable of interest; (3) it depicts the bivariate relationships between a treatment variable and an outcome variable either by unit or in aggregate; (4) it displays the network structure of multi-way fixed effects as a k-partite graph, identifying connected components, singletons, and duplicate observations. For details, see <doi:10.18637/jss.v107.i07>.

This package provides tools for reading and writing biological sequences in multiple formats, including FASTA', PHYLIP', CLUSTAL', STOCKHOLM', MEGA and GenBank'. Supports interleaved and sequential layouts where applicable, and reads or writes gzip, bzip2 and xz compressed files transparently. Converts between formats, renames sequences via a two-column mapping, summarizes sequence counts, lengths, GC content and ambiguous characters, and manipulates sequence sets (e.g., filtering by patterns and computing consensus sequences from alignments). Also includes functions to download nucleotide records from NCBI by accession.

Given bincount data from single-cell copy number profiling (segmented or unsegmented), estimates ploidy, and uses the ploidy estimate to scale the data to absolute copy numbers. Uses the modular quantogram proposed by Kendall (1986) <doi:10.1002/0471667196.ess2129.pub2>, modified by weighting segments according to confidence, and quantifying confidence in the estimate using a theoretical quantogram. Includes optional fused-lasso segmentation with the algorithm in Johnson (2013) <doi:10.1080/10618600.2012.681238>, using the implementation from glmgen by Arnold, Sadhanala, and Tibshirani.

Transforms long data into a matrix form to allow for ease of input into modelling packages for regression, principal components, imputation or machine learning. It does this by pivoting on user defined columns, generating a key-value table for variable names to ensure one-to-one mappings are preserved. It is particularly useful when the indicator names in the columns are long descriptive strings, for example "Energy imports, net (% of energy use)". High level analysis wrapper functions for correlation and principal components analysis are provided.

Standard and extensible Eddy-Covariance data post-processing (Wutzler et al. (2018) <doi:10.5194/bg-15-5015-2018>) includes uStar-filtering, gap-filling, and flux-partitioning. The Eddy-Covariance (EC) micrometeorological technique quantifies continuous exchange fluxes of gases, energy, and momentum between an ecosystem and the atmosphere. It is important for understanding ecosystem dynamics and upscaling exchange fluxes. (Aubinet et al. (2012) <doi:10.1007/978-94-007-2351-1>). This package inputs pre-processed (half-)hourly data and supports further processing. First, a quality-check and filtering is performed based on the relationship between measured flux and friction velocity (uStar) to discard biased data (Papale et al. (2006) <doi:10.5194/bg-3-571-2006>). Second, gaps in the data are filled based on information from environmental conditions (Reichstein et al. (2005) <doi:10.1111/j.1365-2486.2005.001002.x>). Third, the net flux of carbon dioxide is partitioned into its gross fluxes in and out of the ecosystem by night-time based and day-time based approaches (Lasslop et al. (2010) <doi:10.1111/j.1365-2486.2009.02041.x>).

The package provides functions to create and use transcript-centric annotation databases/packages. The annotation for the databases are directly fetched from Ensembl using their Perl API. The functionality and data is similar to that of the TxDb packages from the GenomicFeatures package, but, in addition to retrieve all gene/transcript models and annotations from the database, the ensembldb package also provides a filter framework allowing to retrieve annotations for specific entries like genes encoded on a chromosome region or transcript models of lincRNA genes.

This package provides statistical tests for label-free LC-MS/MS data by spectral counts, to discover differentially expressed proteins between two biological conditions. Three tests are available: Poisson GLM regression, quasi-likelihood GLM regression, and the negative binomial of the edgeR package. The three models admit blocking factors to control for nuisance variables. To assure a good level of reproducibility a post-test filter is available, where we may set the minimum effect size considered biologicaly relevant, and the minimum expression of the most abundant condition.

This package provides a few functions aim to provide a statistic tool for three purposes. First, simulate kin pairs data based on the assumption that every trait is affected by genetic effects (A), common environmental effects (C) and unique environmental effects (E).Second, use kin pairs data to fit an ACE model and get model fit output.Third, calculate power of A estimate given a specific condition. For the mechanisms of power calculation, we suggest to check Visscher(2004)<doi:10.1375/twin.7.5.505>.

Quantifies projected climatic change relative to the climatic niche represented by a species current distribution. A weighted current reference defines the niche centre and empirical radial boundary. Present and projected conditions at each location give local climatic displacement, signed change in niche distance, a derived non-radial reconfiguration term, and exceedance beyond the niche boundary. Occurrence records, range maps, and binary or continuous species distribution model outputs can define reference weights. Matrix and spatial workflows return location-level values, weighted summaries, maps and climatic-variable contributions.

This package contains several functions for equivalence testing and practical significance testing. First, the tsti() command provides an automatic computation of three-sided testing results for a given estimate, standard error, and region of practical equivalence. For details, see Goeman, Solari, & Stijnen (2010) <doi:10.1002/sim.4002> and Isager & Fitzgerald (2024) <doi:10.31234/osf.io/8y925>. Second, the lddtest() command performs logarithmic density discontinuity equivalence testing for regression discontinuity designs. For reference, see Fitzgerald (2025) <doi:10.31222/osf.io/2dgrp_v1>.

This package implements goodness-of-fit test statistics and graphical methods for symmetric and asymmetric location-scale distributions under progressive Type-II censoring using the modified Lorenz curve and ratio modified sample Lorenz curve, as proposed by Lee (2024) <doi:10.3390/sym16020202>. Also provides order statistics distance test statistics based on Pakyari and Balakrishnan (2013) <doi:10.1080/00949655.2011.625424>. Supports calculation of test statistics, Monte Carlo p-values, critical values, and L-plot visual diagnostics for complete and progressively Type-II censored data.

Extracts reproducible geometric and urban-morphology descriptors from polygon and multipart polygon building footprints stored as sf objects. Methods include area, perimeter, compactness, shape, bounding-box, topology, convexity, and orientation measures, together with optional building-height, floor-count, gross-floor-area, floor-space-index, coverage, volume, and floor-height metrics derived from explicitly named attribute columns. The resulting sf objects preserve the source attributes and geometry for spatial analysis, visualization, and downstream statistical or machine-learning workflows, with optional future-based parallel processing for larger datasets.

Render a pairwise, asymmetric smoothed-association matrix of continuous variables. Each cell shows the fitted spline from an mgcv generalised additive model, with the upper triangle displaying gam(x_j ~ s(x_i)) and the lower triangle gam(x_i ~ s(x_j))'. Unlike Pearson's correlation matrix, the visualisation is intentionally asymmetric, revealing heteroscedasticity, leverage, and directional non-linearity that a single scalar correlation hides. An asymmetry index and a 24-category shape taxonomy quantify the directional difference and qualitative form of each fitted smooth.

Knowledge space theory by Doignon and Falmagne (1999) <doi:10.1007/978-3-642-58625-5> is a set- and order-theoretical framework, which proposes mathematical formalisms to operationalize knowledge structures in a particular domain. The kstMatrix package provides basic functionalities to generate, handle, and manipulate knowledge structures and knowledge spaces. Opposed to the kst package, kstMatrix uses matrix representations for knowledge structures. Furthermore, kstMatrix contains several knowledge spaces obtained in the 1990s by the research group around Cornelia Dowling through querying experts.

Administers survey and experimental instruments to panels of language-model personas, with respondent-level randomization, benchmark comparison against human data, and conjoint estimation from recorded respondent-level profile assignments. Samples of language-model personas follow Argyle et al. (2023) <doi:10.1017/pan.2023.2>; the case for benchmarking them against human data is set out in Bisbee et al. (2024) <doi:10.1017/pan.2024.5>; the conjoint estimand is the average marginal component effect of Hainmueller et al. (2014) <doi:10.1093/pan/mpt024>.

This package implements three families of parsimonious hidden Markov models (HMMs) for matrix-variate longitudinal data using the Expectation-Conditional Maximization (ECM) algorithm. The package supports matrix-variate normal, t, and contaminated normal distributions as emission distributions. For each hidden state, parsimony is achieved through the eigen-decomposition of the covariance matrices associated with the emission distribution. This approach results in a comprehensive set of 98 parsimonious HMMs for each type of emission distribution. Atypical matrix detection is also supported, utilizing the fitted (heavy-tailed) models.

This package provides essential tools for the pre-processing techniques of matching and weighting multiply imputed datasets. The package includes functions for matching within and across multiply imputed datasets using various methods, estimating weights for units in the imputed datasets using multiple weighting methods, calculating causal effect estimates in each matched or weighted dataset using parametric or non-parametric statistical models, and pooling the resulting estimates according to Rubin's rules (please see <https://journal.r-project.org/archive/2021/RJ-2021-073/> for more details).

This package provides a unified, programmatic interface for searching, browsing, and retrieving metadata from various international organization data repositories that use the National Data Archive ('NADA') software, such as the World Bank, FAO', and the International Household Survey Network ('IHSN'). Functions allow users to discover available data collections, country codes, and access types, perform complex searches using keyword and spatial/temporal filters, and retrieve detailed study information, including file lists and variable-level data dictionaries. It simplifies access to microdata for researchers and policy analysts globally.