Forest Structure Extrapolation with R

Set of tools to streamline the modeling of the relationship between satellite imagery time series or any other environmental information, such as terrain elevation, with forest structural attributes derived from 3D point cloud data and their subsequent imputation over the broader landscape.

Lightweight Reproducible Reporting

Distributed reproducible computing framework, adopting ideas from git, docker and other software. By defining a lightweight interface around the inputs and outputs of an analysis, a lot of the repetitive work for reproducible research can be automated. We define a simple format for organising and describing work that facilitates collaborative reproducible research and acknowledges that all analyses are run multiple times over their lifespans.

Downloading, Reading and Analyzing PNS Microdata

Provides tools for downloading, reading and analyzing the National Survey of Health - PNS, a household survey from Brazilian Institute of Geography and Statistics - IBGE. The data must be downloaded from the official website < https://www.ibge.gov.br/>. Further analysis must be made using package 'survey'.

Downloading, Reading and Analyzing PNAD COVID19 Microdata

Provides tools for downloading, reading and analyzing the COVID19 National Household Sample Survey - PNAD COVID19, a household survey from Brazilian Institute of Geography and Statistics - IBGE. The data must be downloaded from the official website < https://www.ibge.gov.br/>. Further analysis must be made using package 'survey'.

Downloading, Reading and Analyzing PNDS Microdata - Package in Development

Provides tools for downloading, reading and analyzing the National Survey of Demographic and Health - PNDS, a household survey from Brazilian Institute of Geography and Statistics - IBGE. The data must be downloaded from the official website < https://www.ibge.gov.br/>. Further analysis must be made using package 'survey'.

ML Estimation for Multivariate Normal Data with Missing Values

Finds the Maximum Likelihood (ML) Estimate of the mean vector and variance-covariance matrix for multivariate normal data with missing values.

Automatic Fixed Rank Kriging

Automatic fixed rank kriging for (irregularly located) spatial data using a class of basis functions with multi-resolution features and ordered in terms of their resolutions. The model parameters are estimated by maximum likelihood (ML) and the number of basis functions is determined by Akaike's information criterion (AIC). For spatial data with either one realization or independent replicates, the ML estimates and AIC are efficiently computed using their closed-form expressions when no missing value occurs. Details regarding the basis function construction, parameter estimation, and AIC calculation can be found in Tzeng and Huang (2018) . For data with missing values, the ML estimates are obtained using the expectation- maximization algorithm. Apart from the number of basis functions, there are no other tuning parameters, making the method fully automatic. Users can also include a stationary structure in the spatial covariance, which utilizes 'LatticeKrig' package.

Data Sets from Mixed-Effects Models in S

Data sets and sample analyses from Pinheiro and Bates, "Mixed-effects Models in S and S-PLUS" (Springer, 2000).

Continuous Glucose Monitoring Data Analyzer

Contains all of the functions necessary for the complete analysis of a continuous glucose monitoring study and can be applied to data measured by various existing 'CGM' devices such as 'FreeStyle Libre', 'Glutalor', 'Dexcom' and 'Medtronic CGM'. It reads a series of data files, is able to convert various formats of time stamps, can deal with missing values, calculates both regular statistics and nonlinear statistics, and conducts group comparison. It also displays results in a concise format. Also contains two unique features new to 'CGM' analysis: one is the implementation of strictly standard mean difference and the class of effect size; the other is the development of a new type of plot called antenna plot. It corresponds to 'Zhang XD'(2018)'s article 'CGManalyzer: an R package for analyzing continuous glucose monitoring studies'.

Robust Clustering Procedures

A clustering algorithm similar to K-Means is implemented, it has two main advantages, namely (a) The estimator is resistant to outliers, that means that results of estimator are still correct when there are atypical values in the sample and (b) The estimator is efficient, roughly speaking, if there are no outliers in the sample, results will be similar to those obtained by a classic algorithm (K-Means). Clustering procedure is carried out by minimizing the overall robust scale so-called tau scale. (see Gonzalez, Yohai and Zamar (2019) ).