MLS: MLS

CRAN

ALSM: Companion to Applied Linear Statistical Models

R: MLS
MLSR Documentation
MLS

mL: mL

GITHUB

t-arae/prtclmisc:

R: mL
mLR Documentation
mL

ML: ML model

CRAN

targeted: Targeted Inference

R: ML model
const macros = { "\\R": "\\textsf{R}", "\\code": "\\texttt"};
function processMathHTML() {

quit: Terminate an R Session

GITHUB

robertzk/monadicbase: The R Base Package

R: Terminate an R Session
quitR Documentation
Terminate an R Session

mls: MLS league results 1996-2016

GITHUB

jalapic/engsoccerdata: English and European Soccer Results 1871-2020

R: MLS league results 1996-2016
mlsR Documentation
MLS league results 1996-2016

ML: Functions for ML estimation of regression parameters for...

CRAN

sensmediation: Parametric Estimation and Sensitivity Analysis of Direct and Indirect Effects

R: Functions for ML estimation of regression parameters for...
const macros = { "\\R": "\\textsf{R}", "\\code

Mls: ML Estimates of Mean and Covariance Based on Incomplete Data

CRAN

MissMech: Testing Homoscedasticity, Multivariate Normality, and Missing Completely at Random

R: ML Estimates of Mean and Covariance Based on Incomplete Data
const macros = { "\\R": "\\textsf{R}", "\\code

quit: fun_name

GITHUB

granatb/RapeR: Rapuje kiedy nie chcesz

R: fun_name
quitR Documentation
fun_name

ml-params: Spark ML - ML Params

CRAN

sparklyr: R Interface to Apache Spark

R: Spark ML - ML Params
const macros = { "\\R": "\\textsf{R}", "\\code": "\\texttt"};
function processMathHTML

ee2.ml: ee2.ml

GITHUB

shuyang1987/IntegrativeHTE: Integrative analysis of the heterogeneous treatment effect (HTE)

R: ee2.ml
ee2.mlR Documentation
ee2.ml

ee1.ml: ee1.ml

GITHUB

shuyang1987/IntegrativeHTE: Integrative analysis of the heterogeneous treatment effect (HTE)

R: ee1.ml
ee1.mlR Documentation
ee1.ml

ml-persistence: Spark ML - Model Persistence

CRAN

sparklyr: R Interface to Apache Spark

R: Spark ML - Model Persistence
const macros = { "\\R": "\\textsf{R}", "\\code": "\\texttt"};
function

mls: MIDAS lag structure

CRAN

midasr: Mixed Data Sampling Regression

MIDAS lags
Usage
mls(x, k, m, ...)

ml-tuning: Spark ML - Tuning

CRAN

sparklyr: R Interface to Apache Spark

R: Spark ML - Tuning
const macros = { "\\R": "\\textsf{R}", "\\code": "\\texttt"};
function processMathHTML

ml: Calculates Maximum Likelihood

GITHUB

bgupsych/bgupsych: Useful commands for BGU Psychology students

This function returns the likelyhood of the data,
given a binomal probability.
Usage

mls: Miles for Cassie

GITHUB

likanzhan/acqr: Functions Helping You understand Statistics Easily

R: Miles for Cassie
mlsR Documentation
Miles for Cassie

ml: Shorthand connection constructor for embedded MonetDB

CRAN

MonetDBLite: In-Process Version of 'MonetDB'

for embedded MonetDB
Description
ml(...) provides a short way of connecting to an embedded MonetDB database. It is equivalent

ml: Shorthand connection constructor for embedded MonetDB

GITHUB

hannesmuehleisen/MonetDBLite-R: In-Process Version of 'MonetDB'

for embedded MonetDB
Description
ml(...) provides a short way of connecting to an embedded MonetDB database. It is equivalent

mls: Modified Least Squares

GITHUB

xinxuyale/HDCI: High Dimensional Confidence Interval Based on Lasso and Bootstrap

estimate.
Usage
mls(x, y, tau = 0, standardize = TRUE, intercept = TRUE)