Two letter hyphenation through babel package

I am using bable package in USenglish'' settings.

I need two letter hyphenation should be avoided.

\lefthyphenmin=3, \righthyphenmin=3 command is working perfectly without the babel package. If the babel package is used this two commands are not working.

I will try to control through babel package hyphenation below commands

\providehyphenmins{\CurrentOption}{\m@ne\m@ne}
\providehyphenmins{english}{\thr@@}
\renewcommand\englishhyphenmins{3}


But this commands are not working.

Please advise how to avoid two letter hyphenation through the babel package.

MWE:

\documentclass[USenglish,twocolumn]{article}

\usepackage{babel}

\providehyphenmins{\CurrentOption}{\m@ne\m@ne}
%\providehyphenmins{english}{\thr@@}
%\renewcommand\englishhyphenmins{3}
\lefthyphenmin=3%
\righthyphenmin=3%

\begin{document}

Earlier research into pairwise comparisons produced a probabilistic model known as the Bradley-Terry model. This model has proven useful for calculating ratings based on observed comparisons which can be represented in a weighted, directed graph.  The ratings, and consequential rank of a team, developed for each node can then be used to determine which team is more likely to win. This model can also be extended to estimate the probability of winning games.

In 1982, Maher presented a Poisson regression model to estimate win probabilities based on a team's specific skills. Poisson scoring models assume goal scoring follows an independent Poisson distribution, and use past performance to obtain maximum likelihood {estimates} for the number of goals scored by both the home and away team. A benefit of these models is that they can account for additional explanatory variables that affect scoring such as home ice advantage. Another example of a Poisson regression model is the Dixon and Cole model which applies a time weighting model to discount the value of older information. Likewise,  looked at other explanatory variables by incorporating full strength and non-standard scoring into a Poisson model to predict the goal scoring in games played in the 2009 NHL season.

Other rating techniques have involved using Google's PageRank algorithm to model pairwise interactions {between} teams. The PageRank model uses Makov chains to generate ratings by solving the stationary vector of an irreducible stochastic matrix. When formulating the stochastic matrix, the primary inputs are interactions coded in an adjacency matrix and a vector of additive constants coded in a personalization vector. The model then uses a damping coefficient to adjust how much weight is placed on the adjacency matrix and personalization vector.

Park and Newman were two of the first people to {extend} the PageRank model to sports rankings and {defined} a total win score'' to account for a team's direct and indirect wins. This model assumes transitivity of wins to measure indirect wins and controls how much weight is placed on indirect wins by incorporating a damping coefficient.

In 2008, Govan et al. developed a PageRank model called the Generalized Markov (GeM) that models cumulative margins of victory between pairs of teams with weighted directed edges. For this particular model, Govan et al. used a uniform probability vector for the personalization vector and chose an arbitrary value for the damping coefficient. A year later, Govan et al. proposed an Offense-Defense'' model that rated teams based on a combination of distinct offensive and defensive ratings.

\end{document}


See the below two word hyphenation should be avoided.

You are using USenglish, so you should redefine its hyphenmins:

\documentclass[USenglish,twocolumn]{article}

\usepackage{babel}

\renewcommand\USenglishhyphenmins{33}

\begin{document}

Earlier research into pairwise comparisons produced a probabilistic model known as the Bradley-Terry model. This model has proven useful for calculating ratings based on observed comparisons which can be represented in a weighted, directed graph.  The ratings, and consequential rank of a team, developed for each node can then be used to determine which team is more likely to win. This model can also be extended to estimate the probability of winning games.

In 1982, Maher presented a Poisson regression model to estimate win probabilities based on a team's specific skills. Poisson scoring models assume goal scoring follows an independent Poisson distribution, and use past performance to obtain maximum likelihood {estimates} for the number of goals scored by both the home and away team. A benefit of these models is that they can account for additional explanatory variables that affect scoring such as home ice advantage. Another example of a Poisson regression model is the Dixon and Cole model which applies a time weighting model to discount the value of older information. Likewise,  looked at other explanatory variables by incorporating full strength and non-standard scoring into a Poisson model to predict the goal scoring in games played in the 2009 NHL season.

Other rating techniques have involved using Google's PageRank algorithm to model pairwise interactions {between} teams. The PageRank model uses Makov chains to generate ratings by solving the stationary vector of an irreducible stochastic matrix. When formulating the stochastic matrix, the primary inputs are interactions coded in an adjacency matrix and a vector of additive constants coded in a personalization vector. The model then uses a damping coefficient to adjust how much weight is placed on the adjacency matrix and personalization vector.

Park and Newman were two of the first people to {extend} the PageRank model to sports rankings and {defined} a total win score'' to account for a team's direct and indirect wins. This model assumes transitivity of wins to measure indirect wins and controls how much weight is placed on indirect wins by incorporating a damping coefficient.

In 2008, Govan et al. developed a PageRank model called the Generalized Markov (GeM) that models cumulative margins of victory between pairs of teams with weighted directed edges. For this particular model, Govan et al. used a uniform probability vector for the personalization vector and chose an arbitrary value for the damping coefficient. A year later, Govan et al. proposed an Offense-Defense'' model that rated teams based on a combination of distinct offensive and defensive ratings.

\end{document}


\providehyphenmins{USenglish}{33} would work too, but only before \usepackage{babel}.

• BTW: is USenglish actually a thing? I had a look inside USenglish.def it just loades english.ldf. Is the distinction just for other packages that might do different things? – daleif Nov 30 '17 at 15:01
• @daleif english.ldf does different things depending on the current value of \CurrentOption. E.g. languagename is different, \dateUSenglish is defined only if you actually call USenglish etc. – Ulrike Fischer Nov 30 '17 at 15:04
• Ahh, didn't know that, had not looked – daleif Nov 30 '17 at 15:27

Write \lefthyphenmin=3 and \righthyphenmin=3 in the document environment!

\documentclass[USenglish,twocolumn]{article}
\usepackage{babel}
\providehyphenmins{\CurrentOption}{\m@ne\m@ne}
%\providehyphenmins{english}{\thr@@}
%\renewcommand\englishhyphenmins{3}
\begin{document}
\lefthyphenmin=3
\righthyphenmin=3
Earlier research into pairwise comparisons produced a probabilistic model known as the Bradley-Terry model. This model has proven useful for calculating ratings based on observed comparisons which can be represented in a weighted, directed graph.  The ratings, and consequential rank of a team, developed for each node can then be used to determine which team is more likely to win. This model can also be extended to estimate the probability of winning games.

In 1982, Maher presented a Poisson regression model to estimate win probabilities based on a team's specific skills. Poisson scoring models assume goal scoring follows an independent Poisson distribution, and use past performance to obtain maximum likelihood {estimates} for the number of goals scored by both the home and away team. A benefit of these models is that they can account for additional explanatory variables that affect scoring such as home ice advantage. Another example of a Poisson regression model is the Dixon and Cole model which applies a time weighting model to discount the value of older information. Likewise,  looked at other explanatory variables by incorporating full strength and non-standard scoring into a Poisson model to predict the goal scoring in games played in the 2009 NHL season.

Other rating techniques have involved using Google's PageRank algorithm to model pairwise interactions {between} teams. The PageRank model uses Makov chains to generate ratings by solving the stationary vector of an irreducible stochastic matrix. When formulating the stochastic matrix, the primary inputs are interactions coded in an adjacency matrix and a vector of additive constants coded in a personalization vector. The model then uses a damping coefficient to adjust how much weight is placed on the adjacency matrix and personalization vector.

Park and Newman were two of the first people to {extend} the PageRank model to sports rankings and {defined} a total win score'' to account for a team's direct and indirect wins. This model assumes transitivity of wins to measure indirect wins and controls how much weight is placed on indirect wins by incorporating a damping coefficient.

In 2008, Govan et al. developed a PageRank model called the Generalized Markov (GeM) that models cumulative margins of victory between pairs of teams with weighted directed edges. For this particular model, Govan et al. used a uniform probability vector for the personalization vector and chose an arbitrary value for the damping coefficient. A year later, Govan et al. proposed an Offense-Defense'' model that rated teams based on a combination of distinct offensive and defensive ratings.
\end{document}

• also \englishhyphenmins expects its argument to be two digits, so \renewcommand\englishhyphenmins{33}` works fine (the Danish one use to be 12, which no-one liked) – daleif Nov 30 '17 at 14:51
• @Ulrike Fischer your solution is working good – Vetri Dec 2 '17 at 4:40